Datasets:
vgainullin
/
xciting_data

Dataset card Data Studio Files
xet
 Community
1
paragraph_index
int64
sec
string
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1
INTRODUCTION
1
3
[ "b3", "b1", "b3", "b2", "b3" ]
16,966,337
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(3), there are 22 distinct subcellular localization categories in budding yeast.
[ "3", "1", "3", "2", "3" ]
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1
false
, there are 22 distinct subcellular localization categories in budding yeast.
[ "3" ]
, there are 22 distinct subcellular localization categories in budding yeast.
false
false
true
true
false
537
1
INTRODUCTION
1
3
[ "b3", "b1", "b3", "b2", "b3" ]
16,966,337
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It means that the possibility of correct prediction of one localization is <4.55% with a random guess.
[ "3", "1", "3", "2", "3" ]
102
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false
It means that the possibility of correct prediction of one localization is <4.55% with a random guess.
[]
It means that the possibility of correct prediction of one localization is <4.55% with a random guess.
true
true
true
true
true
537
1
INTRODUCTION
1
1
[ "b3", "b1", "b3", "b2", "b3" ]
16,966,337
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Second, the prediction task is a ‘multi-label’ classification problem; some proteins may have several different subcellular localizations (1).
[ "3", "1", "3", "2", "3" ]
142
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Second, the prediction task is a ‘multi-label’ classification problem; some proteins may have several different subcellular localizations.
[ "1" ]
Second, the prediction task is a ‘multi-label’ classification problem; some proteins may have several different subcellular localizations.
true
true
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537
1
INTRODUCTION
1
3
[ "b3", "b1", "b3", "b2", "b3" ]
16,966,337
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For instance, the YBR156C can be located either in ‘microtubule’ or ‘nucleus’ according to the work of Huh et al.
[ "3", "1", "3", "2", "3" ]
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For instance, the YBR156C can be located either in ‘microtubule’ or ‘nucleus’ according to the work of Huh et al.
[]
For instance, the YBR156C can be located either in ‘microtubule’ or ‘nucleus’ according to the work of Huh et al.
true
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537
1
INTRODUCTION
1
3
[ "b3", "b1", "b3", "b2", "b3" ]
16,966,337
pmid-14562095|pmid-15513989|pmid-14562095|pmid-10195282|pmid-14562095
Thus, a computational method should be able to handle the multi-label problem.
[ "3", "1", "3", "2", "3" ]
78
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Thus, a computational method should be able to handle the multi-label problem.
[]
Thus, a computational method should be able to handle the multi-label problem.
true
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537
1
INTRODUCTION
1
3
[ "b3", "b1", "b3", "b2", "b3" ]
16,966,337
pmid-14562095|pmid-15513989|pmid-14562095|pmid-10195282|pmid-14562095
Finally, the number of proteins in each localization is too different making a protein localization data set highly ‘imbalanced’.
[ "3", "1", "3", "2", "3" ]
129
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false
Finally, the number of proteins in each localization is too different making a protein localization data set highly ‘imbalanced’.
[]
Finally, the number of proteins in each localization is too different making a protein localization data set highly ‘imbalanced’.
true
true
true
true
true
537
1
INTRODUCTION
1
2
[ "b3", "b1", "b3", "b2", "b3" ]
16,966,337
pmid-14562095|pmid-15513989|pmid-14562095|pmid-10195282|pmid-14562095
It is generally accepted that proteins located in some organelles are much more abundant than in others (2).
[ "3", "1", "3", "2", "3" ]
108
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1
false
It is generally accepted that proteins located in some organelles are much more abundant than in others.
[ "2" ]
It is generally accepted that proteins located in some organelles are much more abundant than in others.
true
true
true
true
true
537
1
INTRODUCTION
1
3
[ "b3", "b1", "b3", "b2", "b3" ]
16,966,337
pmid-14562095|pmid-15513989|pmid-14562095|pmid-10195282|pmid-14562095
It also can be checked with the data of Huh et al.
[ "3", "1", "3", "2", "3" ]
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It also can be checked with the data of Huh et al.
[]
It also can be checked with the data of Huh et al.
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537
1
INTRODUCTION
1
3
[ "b3", "b1", "b3", "b2", "b3" ]
16,966,337
pmid-14562095|pmid-15513989|pmid-14562095|pmid-10195282|pmid-14562095
(3); the number of proteins in ‘cytoplasm’ is 1782, while the number of proteins in ‘ER to Golgi’ is only 6 (see the second column of Table 1).
[ "3", "1", "3", "2", "3" ]
143
3,208
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; the number of proteins in ‘cytoplasm’ is 1782, while the number of proteins in ‘ER to Golgi’ is only 6.
[ "3", "see the second column of Table 1" ]
; the number of proteins in ‘cytoplasm’ is 1782, while the number of proteins in ‘ER to Golgi’ is only 6.
false
false
true
true
false
537
1
INTRODUCTION
1
3
[ "b3", "b1", "b3", "b2", "b3" ]
16,966,337
pmid-14562095|pmid-15513989|pmid-14562095|pmid-10195282|pmid-14562095
All these three characteristics make the task difficult.
[ "3", "1", "3", "2", "3" ]
56
3,209
0
false
All these three characteristics make the task difficult.
[]
All these three characteristics make the task difficult.
true
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true
true
true
537
1
INTRODUCTION
1
3
[ "b3", "b1", "b3", "b2", "b3" ]
16,966,337
pmid-14562095|pmid-15513989|pmid-14562095|pmid-10195282|pmid-14562095
Thus, not only good features for a protein but also a good computational algorithm is ultimately needed for the reliable prediction of protein subcellular localization.
[ "3", "1", "3", "2", "3" ]
168
3,210
0
false
Thus, not only good features for a protein but also a good computational algorithm is ultimately needed for the reliable prediction of protein subcellular localization.
[]
Thus, not only good features for a protein but also a good computational algorithm is ultimately needed for the reliable prediction of protein subcellular localization.
true
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true
537
2
INTRODUCTION
0
null
null
16,966,337
null
Actually, many works have been done during the last decade or so in this field.
null
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false
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Actually, many works have been done during the last decade or so in this field.
true
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true
true
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538
2
INTRODUCTION
0
null
null
16,966,337
null
The efforts in these works have followed several trends (see Table 2).
null
70
3,212
0
false
null
null
The efforts in these works have followed several trends (see Table 2).
true
true
true
true
true
538
3
INTRODUCTION
1
1
[ "b1", "b2", "b4", "b14", "b15", "b22", "b4", "b5", "b6", "b18", "b19", "b1", "b9", "b12", "b23", "b24", "b26", "b23", "b24", "b28", "b9", "b25", "b1", "b11", "b29", "b26", "b30", "b1", "b11" ]
16,966,337
pmid-15513989|pmid-10195282|pmid-8145256|pmid-14635197|NA|pmid-12471598|pmid-8145256|pmid-11288174|pmid-11097861|pmid-12967962|pmid-13678304|pmid-15513989|pmid-12719255|pmid-12824378|pmid-10087920|pmid-10966805|pmid-12169534|pmid-10087920|pmid-10966805|pmid-11125049|pmid-12719255|pmid-12186861|pmid-15513989|pmid-146233...
1) Feature Extraction: one trend is to try to extract good information (or features) from given proteins.
