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Datasets:
maomlab
/
AttentiveSkin

Tasks:
Tabular Classification
Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
1K - 10K
Tags:
chemistry
toxicology
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files
xet
Community
2
haneulpark commited on
Commit
3d02a3c
·
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1 Parent(s): 9087cf7

Update README.md

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  1. README.md +84 -80
README.md CHANGED
@@ -6,9 +6,11 @@ tags:
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  - toxicology
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  pretty_name: AttentiveSkin
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  dataset_summary: >-
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- Compiled GHS dataset comprising 731 Corrosion, 1283 Irritation, and 1205 Negative samples from 6 governmental databases and 2 external datasets.
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- Results of the two binary tasks (Corr vs Neg, Irrit vs Neg) will be generated separately.
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- citation: >-
 
 
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  @article{,
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  author = {Zejun Huang and Shang Lou and Haoqiang Wang and Weihua Li and Guixia Liu, and Yun Tang},
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  doi = {10.1021/acs.chemrestox.3c00332},
@@ -37,90 +39,92 @@ configs:
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  - split: test
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  path: Irrit_Neg/test.csv
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  - split: train
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- path: Irrit_Neg/train.csv
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  dataset_info:
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  - config_name: Corr_Neg
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  features:
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- - name: "Name"
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- dtype: string
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- - name: "Synonym"
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- dtype: string
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- - name: "CAS RN"
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- dtype: string
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- - name: "GHS"
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- dtype:
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- class_label:
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- names:
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- 0: "NC"
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- 1: "Cat 1"
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- - name: "Detailed Page"
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- dtype: string
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- - name: "Evidence"
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- dtype: string
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- - name: "OECD TG 404"
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- dtype: string
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- - name: "Data Source"
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- dtype: string
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- - name: "Frequency"
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- dtype: int64
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- - name: "SMILES"
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- dtype: string
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- - name: "SMILES URL"
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- dtype: string
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- - name: "SMILES Source"
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- dtype: string
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- - name: "Canonical SMILES"
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- dtype: string
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- - name: "Split"
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- dtype: string
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  splits:
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- - name: train
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- num_bytes: 196688
79
- num_examples: 1755
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- - name: test
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- num_bytes: 20400
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- num_examples: 181
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  - config_name: Irrit_Neg
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  features:
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- - name: "Name"
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- dtype: string
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- - name: "Synonym"
88
- dtype: string
89
- - name: "CAS RN"
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- dtype: string
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- - name: "GHS"
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- dtype:
93
- class_label:
94
- names:
95
- 0: "NC"
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- 1: "Cat 2"
97
- - name: "Detailed Page"
98
- dtype: string
99
- - name: "Evidence"
100
- dtype: string
101
- - name: "OECD TG 404"
102
- dtype: string
103
- - name: "Data Source"
104
- dtype: string
105
- - name: "Frequency"
106
- dtype: int64
107
- - name: "SMILES"
108
- dtype: string
109
- - name: "SMILES URL"
110
- dtype: string
111
- - name: "SMILES Source"
112
- dtype: string
113
- - name: "Canonical SMILES"
114
- dtype: string
115
- - name: "Split"
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- dtype: string
117
  splits:
118
- - name: train
119
- num_bytes: 249776
120
- num_examples: 2229
121
- - name: test
122
- num_bytes: 29136
123
- num_examples: 259
 
 
124
  ---
125
  # Attentive Skin
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  To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods
 
6
  - toxicology
7
  pretty_name: AttentiveSkin
8
  dataset_summary: >-
9
+ Compiled GHS dataset comprising 731 Corrosion, 1283 Irritation, and 1205
10
+ Negative samples from 6 governmental databases and 2 external datasets.
11
+ Results of the two binary tasks (Corr vs Neg, Irrit vs Neg) will be generated
12
+ separately.
13
+ citation: |-
14
  @article{,
15
  author = {Zejun Huang and Shang Lou and Haoqiang Wang and Weihua Li and Guixia Liu, and Yun Tang},
16
  doi = {10.1021/acs.chemrestox.3c00332},
 
39
  - split: test
40
  path: Irrit_Neg/test.csv
41
  - split: train
42
+ path: Irrit_Neg/train.csv
43
  dataset_info:
44
  - config_name: Corr_Neg
45
  features:
46
+ - name: Name
47
+ dtype: string
48
+ - name: Synonym
49
+ dtype: string
50
+ - name: CAS RN
51
+ dtype: string
52
+ - name: GHS
53
+ dtype:
54
+ class_label:
55
+ names:
56
+ '0': NC
57
+ '1': Cat 1
58
+ - name: Detailed Page
59
+ dtype: string
60
+ - name: Evidence
61
+ dtype: string
62
+ - name: OECD TG 404
63
+ dtype: string
64
+ - name: Data Source
65
+ dtype: string
66
+ - name: Frequency
67
+ dtype: int64
68
+ - name: SMILES
69
+ dtype: string
70
+ - name: SMILES URL
71
+ dtype: string
72
+ - name: SMILES Source
73
+ dtype: string
74
+ - name: Canonical SMILES
75
+ dtype: string
76
+ - name: Split
77
+ dtype: string
78
  splits:
79
+ - name: train
80
+ num_bytes: 196688
81
+ num_examples: 1755
82
+ - name: test
83
+ num_bytes: 20400
84
+ num_examples: 181
85
  - config_name: Irrit_Neg
86
  features:
87
+ - name: Name
88
+ dtype: string
89
+ - name: Synonym
90
+ dtype: string
91
+ - name: CAS RN
92
+ dtype: string
93
+ - name: GHS
94
+ dtype:
95
+ class_label:
96
+ names:
97
+ '0': NC
98
+ '1': Cat 2
99
+ - name: Detailed Page
100
+ dtype: string
101
+ - name: Evidence
102
+ dtype: string
103
+ - name: OECD TG 404
104
+ dtype: string
105
+ - name: Data Source
106
+ dtype: string
107
+ - name: Frequency
108
+ dtype: int64
109
+ - name: SMILES
110
+ dtype: string
111
+ - name: SMILES URL
112
+ dtype: string
113
+ - name: SMILES Source
114
+ dtype: string
115
+ - name: Canonical SMILES
116
+ dtype: string
117
+ - name: Split
118
+ dtype: string
119
  splits:
120
+ - name: train
121
+ num_bytes: 249776
122
+ num_examples: 2229
123
+ - name: test
124
+ num_bytes: 29136
125
+ num_examples: 259
126
+ task_categories:
127
+ - tabular-classification
128
  ---
129
  # Attentive Skin
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  To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods