Identification of anticancer peptides based on Random Relevance Vector Machines Cancer is the most threat to humanu0027s health and life. At present, people have developed several ways to against cancer, such as surgical operation, radiotherapy and chemotherapy. However, cancers still cause highly mortality rate. A main part of the reason is that the traditional methods bring treatment effect as well as the negative effect. Recently, the anticancer peptides (ACPs) have been discovered which can be a new way to treat cancer. Since ACPs are natural biologics, they are safer than other methods. However, the experimental technology is an expensive way to find ACPs so we purpose a new machine learning method to identify the ACPs which named Random Relevance Vector Machines (RRVMs). The cross validations experiments show the high accuracy and stability of this new method.

A Deep Neural Network for Antimicrobial Peptide Recognition With the widespread use of antibiotics, many bacteria have developed resistance. Antimicrobial peptides have broad applications in medicine because of their high antibacterial activity. In this paper, a neural network model is introduced to recognize and detect antimicrobial peptides. Our model consists of an embedded, convolutional, bidirectional LSTM, and full connection layers. The embedded layer is used to code different amino acid residues into different vectors. The convolutional layer and bidirectional LSTM extract peptide amino acid residue sequence information. The full connection layer maps the sequence information linearly to the interval from 0 to 1, as the peptide for the probability of antimicrobial peptides. Training and testing on several different datasets reveal that our model performs better than other proposed models.