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1) Feature Extraction: one trend is to try to extract good information (or features) from given proteins.
[]
1) Feature Extraction: one trend is to try to extract good information (or features) from given proteins.
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539
3
INTRODUCTION
1
1
[ "b1", "b2", "b4", "b14", "b15", "b22", "b4", "b5", "b6", "b18", "b19", "b1", "b9", "b12", "b23", "b24", "b26", "b23", "b24", "b28", "b9", "b25", "b1", "b11", "b29", "b26", "b30", "b1", "b11" ]
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One category of the features used is based on amino acid composition (AA) (1,2,4–14,15–22).
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One category of the features used is based on amino acid composition (AA).
[ "1,2,4–14,15–22" ]
One category of the features used is based on amino acid composition (AA).
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INTRODUCTION
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1
[ "b1", "b2", "b4", "b14", "b15", "b22", "b4", "b5", "b6", "b18", "b19", "b1", "b9", "b12", "b23", "b24", "b26", "b23", "b24", "b28", "b9", "b25", "b1", "b11", "b29", "b26", "b30", "b1", "b11" ]
16,966,337
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Many works have used AA as the unique feature or the complementary feature of a protein owing to its simplicity and its high coverage.
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Many works have used AA as the unique feature or the complementary feature of a protein owing to its simplicity and its high coverage.
[]
Many works have used AA as the unique feature or the complementary feature of a protein owing to its simplicity and its high coverage.
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true
true
true
true
539
3
INTRODUCTION
1
1
[ "b1", "b2", "b4", "b14", "b15", "b22", "b4", "b5", "b6", "b18", "b19", "b1", "b9", "b12", "b23", "b24", "b26", "b23", "b24", "b28", "b9", "b25", "b1", "b11", "b29", "b26", "b30", "b1", "b11" ]
16,966,337
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Prediction based on only AA features would lose sequence order information.
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Prediction based on only AA features would lose sequence order information.
[]
Prediction based on only AA features would lose sequence order information.
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539
3
INTRODUCTION
1
4
[ "b1", "b2", "b4", "b14", "b15", "b22", "b4", "b5", "b6", "b18", "b19", "b1", "b9", "b12", "b23", "b24", "b26", "b23", "b24", "b28", "b9", "b25", "b1", "b11", "b29", "b26", "b30", "b1", "b11" ]
16,966,337
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Thus, to give sequential information to the AA, Nakashima and Nishikawa (4) also used amino acid pair composition (PairAA), Chou (5) used pseudo amino acid composition (PseAA) using sequence-order correlation (SOC) factor (6), and Park and Kanehisa (18) also used gapped amino acid composition (GapAA).
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Thus, to give sequential information to the AA, Nakashima and Nishikawa also used amino acid pair composition (PairAA), Chou used pseudo amino acid composition (PseAA) using sequence-order correlation (SOC) factor, and Park and Kanehisa also used gapped amino acid composition (GapAA).
[ "4", "5", "6", "18" ]
Thus, to give sequential information to the AA, Nakashima and Nishikawa also used amino acid pair composition (PairAA), Chou used pseudo amino acid composition (PseAA) using sequence-order correlation (SOC) factor, and Park and Kanehisa also used gapped amino acid composition (GapAA).
true
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INTRODUCTION
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1
[ "b1", "b2", "b4", "b14", "b15", "b22", "b4", "b5", "b6", "b18", "b19", "b1", "b9", "b12", "b23", "b24", "b26", "b23", "b24", "b28", "b9", "b25", "b1", "b11", "b29", "b26", "b30", "b1", "b11" ]
16,966,337
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For more sequence order effect, Pan et al.
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For more sequence order effect, Pan et al.
[]
For more sequence order effect, Pan et al.
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539
3
INTRODUCTION
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19
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16,966,337
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(19) used digital signal processing filter technique to the PseAA.
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used digital signal processing filter technique to the PseAA.
[ "19" ]
used digital signal processing filter technique to the PseAA.
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539
3
INTRODUCTION
1
1
[ "b1", "b2", "b4", "b14", "b15", "b22", "b4", "b5", "b6", "b18", "b19", "b1", "b9", "b12", "b23", "b24", "b26", "b23", "b24", "b28", "b9", "b25", "b1", "b11", "b29", "b26", "b30", "b1", "b11" ]
16,966,337
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These features based on AA have the advantage of achieving a very high coverage but may have limit on the high performance.
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These features based on AA have the advantage of achieving a very high coverage but may have limit on the high performance.
[]
These features based on AA have the advantage of achieving a very high coverage but may have limit on the high performance.
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true
true
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true
539
3
INTRODUCTION
1
1
[ "b1", "b2", "b4", "b14", "b15", "b22", "b4", "b5", "b6", "b18", "b19", "b1", "b9", "b12", "b23", "b24", "b26", "b23", "b24", "b28", "b9", "b25", "b1", "b11", "b29", "b26", "b30", "b1", "b11" ]
16,966,337
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Other researchers have used several kinds of motif information as the feature of proteins (1,9–12,23,24–26).
[ "1", "2", "4", "14", "15", "22", "4", "5", "6", "18", "19", "1", "9", "12", "23", "24", "26", "23", "24", "28", "9", "25", "1", "11", "29", "26", "30", "1", "11" ]
108
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Other researchers have used several kinds of motif information as the feature of proteins.
[ "1,9–12,23,24–26" ]
Other researchers have used several kinds of motif information as the feature of proteins.
true
true
true
true
true
539
3
INTRODUCTION
1
23
[ "b1", "b2", "b4", "b14", "b15", "b22", "b4", "b5", "b6", "b18", "b19", "b1", "b9", "b12", "b23", "b24", "b26", "b23", "b24", "b28", "b9", "b25", "b1", "b11", "b29", "b26", "b30", "b1", "b11" ]
16,966,337
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Since Nakai and Horton (23) used protein sorting signal motifs in the N-terminal portion of a protein, some researchers (24) have used the motif in the prediction of localization.
[ "1", "2", "4", "14", "15", "22", "4", "5", "6", "18", "19", "1", "9", "12", "23", "24", "26", "23", "24", "28", "9", "25", "1", "11", "29", "26", "30", "1", "11" ]
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Since Nakai and Horton used protein sorting signal motifs in the N-terminal portion of a protein, some researchers have used the motif in the prediction of localization.
[ "23", "24" ]
Since Nakai and Horton used protein sorting signal motifs in the N-terminal portion of a protein, some researchers have used the motif in the prediction of localization.
true
true
true
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true
539
3
INTRODUCTION
1
28
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16,966,337
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Using the 2005 functional domain sequences of SBASE-A which is a collection of well known structural and functional domain types (28), Chou and Cai (9,25) represented a protein as a vector with a 2005-dimensional functional domain composition (SBASE-FunD).
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Using the 2005 functional domain sequences of SBASE-A which is a collection of well known structural and functional domain types, Chou and Cai represented a protein as a vector with a 2005-dimensional functional domain composition (SBASE-FunD).
[ "28", "9,25" ]
Using the 2005 functional domain sequences of SBASE-A which is a collection of well known structural and functional domain types, Chou and Cai represented a protein as a vector with a 2005-dimensional functional domain composition (SBASE-FunD).
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INTRODUCTION
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They also introduced the dimensional functional domain composition (InterPro-FunD) (1,11) using the InterPro database (29).
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They also introduced the dimensional functional domain composition (InterPro-FunD) using the InterPro database.
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They also introduced the dimensional functional domain composition (InterPro-FunD) using the InterPro database.
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539
3
INTRODUCTION
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26
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In contrast, Nair and Rost (26) represented a protein with functional annotations from the SWISS-PROT database (30).
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In contrast, Nair and Rost represented a protein with functional annotations from the SWISS-PROT database.
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In contrast, Nair and Rost represented a protein with functional annotations from the SWISS-PROT database.
true
true
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true
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539
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INTRODUCTION
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Recently, for higher prediction accuracy Cai and Chou (1,11) used Gene Ontology (GO) term as a auxiliary feature of a protein.
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Recently, for higher prediction accuracy Cai and Chou used Gene Ontology (GO) term as a auxiliary feature of a protein.
[ "1,11" ]
Recently, for higher prediction accuracy Cai and Chou used Gene Ontology (GO) term as a auxiliary feature of a protein.
true
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INTRODUCTION
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Even though motif information and GO can improve the prediction accuracy, the information has a limited coverage of the proteins.
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Even though motif information and GO can improve the prediction accuracy, the information has a limited coverage of the proteins.
[]
Even though motif information and GO can improve the prediction accuracy, the information has a limited coverage of the proteins.
true
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INTRODUCTION
1
4
[ "b4", "b13", "b27", "b23", "b2", "b14", "b1", "b3" ]
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2) Class coverage extension: another trend is to increase the coverage of protein localization for practical use.
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2) Class coverage extension: another trend is to increase the coverage of protein localization for practical use.
[]
2) Class coverage extension: another trend is to increase the coverage of protein localization for practical use.
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INTRODUCTION
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[ "b4", "b13", "b27", "b23", "b2", "b14", "b1", "b3" ]
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At the beginning, Nakashima and Nishikawa (4) distinguished between intracellular proteins and extracellular proteins using the AA and the PairAA features.
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At the beginning, Nakashima and Nishikawa distinguished between intracellular proteins and extracellular proteins using the AA and the PairAA features.
[ "4" ]
At the beginning, Nakashima and Nishikawa distinguished between intracellular proteins and extracellular proteins using the AA and the PairAA features.
true
true
true
true
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INTRODUCTION
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[ "b4", "b13", "b27", "b23", "b2", "b14", "b1", "b3" ]
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After that, many researchers enlarged the number of localization classes to 5 classes (13), to 8 classes (27), to 11 classes (23), to 12 classes (2), then to 14 classes (14).
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After that, many researchers enlarged the number of localization classes to 5 classes, to 8 classes, to 11 classes, to 12 classes, then to 14 classes.
[ "13", "27", "23", "2", "14" ]
After that, many researchers enlarged the number of localization classes to 5 classes, to 8 classes, to 11 classes, to 12 classes, then to 14 classes.
true
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540
4
INTRODUCTION
1
1
[ "b4", "b13", "b27", "b23", "b2", "b14", "b1", "b3" ]
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Recently Chou and Cai (1) used up to 22 localization classes using the dataset of Huh et al.
[ "4", "13", "27", "23", "2", "14", "1", "3" ]
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Recently Chou and Cai used up to 22 localization classes using the dataset of Huh et al.
[ "1" ]
Recently Chou and Cai used up to 22 localization classes using the dataset of Huh et al.
true
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true
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540
4
INTRODUCTION
1
3
[ "b4", "b13", "b27", "b23", "b2", "b14", "b1", "b3" ]
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(3), which is the biggest coverage of protein localization up to now.
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1
false
, which is the biggest coverage of protein localization up to now.
[ "3" ]
, which is the biggest coverage of protein localization up to now.
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true
true
false
540
5
INTRODUCTION
1
31
[ "b31", "b32", "b2", "b27", "b6", "b15", "b20", "b1", "b11", "b17", "b23", "b26", "b21", "b24", "b7", "b9", "b16", "b18", "b25", "b12" ]
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3) Computational algorithm: to improve the prediction quality, another trend is to try to use an efficient computational algorithm in the prediction stage.
[ "31", "32", "2", "27", "6", "15", "20", "1", "11", "17", "23", "26", "21", "24", "7", "9", "16", "18", "25", "12" ]
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3) Computational algorithm: to improve the prediction quality, another trend is to try to use an efficient computational algorithm in the prediction stage.
[]
3) Computational algorithm: to improve the prediction quality, another trend is to try to use an efficient computational algorithm in the prediction stage.
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541
5
INTRODUCTION
1
21
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16,966,337
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Current computational methods include the following: a Least Distance Algorithm using various distance measures [a distance in PlotLock (31) that is modified from Mahalanobis distance originally introduced by Chou in predicting protein structural class (32), a Covariant discriminant algorithm (CD) in (2,27), and an aug...
[ "31", "32", "2", "27", "6", "15", "20", "1", "11", "17", "23", "26", "21", "24", "7", "9", "16", "18", "25", "12" ]
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Current computational methods include the following: a Least Distance Algorithm using various distance measures, an Artificial Neural Network approach in, a Nearest Neighbor approach in, a Markov Model (MM) in, a Bayesian Network (BN) approach in, and Support Vector Machines (SVMs) approach in.
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Current computational methods include the following: a Least Distance Algorithm using various distance measures, an Artificial Neural Network approach in, a Nearest Neighbor approach in, a Markov Model (MM) in, a Bayesian Network (BN) approach in, and Support Vector Machines (SVMs) approach in.
true
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INTRODUCTION
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12
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In (12), three algorithms, such as SVMs, a Hidden MM and a BN are used for improving prediction accuracy.
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In, three algorithms, such as SVMs, a Hidden MM and a BN are used for improving prediction accuracy.
[ "12" ]
In, three algorithms, such as SVMs, a Hidden MM and a BN are used for improving prediction accuracy.
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INTRODUCTION
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1
[ "b1" ]
16,966,337
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Even though many previous works have been done for the prediction of protein subcellular localization, none of them tackled effectively the three characteristics of protein localization prediction at the same time.
[ "1" ]
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Even though many previous works have been done for the prediction of protein subcellular localization, none of them tackled effectively the three characteristics of protein localization prediction at the same time.
[]
Even though many previous works have been done for the prediction of protein subcellular localization, none of them tackled effectively the three characteristics of protein localization prediction at the same time.
true
true
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542
6
INTRODUCTION
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1
[ "b1" ]
16,966,337
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For example, many existing predictors use only less than five different subcellular localizations.
[ "1" ]
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0
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For example, many existing predictors use only less than five different subcellular localizations.
[]
For example, many existing predictors use only less than five different subcellular localizations.
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542
6
INTRODUCTION
1
1
[ "b1" ]
16,966,337
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Moreover, very few predictors deal with the issue of multiple-localization proteins except for Chou and Cai (1).
[ "1" ]
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1
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Moreover, very few predictors deal with the issue of multiple-localization proteins except for Chou and Cai.
[ "1" ]
Moreover, very few predictors deal with the issue of multiple-localization proteins except for Chou and Cai.
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INTRODUCTION
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1
[ "b1" ]
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The majority only assumed that there is no multiple-localization protein.
[ "1" ]
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0
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The majority only assumed that there is no multiple-localization protein.
