ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides.

Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combining discriminative features from different information sources in an interactive way. Our model extracts sequence, physicochemical, and evolutionary based features for ACP identification using different numerical peptide representations while restraining parameter overhead. It is evident through rigorous experiments using cross-validation and independent-dataset that ACP-MHCNN outperforms other models for anticancer peptide identification by a substantial margin on our employed benchmarks. ACP-MHCNN outperforms state-of-the-art model by 6.3%, 8.6%, 3.7%, 4.0%, and 0.20 in terms of accuracy, sensitivity, specificity, precision, and MCC respectively. ACP-MHCNN and its relevant codes and datasets are publicly available at: https://github.com/mrzResearchArena/Anticancer-Peptides-CNN . ACP-MHCNN is also publicly available as an online predictor at: https://anticancer.pythonanywhere.com/ .

SubFeat: Feature subspacing ensemble classifier for function prediction of DNA, RNA and protein sequences.

The information of a cell is primarily contained in deoxyribonucleic acid (DNA). There is a flow of DNA information to protein sequences via ribonucleic acids (RNA) through transcription and translation. These entities are vital for the genetic process. Recent epigenetics developments also show the importance of the genetic material and knowledge of their attributes and functions. However, the growth in these entities' available features or functionalities is still slow due to the time-consuming and expensive in vitro experimental methods. In this paper, we have proposed an ensemble classification algorithm called SubFeat to predict biological entities' functionalities from different types of datasets. Our model uses a feature subspace-based novel ensemble method. It divides the feature space into sub-spaces, which are then passed to learn individual classifier models. The ensemble is built on these base classifiers that use a weighted majority voting mechanism. SubFeat tested on four datasets comprising two DNA, one RNA, and one protein dataset, and it outperformed all the existing single classifiers and the ensemble classifiers. SubFeat is made available as a Python-based tool. We have made the package SubFeat available online along with a user manual. It is freely accessible from here: https://github.com/fazlulhaquejony/SubFeat.

Effective DNA binding protein prediction by using key features via Chou's general PseAAC.

DNA-binding proteins (DBPs) are responsible for several cellular functions, starting from our immunity system to the transport of oxygen. In the recent studies, scientists have used supervised machine learning based methods that use information from the protein sequence only to classify the DBPs. Most of the methods work effectively on the train sets but performance of most of them degrades in the independent test set. It shows a room for improving the prediction method by reducing over-fitting. In this paper, we have extracted several features solely using the protein sequence and carried out two different types of feature selection on them. Our results have proven comparable on training set and significantly improved on the independent test set. On the independent test set our accuracy was 82.26% which is 1.62% improved compared to the previous best state-of-the-art methods. Performance in terms of sensitivity and area under receiver operating characteristic curve for the independent test set was also higher and they were 0.95 and 0.823 respectively.