Toxicity Prediction in Peptides and Proteins using Random forest,Decision Tree and Logistic Regression

In the toxicology field, it remains a major challenge to predict and understand toxicity of peptides and proteins.Toxicity prediction is critical for reducing the cost and labour of preclinical and clinical studies for a medicine.So far, peptide/protein-based treatments have been developed to treat a variety of diseases. In our research work, we review machine learning approaches which have been employed for toxicity prediction of proteins and peptides, including random forests, decision tree, and logistic regression.We developed model including Amino Acid Composition,Di-peptide composition,binary profile of patterns and motif identification and predicted toxicity of proteins and peptides using machine learning methods.