[]
The majority only assumed that there is no multiple-localization protein.
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INTRODUCTION
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[ "b1" ]
16,966,337
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Furthermore, almost all previous methods did not consider the imbalanced problem in a given dataset.
[ "1" ]
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Furthermore, almost all previous methods did not consider the imbalanced problem in a given dataset.
[]
Furthermore, almost all previous methods did not consider the imbalanced problem in a given dataset.
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INTRODUCTION
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[ "b1" ]
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That means these methods achieve high accuracy only for the most populated localizations, such as the ‘nucleus’ and ‘cytosol’.
[ "1" ]
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That means these methods achieve high accuracy only for the most populated localizations, such as the ‘nucleus’ and ‘cytosol’.
[]
That means these methods achieve high accuracy only for the most populated localizations, such as the ‘nucleus’ and ‘cytosol’.
true
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542
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INTRODUCTION
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[ "b1" ]
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They, however, are generally less accurate on the numerous localizations containing fewer individual proteins.
[ "1" ]
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They, however, are generally less accurate on the numerous localizations containing fewer individual proteins.
[]
They, however, are generally less accurate on the numerous localizations containing fewer individual proteins.
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INTRODUCTION
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1
[ "b1" ]
16,966,337
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Thus, a new computational method is eventually needed for more reliable prediction which should have the following characteristics: (i) it can show relatively good performance in case many classes exist, (ii) it can handle a multi-label problem and (iii) it should be robust in an imbalanced dataset.
[ "1" ]
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Thus, a new computational method is eventually needed for more reliable prediction which should have the following characteristics: (i) it can show relatively good performance in case many classes exist, (ii) it can handle a multi-label problem and (iii) it should be robust in an imbalanced dataset.
[]
Thus, a new computational method is eventually needed for more reliable prediction which should have the following characteristics: (i) it can show relatively good performance in case many classes exist, (ii) it can handle a multi-label problem and (iii) it should be robust in an imbalanced dataset.
true
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INTRODUCTION
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Our study is aimed to address these issues.
[ "1" ]
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0
false
Our study is aimed to address these issues.
[]
Our study is aimed to address these issues.
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INTRODUCTION
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33
[ "b33", "b34", "b36", "b37", "b33", "b33", "b36" ]
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NA|NA|NA|NA|NA|NA|NA
To achieve the purpose, we developed a PLPD method which can predict better the localization information of proteins using a Density-induced Support Vector Data Description (D-SVDD) approach.
[ "33", "34", "36", "37", "33", "33", "36" ]
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0
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To achieve the purpose, we developed a PLPD method which can predict better the localization information of proteins using a Density-induced Support Vector Data Description (D-SVDD) approach.
[]
To achieve the purpose, we developed a PLPD method which can predict better the localization information of proteins using a Density-induced Support Vector Data Description (D-SVDD) approach.
true
true
true
true
true
543
7
INTRODUCTION
1
33
[ "b33", "b34", "b36", "b37", "b33", "b33", "b36" ]
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NA|NA|NA|NA|NA|NA|NA
The PLPD stands for ‘Protein Localization Predictor based on D-SVDD’.
[ "33", "34", "36", "37", "33", "33", "36" ]
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The PLPD stands for ‘Protein Localization Predictor based on D-SVDD’.
[]
The PLPD stands for ‘Protein Localization Predictor based on D-SVDD’.
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543
7
INTRODUCTION
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33
[ "b33", "b34", "b36", "b37", "b33", "b33", "b36" ]
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NA|NA|NA|NA|NA|NA|NA
The D-SVDD (33) is a general extension of conventional Support Vector Data Description (C-SVDD) (34–36) inspired by the SVMs (37).
[ "33", "34", "36", "37", "33", "33", "36" ]
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The D-SVDD is a general extension of conventional Support Vector Data Description (C-SVDD) inspired by the SVMs.
[ "33", "34–36", "37" ]
The D-SVDD is a general extension of conventional Support Vector Data Description (C-SVDD) inspired by the SVMs.
true
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INTRODUCTION
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33
[ "b33", "b34", "b36", "b37", "b33", "b33", "b36" ]
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NA|NA|NA|NA|NA|NA|NA
According to the work of Lee et al.
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According to the work of Lee et al.
[]
According to the work of Lee et al.
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INTRODUCTION
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33
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NA|NA|NA|NA|NA|NA|NA
(33), D-SVDD highly outperformed the C-SVDD.
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, D-SVDD highly outperformed the C-SVDD.
[ "33" ]
, D-SVDD highly outperformed the C-SVDD.
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543
7
INTRODUCTION
1
33
[ "b33", "b34", "b36", "b37", "b33", "b33", "b36" ]
16,966,337
NA|NA|NA|NA|NA|NA|NA
D-SVDD is one of one-class classification methods whose purpose is to give a compact description of a set of data referred to as target data.
[ "33", "34", "36", "37", "33", "33", "36" ]
141
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false
D-SVDD is one of one-class classification methods whose purpose is to give a compact description of a set of data referred to as target data.
[]
D-SVDD is one of one-class classification methods whose purpose is to give a compact description of a set of data referred to as target data.
true
true
true
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543
7
INTRODUCTION
1
33
[ "b33", "b34", "b36", "b37", "b33", "b33", "b36" ]
16,966,337
NA|NA|NA|NA|NA|NA|NA
One-class classification methods are suitable for imbalanced datasets since find compact descriptions for target data independently from other data (33,36).
[ "33", "34", "36", "37", "33", "33", "36" ]
156
3,251
0
false
One-class classification methods are suitable for imbalanced datasets since find compact descriptions for target data independently from other data.
[ "33,36" ]
One-class classification methods are suitable for imbalanced datasets since find compact descriptions for target data independently from other data.
true
true
true
true
true
543
7
INTRODUCTION
1
33
[ "b33", "b34", "b36", "b37", "b33", "b33", "b36" ]
16,966,337
NA|NA|NA|NA|NA|NA|NA
Moreover, they are easily used for the dataset whose number of classes is big owing to linear complexity with regard to the number of classes.
[ "33", "34", "36", "37", "33", "33", "36" ]
142
3,252
0
false
Moreover, they are easily used for the dataset whose number of classes is big owing to linear complexity with regard to the number of classes.
[]
Moreover, they are easily used for the dataset whose number of classes is big owing to linear complexity with regard to the number of classes.
true
true
true
true
true
543
7
INTRODUCTION
1
33
[ "b33", "b34", "b36", "b37", "b33", "b33", "b36" ]
16,966,337
NA|NA|NA|NA|NA|NA|NA
However, original D-SVDD is not for a multi-class and multi-label problem.
[ "33", "34", "36", "37", "33", "33", "36" ]
74
3,253
0
false
However, original D-SVDD is not for a multi-class and multi-label problem.
[]
However, original D-SVDD is not for a multi-class and multi-label problem.
true
true
true
true
true
543
7
INTRODUCTION
1
33
[ "b33", "b34", "b36", "b37", "b33", "b33", "b36" ]
16,966,337
NA|NA|NA|NA|NA|NA|NA
For the protein localization problem, thus, we propose the PLPD method by extending the original D-SVDD method using the likelihood of a specific protein localization.
[ "33", "34", "36", "37", "33", "33", "36" ]
167
3,254
0
false
For the protein localization problem, thus, we propose the PLPD method by extending the original D-SVDD method using the likelihood of a specific protein localization.
