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BIPSPI+: Mining Type-Specific Datasets of Protein Complexes to Improve Protein Binding Site Prediction.

Journal of molecular biology
Computational approaches for predicting protein-protein interfaces are extremely useful for understanding and modelling the quaternary structure of protein assemblies. In particular, partner-specific binding site prediction methods allow delineating ...

EvoRator: Prediction of Residue-level Evolutionary Rates from Protein Structures Using Machine Learning.

Journal of molecular biology
Measuring evolutionary rates at the residue level is indispensable for gaining structural and functional insights into proteins. State-of-the-art tools for estimating rates take as input a large set of homologous proteins, a probabilistic model of ev...

MLCPP 2.0: An Updated Cell-penetrating Peptides and Their Uptake Efficiency Predictor.

Journal of molecular biology
Cell-penetrating peptides (CPPs) translocate into the cell as various biologically active conjugates and possess numerous biomedical applications. Several machine learning-based predictors have been proposed in the past, but they mostly focus on iden...

MarkerML - Marker Feature Identification in Metagenomic Datasets Using Interpretable Machine Learning.

Journal of molecular biology
Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis te...

Screening membraneless organelle participants with machine-learning models that integrate multimodal features.

Proceedings of the National Academy of Sciences of the United States of America
Protein self-assembly is one of the formation mechanisms of biomolecular condensates. However, most phase-separating systems (PS) demand multiple partners in biological conditions. In this study, we divided PS proteins into two groups according to th...

ScanNet: A Web Server for Structure-based Prediction of Protein Binding Sites with Geometric Deep Learning.

Journal of molecular biology
Predicting the various binding sites of a protein from its structure sheds light on its function and paves the way towards design of interaction inhibitors. Here, we report ScanNet, a freely available web server for prediction of protein-protein, pro...

Prevalence of depressive symptoms and associated factors during the COVID-19 pandemic: A national-based study.

Journal of affective disorders
BACKGROUND: Previous studies have reported that the prevalence of depression and depressive symptoms was significantly higher than that before the COVID-19 pandemic. This study aimed to explore the prevalence of depressive symptoms and evaluate the i...

Using machine learning algorithms and techniques for defining the impact of affective temperament types, content search and activities on the internet on the development of problematic internet use in adolescents' population.

Frontiers in public health
BACKGROUND: By using algorithms and Machine Learning - ML techniques, the aim of this research was to determine the impact of the following factors on the development of Problematic Internet Use (PIU): sociodemographic factors, the intensity of using...

SLKB: synthetic lethality knowledge base.

Nucleic acids research
Emerging CRISPR-Cas9 technology permits synthetic lethality (SL) screening of large number of gene pairs from gene combination double knockout (CDKO) experiments. However, the poor integration and annotation of CDKO SL data in current SL databases li...

HervD Atlas: a curated knowledgebase of associations between human endogenous retroviruses and diseases.

Nucleic acids research
Human endogenous retroviruses (HERVs), as remnants of ancient exogenous retrovirus infected and integrated into germ cells, comprise ∼8% of the human genome. These HERVs have been implicated in numerous diseases, and extensive research has been condu...