With advancements in genomics, there has been substantial reduction in the cost and time of genome sequencing and has resulted in lot of data in genome databases. Antimicrobial host defense proteins provide protection against invading microbes. But c...
In this paper, for accurate prediction of protein-protein interaction (PPI), a novel hybrid classifier is developed by combining the functional-link Siamese neural network (FSNN) with the light gradient boosting machine (LGBM) classifier. The hybrid ...
Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive adva...
Enhancers are deoxyribonucleic acid (DNA) fragments which when bound by transcription factors enhance the transcription of related genes. Due to its sporadic distribution and similar fractions, identification of enhancers from the human genome seems ...
Accurate variant effect prediction has broad impacts on protein engineering. Recent machine learning approaches toward this end are based on representation learning, by which feature vectors are learned and generated from unlabeled sequences. However...
The superior performance of machine-learning scoring functions for docking has caused a series of debates on whether it is due to learning knowledge from training data that are similar in some sense to the test data. With a systematically revised met...
Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date...
The prediction of peptide secondary structures is fundamentally important to reveal the functional mechanisms of peptides with potential applications as therapeutic molecules. In this study, we propose a multi-view deep learning method named Peptide ...
As the best substitute for antibiotics, antimicrobial peptides (AMPs) have important research significance. Due to the high cost and difficulty of experimental methods for identifying AMPs, more and more researches are focused on using computational ...
Full-quantum mechanics (QM) calculations are extraordinarily precise but difficult to apply to large systems, such as biomolecules. Motivated by the massive demand for efficient calculations for large systems at the full-QM level and by the significa...
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