[]
For the protein localization problem, thus, we propose the PLPD method by extending the original D-SVDD method using the likelihood of a specific protein localization.
true
true
true
true
true
543
8
INTRODUCTION
1
3
[ "b3" ]
16,966,337
pmid-14562095
The structure of the paper is organized as follows:
[ "3" ]
51
3,255
0
false
The structure of the paper is organized as follows:
[]
The structure of the paper is organized as follows:
true
true
false
true
false
544
8
INTRODUCTION
1
3
[ "b3" ]
16,966,337
pmid-14562095
First, we briefly provide the information on the C-SVDD and the D-SVDD in Section 2.
[ "3" ]
84
3,256
0
false
First, we briefly provide the information on the C-SVDD and the D-SVDD in Section 2.
[]
First, we briefly provide the information on the C-SVDD and the D-SVDD in Section 2.
true
true
true
true
true
544
8
INTRODUCTION
1
3
[ "b3" ]
16,966,337
pmid-14562095
In this section, we also introduce the proposed PLPD method for protein localization prediction.
[ "3" ]
96
3,257
0
false
In this section, we also introduce the proposed PLPD method for protein localization prediction.
[]
In this section, we also introduce the proposed PLPD method for protein localization prediction.
true
true
true
true
true
544
8
INTRODUCTION
1
3
[ "b3" ]
16,966,337
pmid-14562095
Section 3 highlights the potentials of the proposed approach through experiments with datasets from the work of Huh et al.
[ "3" ]
122
3,258
0
false
Section 3 highlights the potentials of the proposed approach through experiments with datasets from the work of Huh et al.
[]
Section 3 highlights the potentials of the proposed approach through experiments with datasets from the work of Huh et al.
true
true
true
true
true
544
8
INTRODUCTION
1
3
[ "b3" ]
16,966,337
pmid-14562095
Concluding remarks are presented in Section 4.
[ "3" ]
46
3,259
0
false
Concluding remarks are presented in Section 4.
[]
Concluding remarks are presented in Section 4.
true
true
true
true
true
544
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4", "b5" ]
16,931,491
pmid-11237014|pmid-14685227|pmid-15499007|pmid-12164776|pmid-12960966
Large-scale international human genomic/genetic studies, such as the Human Genome Project (1), International HapMap Project (2) and ENCODE Project (3), have contributed to the further understanding of the human genome and genetic disorders.
[ "1", "2", "3", "4", "5" ]
240
3,260
1
false
Large-scale international human genomic/genetic studies, such as the Human Genome Project, International HapMap Project and ENCODE Project, have contributed to the further understanding of the human genome and genetic disorders.
[ "1", "2", "3" ]
Large-scale international human genomic/genetic studies, such as the Human Genome Project, International HapMap Project and ENCODE Project, have contributed to the further understanding of the human genome and genetic disorders.
true
true
true
true
true
545
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4", "b5" ]
16,931,491
pmid-11237014|pmid-14685227|pmid-15499007|pmid-12164776|pmid-12960966
These breakthroughs were made mainly possible by the advent of mature genotyping techniques [e.g.
[ "1", "2", "3", "4", "5" ]
97
3,261
0
false
These breakthroughs were made mainly possible by the advent of mature genotyping techniques [e.g.
[]
These breakthroughs were made mainly possible by the advent of mature genotyping techniques [e.g.
true
true
true
true
true
545
0
INTRODUCTION
1
4
[ "b1", "b2", "b3", "b4", "b5" ]
16,931,491
pmid-11237014|pmid-14685227|pmid-15499007|pmid-12164776|pmid-12960966
MALDI-TOF mass spectrometry (4) and oligonucleotide microarrays (5)].
[ "1", "2", "3", "4", "5" ]
69
3,262
1
false
MALDI-TOF mass spectrometry and oligonucleotide microarrays ].
[ "4", "5" ]
MALDI-TOF mass spectrometry and oligonucleotide microarrays ].
true
true
true
true
true
545
1
INTRODUCTION
1
6
[ "b6", "b11", "b12", "b15", "b16", "b17", "b18", "b20", "b21", "b22", "b23", "b24" ]
16,931,491
pmid-9326338|pmid-15996171|pmid-11140946|pmid-16674557|NA|pmid-12939204|pmid-12112658|pmid-12485472|pmid-9477339|pmid-2995996|pmid-12415316|NA
Although, high-throughput genotyping techniques are readily available, the cost is still very high for large-scale genetic studies that usually involve two high-dimension variables, i.e.
[ "6", "11", "12", "15", "16", "17", "18", "20", "21", "22", "23", "24" ]
186
3,263
0
false
Although, high-throughput genotyping techniques are readily available, the cost is still very high for large-scale genetic studies that usually involve two high-dimension variables, i.e.
[]
Although, high-throughput genotyping techniques are readily available, the cost is still very high for large-scale genetic studies that usually involve two high-dimension variables, i.e.
true
true
true
true
true
546
1
INTRODUCTION
1
6
[ "b6", "b11", "b12", "b15", "b16", "b17", "b18", "b20", "b21", "b22", "b23", "b24" ]
16,931,491
pmid-9326338|pmid-15996171|pmid-11140946|pmid-16674557|NA|pmid-12939204|pmid-12112658|pmid-12485472|pmid-9477339|pmid-2995996|pmid-12415316|NA
large sample sizes and a large number of genetic markers.
[ "6", "11", "12", "15", "16", "17", "18", "20", "21", "22", "23", "24" ]
57
3,264
0
false
large sample sizes and a large number of genetic markers.
[]
large sample sizes and a large number of genetic markers.
false
true
true
true
false
546
1
INTRODUCTION
1
6
[ "b6", "b11", "b12", "b15", "b16", "b17", "b18", "b20", "b21", "b22", "b23", "b24" ]
16,931,491
pmid-9326338|pmid-15996171|pmid-11140946|pmid-16674557|NA|pmid-12939204|pmid-12112658|pmid-12485472|pmid-9477339|pmid-2995996|pmid-12415316|NA
Thus, the development of pooled DNA experiment (allelotyping) technology would help reduce the cost associated with large sample sizes.
[ "6", "11", "12", "15", "16", "17", "18", "20", "21", "22", "23", "24" ]
135
3,265
0
false
Thus, the development of pooled DNA experiment (allelotyping) technology would help reduce the cost associated with large sample sizes.
[]
Thus, the development of pooled DNA experiment (allelotyping) technology would help reduce the cost associated with large sample sizes.
true
true
true
true
true
546
1
INTRODUCTION
1
6
[ "b6", "b11", "b12", "b15", "b16", "b17", "b18", "b20", "b21", "b22", "b23", "b24" ]
16,931,491
pmid-9326338|pmid-15996171|pmid-11140946|pmid-16674557|NA|pmid-12939204|pmid-12112658|pmid-12485472|pmid-9477339|pmid-2995996|pmid-12415316|NA
Allelotyping involves mixing genomic DNAs from different study subjects to reduce the number of samples, and it is an economical alternative compared with individual genotyping experiments.
[ "6", "11", "12", "15", "16", "17", "18", "20", "21", "22", "23", "24" ]
189
3,266
0
false
Allelotyping involves mixing genomic DNAs from different study subjects to reduce the number of samples, and it is an economical alternative compared with individual genotyping experiments.
