ToxinMI

[1]  Xiucai Ye,et al.  ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning , 2022, Bioinform..

[2]  Leyi Wei,et al.  ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism , 2021, Briefings Bioinform..

[3]  Hong-Bin Shen,et al.  ToxDL: deep learning using primary structure and domain embeddings for assessing protein toxicity , 2020, Bioinform..

[4]  T Jeffrey Cole,et al.  TOXIFY: a deep learning approach to classify animal venom proteins , 2019, PeerJ.

[5]  Jiangning Song,et al.  ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides , 2018, Bioinform..

[6]  Menglong Li,et al.  A kernel matrix dimension reduction method for predicting drug-target interaction , 2017 .

[7]  S Rackovsky,et al.  Sequence-, structure-, and dynamics-based comparisons of structurally homologous CheY-like proteins , 2017, Proceedings of the National Academy of Sciences.

[8]  David J. Barlow,et al.  Machine learning can differentiate venom toxins from other proteins having non-toxic physiological functions , 2016, PeerJ Comput. Sci..

[9]  Rahul Kumar,et al.  In Silico Approach for Predicting Toxicity of Peptides and Proteins , 2013, PloS one.

[10]  Xiaolong Wang,et al.  Sequence analysis Application of latent semantic analysis to protein remote homology detection , 2006 .

[11]  Chris H. Q. Ding,et al.  Multi-class protein fold recognition using support vector machines and neural networks , 2001, Bioinform..

[12]  M. Georgiopoulos,et al.  Feed-forward neural networks , 1994, IEEE Potentials.