ToxDL: deep learning using primary structure and domain embeddings for assessing protein toxicity | Zendy

Xiaoyong Pan | Zendy; Jasper Zuallaert | Zendy; Xi Wang | Zendy; HongBin Shen | Zendy; Elda Posada Campos | Zendy; Denys Marushchak | Zendy; Wesley De Neve | Zendy

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ToxDL: deep learning using primary structure and domain embeddings for assessing protein toxicity

Author(s) -

Xiaoyong Pan,

Jasper Zuallaert,

Xi Wang,

HongBin Shen,

Elda Posada Campos,

Denys Marushchak,

Wesley De Neve

Publication year - 2020

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/btaa656

Subject(s) - domain (mathematical analysis) , primary (astronomy) , computer science , toxicity , deep learning , artificial intelligence , machine learning , computational biology , chemistry , biology , mathematics , physics , mathematical analysis , organic chemistry , astronomy

Genetically engineering food crops involves introducing proteins from other species into crop plant species or modifying already existing proteins with gene editing techniques. In addition, newly synthesized proteins can be used as therapeutic protein drugs against diseases. For both research and safety regulation purposes, being able to assess the potential toxicity of newly introduced/synthesized proteins is of high importance.

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