[]
Allelotyping involves mixing genomic DNAs from different study subjects to reduce the number of samples, and it is an economical alternative compared with individual genotyping experiments.
true
true
true
true
true
546
1
INTRODUCTION
1
6
[ "b6", "b11", "b12", "b15", "b16", "b17", "b18", "b20", "b21", "b22", "b23", "b24" ]
16,931,491
pmid-9326338|pmid-15996171|pmid-11140946|pmid-16674557|NA|pmid-12939204|pmid-12112658|pmid-12485472|pmid-9477339|pmid-2995996|pmid-12415316|NA
Allelotyping has been broadly used in disease gene association mapping (6–11), polymorphism identification/validation (12–15), and analysis of genetic diversity (16,17).
[ "6", "11", "12", "15", "16", "17", "18", "20", "21", "22", "23", "24" ]
169
3,267
0
false
Allelotyping has been broadly used in disease gene association mapping, polymorphism identification/validation, and analysis of genetic diversity.
[ "6–11", "12–15", "16,17" ]
Allelotyping has been broadly used in disease gene association mapping, polymorphism identification/validation, and analysis of genetic diversity.
true
true
true
true
true
546
1
INTRODUCTION
1
21
[ "b6", "b11", "b12", "b15", "b16", "b17", "b18", "b20", "b21", "b22", "b23", "b24" ]
16,931,491
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This technique has been used to type single nucleotide polymorphisms (SNPs) (18–20), short tandem repeat polymorphisms (STRPs) (21), and restriction fragment length polymorphisms (RFLPs) (22).
[ "6", "11", "12", "15", "16", "17", "18", "20", "21", "22", "23", "24" ]
192
3,268
1
false
This technique has been used to type single nucleotide polymorphisms (SNPs), short tandem repeat polymorphisms (STRPs), and restriction fragment length polymorphisms (RFLPs).
[ "18–20", "21", "22" ]
This technique has been used to type single nucleotide polymorphisms (SNPs), short tandem repeat polymorphisms (STRPs), and restriction fragment length polymorphisms (RFLPs).
true
true
true
true
true
546
1
INTRODUCTION
1
6
[ "b6", "b11", "b12", "b15", "b16", "b17", "b18", "b20", "b21", "b22", "b23", "b24" ]
16,931,491
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The use of allelotyping in these methods has been comprehensively reviewed (23,24).
[ "6", "11", "12", "15", "16", "17", "18", "20", "21", "22", "23", "24" ]
83
3,269
0
false
The use of allelotyping in these methods has been comprehensively reviewed.
[ "23,24" ]
The use of allelotyping in these methods has been comprehensively reviewed.
true
true
true
true
true
546
2
INTRODUCTION
1
5
[ "b5", "b25", "b26", "b27", "b32" ]
16,931,491
pmid-12960966|pmid-14668223|NA|pmid-11704927|pmid-16627870
On the other hand, the need to reduce the cost of genotyping large numbers of SNPs has prompted the development of modern microarray-based genotyping methods (5,25).
[ "5", "25", "26", "27", "32" ]
165
3,270
0
false
On the other hand, the need to reduce the cost of genotyping large numbers of SNPs has prompted the development of modern microarray-based genotyping methods.
[ "5,25" ]
On the other hand, the need to reduce the cost of genotyping large numbers of SNPs has prompted the development of modern microarray-based genotyping methods.
true
true
true
true
true
547
2
INTRODUCTION
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26
[ "b5", "b25", "b26", "b27", "b32" ]
16,931,491
pmid-12960966|pmid-14668223|NA|pmid-11704927|pmid-16627870
For example, the Affymetrix GeneChip Human Mapping 100 K Set provides genome-wide genotyping for each individual using only a set of two dense oligonucleotide arrays (26).
[ "5", "25", "26", "27", "32" ]
171
3,271
1
false
For example, the Affymetrix GeneChip Human Mapping 100 K Set provides genome-wide genotyping for each individual using only a set of two dense oligonucleotide arrays.
[ "26" ]
For example, the Affymetrix GeneChip Human Mapping 100 K Set provides genome-wide genotyping for each individual using only a set of two dense oligonucleotide arrays.
true
true
true
true
true
547
2
INTRODUCTION
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5
[ "b5", "b25", "b26", "b27", "b32" ]
16,931,491
pmid-12960966|pmid-14668223|NA|pmid-11704927|pmid-16627870
This technique greatly reduces the costs of primer design and assay reagents.
[ "5", "25", "26", "27", "32" ]
77
3,272
0
false
This technique greatly reduces the costs of primer design and assay reagents.
[]
This technique greatly reduces the costs of primer design and assay reagents.
true
true
true
true
true
547
2
INTRODUCTION
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5
[ "b5", "b25", "b26", "b27", "b32" ]
16,931,491
pmid-12960966|pmid-14668223|NA|pmid-11704927|pmid-16627870
Integration of pooled DNA experiments and microarray-based genotyping creates a very cost-effective and high-throughput marker-typing platform for conducting large-scale genetic studies (27–32).
[ "5", "25", "26", "27", "32" ]
194
3,273
0
false
Integration of pooled DNA experiments and microarray-based genotyping creates a very cost-effective and high-throughput marker-typing platform for conducting large-scale genetic studies.
[ "27–32" ]
Integration of pooled DNA experiments and microarray-based genotyping creates a very cost-effective and high-throughput marker-typing platform for conducting large-scale genetic studies.
true
true
true
true
true
547
3
INTRODUCTION
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23
[ "b23", "b33", "b34", "b35" ]
16,931,491
pmid-12415316|pmid-15119834|pmid-15677751|pmid-11140947
The success of a pooled DNA experiment mainly relies on the accurate and reliable estimation of allele frequencies of genetic markers.
[ "23", "33", "34", "35" ]
134
3,274
0
false
The success of a pooled DNA experiment mainly relies on the accurate and reliable estimation of allele frequencies of genetic markers.
[]
The success of a pooled DNA experiment mainly relies on the accurate and reliable estimation of allele frequencies of genetic markers.
true
true
true
true
true
548
3
INTRODUCTION
1
23
[ "b23", "b33", "b34", "b35" ]
16,931,491
pmid-12415316|pmid-15119834|pmid-15677751|pmid-11140947
The estimation procedure mustly consider an adjustment for an imbalance of nucleotide reaction—referred to as ‘preferential amplification’ and/or ‘differential hybridization’.
[ "23", "33", "34", "35" ]
175
3,275
0
false
The estimation procedure mustly consider an adjustment for an imbalance of nucleotide reaction—referred to as ‘preferential amplification’ and/or ‘differential hybridization’.
[]
The estimation procedure mustly consider an adjustment for an imbalance of nucleotide reaction—referred to as ‘preferential amplification’ and/or ‘differential hybridization’.
true
true
true
true
true
548
3
INTRODUCTION
1
23
[ "b23", "b33", "b34", "b35" ]
16,931,491
pmid-12415316|pmid-15119834|pmid-15677751|pmid-11140947
Preferential amplification/hybridization is a function of the characteristics of different nucleotides.
[ "23", "33", "34", "35" ]
103
3,276
0
false
Preferential amplification/hybridization is a function of the characteristics of different nucleotides.
