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- ---
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- license: cc-by-nc-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: cc-by-nc-4.0
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+ size_categories:
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+ - 100K<n<1M
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+ ---
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+ ## Pep2Prob
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+ Pep2Prob is a comprehensive dataset designed to predict peptide-specific fragment ion probability in tandem mass spectrometry (MS/MS) based proteomics studies.
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+ This dataset addresses the limitations of conventional global statistical approaches by enabling the development of models that can predict fragmentation probabilities based on peptide sequence context.
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+ The dataset provides:
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+ - Fragment ion probability statistics for 608,780 unique peptide precursors from over 183 million high-resolution HCD spectr
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+ - Diverse representation of precursors with varying lengths (7-40), charge states (1-8)
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+ - High-quality annotations derived from validated peptide assignments
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+ - Comprehensive coverage of at most 235 fragment ion per precursor
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+
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+ ## Dataset Structure
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+ The dataset is organized as follows:
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+ - `pep2prob_dataset.csv`: The main dataset file containing fragment ion probability statistics derived from over 183 million high-resolution HCD MS/MS spectra, comprising 608,780 unique peptide precursors.
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+ - `X_columns.csv`: Metadata file describing the input features available for each peptide-fragment pair, which may include peptide sequence information, amino acid properties, fragment ion types, and other relevant physicochemical features.
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+ - `Y_columns.csv`: Metadata file describing the target variables, specifically the probability values for different fragment ion types.
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+ - `train_test_split_set_1\` through `train_test_split_set_5\`: Five pre-defined data splits for cross-validation, enabling consistent and reproducible model training and evaluation. Each split contains training and testing indices to facilitate standardized benchmarking of prediction models.
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+ ## Applications
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+ This dataset enables:
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+ - Development of peptide-specific fragment ion probability prediction models
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+ - Benchmarking of machine learning approaches of increasing complexity
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+ - Improvement of peptide identification algorithms in proteomics
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+ - Exploration of the relationship between peptide sequence context and fragmentation behavior
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