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Motivation Extracting useful feature set which contains significant discriminatory information is a critical step in effectively presenting sequence data to predict structural, functional, interaction and expression of proteins, DNAs and RNAs. Also, being able to filter features with significant information and avoid sparsity in the extracted features require the employment of efficient feature selection techniques. Here we present PyFeat as a practical and easy to use toolkit implemented in Python for extracting various features from proteins, DNAs and RNAs. To build PyFeat we mainly focused on extracting features that capture information about the interaction of neighboring residues to be able to provide more local information. We then employ AdaBoost technique to select features with maximum discriminatory information. In this way, we can significantly reduce the number of extracted features and enable PyFeat to represent the combination of effective features from large neighboring residues. As a result, PyFeat is able to extract features from 13 different techniques and represent context free combination of effective features. The source code for PyFeat standalone toolkit and employed benchmarks with a comprehensive user manual explaining its system and workflow in a step by step manner are publicly available. Results https://github.com/mrzResearchArena/PyFeat/blob/master/RESULTS.md. Availability and implementation Toolkit, source code and manual to use PyFeat: https://github.com/mrzResearchArena/PyFeat/ Supplementary information Supplementary data are available at Bioinformatics online.

Prediction of new drug-target interactions is critically important as it can lead the researchers to find new uses for old drugs and to disclose their therapeutic profiles or side effects. However, experimental prediction of drug-target interactions is expensive and time-consuming. As a result, computational methods for predictioning new drug-target interactions have gained a tremendous interest in recent times. Here we present iDTI-ESBoost, a prediction model for identification of drug-target interactions using evolutionary and structural features. Our proposed method uses a novel data balancing and boosting technique to predict drug-target interaction. On four benchmark datasets taken from a gold standard data, iDTI-ESBoost outperforms the state-of-the-art methods in terms of area under receiver operating characteristic (auROC) curve. iDTI-ESBoost also outperforms the latest and the best-performing method found in the literature in terms of area under precision recall (auPR) curve. This is significant as auPR curves are argued as suitable metric for comparison for imbalanced datasets similar to the one studied here. Our reported results show the effectiveness of the classifier, balancing methods and the novel features incorporated in iDTI-ESBoost. iDTI-ESBoost is a novel prediction method that has for the first time exploited the structural features along with the evolutionary features to predict drug-protein interactions. We believe the excellent performance of iDTI-ESBoost both in terms of auROC and auPR would motivate the researchers and practitioners to use it to predict drug-target interactions. To facilitate that, iDTI-ESBoost is implemented and made publicly available at: http://farshidrayhan.pythonanywhere.com/iDTI-ESBoost/.

Intrusion is the violation of information security policy by malicious activities. Intrusion detection (ID) is a series of actions for detecting and recognising suspicious actions that make the expedient acceptance of standards of confidentiality, quality, consistency, and availability of a computer based network system. In this paper, we present a new approach consists with merging of feature selection and classification for multiple class NSL-KDD cup 99 intrusion detection dataset employing support vector machine (SVM). The objective is to improve the competence of intrusion classification with a significantly reduced set of input features from the training data. In supervised learning, feature selection is the process of selecting the important input training features and removing the irrelevant input training features, with the objective of obtaining a feature subset that produces higher classification accuracy. In the experiment, we have applied SVM classifier on several input feature subsets of training dataset of NSL-KDD cup 99 dataset. The experimental results obtained showed the proposed method successfully bring 91% classification accuracy using only three features and 99% classification accuracy using 36 features, while all 41 training features achieved 99% classification accuracy.