Additional file 4 of seqQscorer: automated quality control of next-generation sequencing data using machine learning
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https://figshare.com/articles/dataset/Additional_file_4_of_seqQscorer_automated_quality_control_of_next-generation_sequencing_data_using_machine_learning/14173809
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Additional file 4. Table of optimal models used by seqQscorer. Optimal models are the best models for each data subset (Dataset column). Dataset: species-subset-layout, generic: all data. Feature sets: RAW (raw data), MAP (genome mapping), LOC (genomic localization), TSS (transcription start sites profile). Feature Selection: method-percentage (percentage of retained features), chi-square (chi2), recursive feature elimination (RFE). Algorithm Parameters: relevant to a scikit-learn implementation.
创建时间：
2021-03-05
