PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine | Zendy

Reda Rawi | Zendy; Raghvendra Mall | Zendy; Khalid Kunji | Zendy; Chen-Hsiang Shen | Zendy; Peter D. Kwong | Zendy; Gwo-Yu Chuang | Zendy

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PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine

Author(s) -

Reda Rawi,

Raghvendra Mall,

Khalid Kunji,

Chen-Hsiang Shen,

Peter D. Kwong,

Gwo-Yu Chuang

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx662

Subject(s) - solubility , computer science , gradient boosting , in silico , boosting (machine learning) , protein sequencing , sequence (biology) , correlation coefficient , artificial intelligence , machine learning , chemistry , data mining , peptide sequence , biochemistry , organic chemistry , random forest , gene

Protein solubility can be a decisive factor in both research and production efficiency, and in silico sequence-based predictors that can accurately estimate solubility outcomes are highly sought.

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