[]
Preferential amplification/hybridization is a function of the characteristics of different nucleotides.
true
true
true
true
true
548
3
INTRODUCTION
1
23
[ "b23", "b33", "b34", "b35" ]
16,931,491
pmid-12415316|pmid-15119834|pmid-15677751|pmid-11140947
It is a natural phenomenon that could occur during several typing stages, such as PCR amplification, primer extension, array hybridization or signal detection (23,33), and its magnitude is quantified as the coefficient of preferential amplification/hybridization (CPA) (34,35).
[ "23", "33", "34", "35" ]
277
3,277
0
false
It is a natural phenomenon that could occur during several typing stages, such as PCR amplification, primer extension, array hybridization or signal detection, and its magnitude is quantified as the coefficient of preferential amplification/hybridization (CPA).
[ "23,33", "34,35" ]
It is a natural phenomenon that could occur during several typing stages, such as PCR amplification, primer extension, array hybridization or signal detection, and its magnitude is quantified as the coefficient of preferential amplification/hybridization (CPA).
true
true
true
true
true
548
3
INTRODUCTION
1
23
[ "b23", "b33", "b34", "b35" ]
16,931,491
pmid-12415316|pmid-15119834|pmid-15677751|pmid-11140947
As the name suggests, preferential amplification/hybridization means that one allele tends to be amplified or hybridized more efficiently than another.
[ "23", "33", "34", "35" ]
151
3,278
0
false
As the name suggests, preferential amplification/hybridization means that one allele tends to be amplified or hybridized more efficiently than another.
[]
As the name suggests, preferential amplification/hybridization means that one allele tends to be amplified or hybridized more efficiently than another.
true
true
true
true
true
548
3
INTRODUCTION
1
23
[ "b23", "b33", "b34", "b35" ]
16,931,491
pmid-12415316|pmid-15119834|pmid-15677751|pmid-11140947
Thus, for heterozygous individuals the fluorescence intensity of two alleles containing a SNP may differ.
[ "23", "33", "34", "35" ]
105
3,279
0
false
Thus, for heterozygous individuals the fluorescence intensity of two alleles containing a SNP may differ.
[]
Thus, for heterozygous individuals the fluorescence intensity of two alleles containing a SNP may differ.
true
true
true
true
true
548
3
INTRODUCTION
1
23
[ "b23", "b33", "b34", "b35" ]
16,931,491
pmid-12415316|pmid-15119834|pmid-15677751|pmid-11140947
By definition, CPA is the ratio of average peak intensities of two alleles.
[ "23", "33", "34", "35" ]
75
3,280
0
false
By definition, CPA is the ratio of average peak intensities of two alleles.
[]
By definition, CPA is the ratio of average peak intensities of two alleles.
true
true
true
true
true
548
3
INTRODUCTION
1
23
[ "b23", "b33", "b34", "b35" ]
16,931,491
pmid-12415316|pmid-15119834|pmid-15677751|pmid-11140947
A CPA > 1 indicates that the first allele tends to be amplified/hybridized more efficiently than the second allele; when CPA = 1, there is no preferential amplification/hybridization; if CPA < 1, the first allele tends to be amplified/hybridized less efficiently than the second one.
[ "23", "33", "34", "35" ]
283
3,281
0
false
A CPA > 1 indicates that the first allele tends to be amplified/hybridized more efficiently than the second allele; when CPA = 1, there is no preferential amplification/hybridization; if CPA < 1, the first allele tends to be amplified/hybridized less efficiently than the second one.
[]
A CPA > 1 indicates that the first allele tends to be amplified/hybridized more efficiently than the second allele; when CPA = 1, there is no preferential amplification/hybridization; if CPA < 1, the first allele tends to be amplified/hybridized less efficiently than the second one.
true
true
true
true
true
548
3
INTRODUCTION
1
23
[ "b23", "b33", "b34", "b35" ]
16,931,491
pmid-12415316|pmid-15119834|pmid-15677751|pmid-11140947
This factor might have little impact on genotype calling for individual genotyping; however, it distorts the estimation of allele frequency in DNA-pooling allelotyping, where allele frequencies are estimated by calculating relative peak intensities of two alleles accumulated in a DNA pool.
[ "23", "33", "34", "35" ]
290
3,282
0
false
This factor might have little impact on genotype calling for individual genotyping; however, it distorts the estimation of allele frequency in DNA-pooling allelotyping, where allele frequencies are estimated by calculating relative peak intensities of two alleles accumulated in a DNA pool.
[]
This factor might have little impact on genotype calling for individual genotyping; however, it distorts the estimation of allele frequency in DNA-pooling allelotyping, where allele frequencies are estimated by calculating relative peak intensities of two alleles accumulated in a DNA pool.
true
true
true
true
true
548
4
INTRODUCTION
1
34
[ "b34", "b36" ]
16,931,491
pmid-15677751|pmid-15700279
In a pooled DNA study, allele frequency estimates are biased if adjustments are not made for preferential amplification/hybridization.
[ "34", "36" ]
134
3,283
0
false
In a pooled DNA study, allele frequency estimates are biased if adjustments are not made for preferential amplification/hybridization.
[]
In a pooled DNA study, allele frequency estimates are biased if adjustments are not made for preferential amplification/hybridization.
true
true
true
true
true
549
4
INTRODUCTION
1
34
[ "b34", "b36" ]
16,931,491
pmid-15677751|pmid-15700279
This issue has generated much research interest (34–36).
[ "34", "36" ]
56
3,284
0
false
This issue has generated much research interest.
[ "34–36" ]
This issue has generated much research interest.
true
true
true
true
true
549
4
INTRODUCTION
1
34
[ "b34", "b36" ]
16,931,491
pmid-15677751|pmid-15700279
Under a feasible pooled DNA experiment, the estimation bias relates to the extent of preferential amplification/hybridization and ratio of peak intensities (RPI) of two alleles (Figure 1).
[ "34", "36" ]
188
3,285
0
false
Under a feasible pooled DNA experiment, the estimation bias relates to the extent of preferential amplification/hybridization and ratio of peak intensities (RPI) of two alleles (Figure 1).
[]
Under a feasible pooled DNA experiment, the estimation bias relates to the extent of preferential amplification/hybridization and ratio of peak intensities (RPI) of two alleles (Figure 1).
true
true
true
true
true
549
4
INTRODUCTION
1
34
[ "b34", "b36" ]
16,931,491
pmid-15677751|pmid-15700279
For example, the positive (negative) bias for CPA = 2 and RPI = 1 (CPA = 0.5 and RPI = 1) is about 0.17 for allele frequency estimation, and the positive (negative) bias for CPA = 4 and RPI = 1 (CPA = 0.25 and RPI = 1) is about 0.30.
[ "34", "36" ]
233
3,286
0
false
For example, the positive (negative) bias for CPA = 2 and RPI = 1 (CPA = 0.5 and RPI = 1) is about 0.17 for allele frequency estimation, and the positive (negative) bias for CPA = 4 and RPI = 1 (CPA = 0.25 and RPI = 1) is about 0.30.
[]
For example, the positive (negative) bias for CPA = 2 and RPI = 1 (CPA = 0.5 and RPI = 1) is about 0.17 for allele frequency estimation, and the positive (negative) bias for CPA = 4 and RPI = 1 (CPA = 0.25 and RPI = 1) is about 0.30.
true
true
true
true
true
549
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16,931,491
pmid-12482934|pmid-11140947|NA|pmid-15677751|pmid-15701753|pmid-15700279
In many studies, additional heterozygous individuals have been collected to perform a CPA adjustment (7,35,37).
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In many studies, additional heterozygous individuals have been collected to perform a CPA adjustment.
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In many studies, additional heterozygous individuals have been collected to perform a CPA adjustment.
true
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INTRODUCTION
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16,931,491
pmid-12482934|pmid-11140947|NA|pmid-15677751|pmid-15701753|pmid-15700279
Moreover, for two kinds of genotyping experiments—sequential genotyping and large-scale genotyping—one can calculate the required number of heterozygous individuals to yield a reasonably accurate and precise estimation of CPA (34).
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231
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false
Moreover, for two kinds of genotyping experiments—sequential genotyping and large-scale genotyping—one can calculate the required number of heterozygous individuals to yield a reasonably accurate and precise estimation of CPA.
[ "34" ]
Moreover, for two kinds of genotyping experiments—sequential genotyping and large-scale genotyping—one can calculate the required number of heterozygous individuals to yield a reasonably accurate and precise estimation of CPA.
true
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true
550
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INTRODUCTION
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16,931,491
pmid-12482934|pmid-11140947|NA|pmid-15677751|pmid-15701753|pmid-15700279
The required number of heterozygotes follows a negative binomial distribution in the former experiment and a binomial distribution for the latter.
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146
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The required number of heterozygotes follows a negative binomial distribution in the former experiment and a binomial distribution for the latter.
[]
The required number of heterozygotes follows a negative binomial distribution in the former experiment and a binomial distribution for the latter.
true
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true
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true
550
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INTRODUCTION
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7
[ "b7", "b35", "b37", "b34", "b38", "b36" ]
16,931,491
pmid-12482934|pmid-11140947|NA|pmid-15677751|pmid-15701753|pmid-15700279
Allele frequency and RPI variability affect the required number of heterozygotes.
[ "7", "35", "37", "34", "38", "36" ]
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Allele frequency and RPI variability affect the required number of heterozygotes.
[]
Allele frequency and RPI variability affect the required number of heterozygotes.
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true
true
550
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INTRODUCTION
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16,931,491
pmid-12482934|pmid-11140947|NA|pmid-15677751|pmid-15701753|pmid-15700279
A SNP with a low minor allele frequency and/or high RPI variability requires a large number samples to attain the necessary precision.
[ "7", "35", "37", "34", "38", "36" ]
134
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A SNP with a low minor allele frequency and/or high RPI variability requires a large number samples to attain the necessary precision.
[]
A SNP with a low minor allele frequency and/or high RPI variability requires a large number samples to attain the necessary precision.
true
true
true
true
true
550
5
INTRODUCTION
1
7
[ "b7", "b35", "b37", "b34", "b38", "b36" ]
16,931,491
pmid-12482934|pmid-11140947|NA|pmid-15677751|pmid-15701753|pmid-15700279
However, additional genotyping of large numbers of heterozygous individuals increases both cost and effort.
[ "7", "35", "37", "34", "38", "36" ]
107
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However, additional genotyping of large numbers of heterozygous individuals increases both cost and effort.
[]
However, additional genotyping of large numbers of heterozygous individuals increases both cost and effort.
true
true
true
true
true
550
5
INTRODUCTION
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38
[ "b7", "b35", "b37", "b34", "b38", "b36" ]
16,931,491
pmid-12482934|pmid-11140947|NA|pmid-15677751|pmid-15701753|pmid-15700279
Thus, the construction of a central resource (38) for information on preferential amplification/hybridization or the use of a robust empirical estimation (36) would help diminish the cost of experimentation.
[ "7", "35", "37", "34", "38", "36" ]
207
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Thus, the construction of a central resource for information on preferential amplification/hybridization or the use of a robust empirical estimation would help diminish the cost of experimentation.
[ "38", "36" ]
Thus, the construction of a central resource for information on preferential amplification/hybridization or the use of a robust empirical estimation would help diminish the cost of experimentation.
true
true
true
true
true
550
6
INTRODUCTION
0
null
null
16,931,491
null
In the present study, we surveyed the distribution of CPA across the human genome and established a statistical model for CPA.
null
126
3,294
0
false
null
null
In the present study, we surveyed the distribution of CPA across the human genome and established a statistical model for CPA.
true
true
true
true
true
551
6
INTRODUCTION
0
null
null
16,931,491
null
We have constructed a publicly available database of CPA for different ethnic populations, and we suggest guidance for the use of CPA in DNA pooling studies.
null
157
3,295
0
false
null
null
We have constructed a publicly available database of CPA for different ethnic populations, and we suggest guidance for the use of CPA in DNA pooling studies.
true
true
true
true
true
551
6
INTRODUCTION
0
null
null
16,931,491
null
The robustness of allele frequency estimation using the database was validated by a pooled DNA experiment.
null
106
3,296
0
false
null
null
The robustness of allele frequency estimation using the database was validated by a pooled DNA experiment.
true
true
true
true
true
551
0
INTRODUCTION
1
1
[ "b1", "b2", "b6", "b7", "b9" ]
17,142,221
pmid-12519985|pmid-16554755|pmid-12872131|pmid-12634792|pmid-12634793
The Saccharomyces Genome Database (SGD) collects, organizes and presents biological information about the genes and proteins of the budding yeast Saccharomyces cerevisiae.
[ "1", "2", "6", "7", "9" ]
171
3,297
0
false
The Saccharomyces Genome Database (SGD) collects, organizes and presents biological information about the genes and proteins of the budding yeast Saccharomyces cerevisiae.
[]
The Saccharomyces Genome Database (SGD) collects, organizes and presents biological information about the genes and proteins of the budding yeast Saccharomyces cerevisiae.
true
true
true
true
true
552
0
INTRODUCTION
1
1
[ "b1", "b2", "b6", "b7", "b9" ]
17,142,221
pmid-12519985|pmid-16554755|pmid-12872131|pmid-12634792|pmid-12634793
In 2003, in response to the community's needs for additional sequence-based predictive protein information, SGD introduced the Protein Information page, the PDB Homologs page, and the eMOTIF resource for the display of shared protein motifs (1).
[ "1", "2", "6", "7", "9" ]
245
3,298
1
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In 2003, in response to the community's needs for additional sequence-based predictive protein information, SGD introduced the Protein Information page, the PDB Homologs page, and the eMOTIF resource for the display of shared protein motifs.
[ "1" ]
In 2003, in response to the community's needs for additional sequence-based predictive protein information, SGD introduced the Protein Information page, the PDB Homologs page, and the eMOTIF resource for the display of shared protein motifs.
true
true
true
true
true
552
0
INTRODUCTION
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1
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17,142,221
pmid-12519985|pmid-16554755|pmid-12872131|pmid-12634792|pmid-12634793
Since that time, there has been a marked increase in the number of studies focused on protein function, regulation and pathway/process involvement.
[ "1", "2", "6", "7", "9" ]
147
3,299
0
false
Since that time, there has been a marked increase in the number of studies focused on protein function, regulation and pathway/process involvement.
[]
Since that time, there has been a marked increase in the number of studies focused on protein function, regulation and pathway/process involvement.
true
true
true
true
true
552