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Membrane Proteins

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Prediction Enhancement of Residue Real-Value Relative Accessible Surface Area in Transmembrane Helical Proteins by Solving the Output Preference Problem of Machine Learning-Based Predictors.

Journal of chemical information and modeling
The α-helical transmembrane proteins constitute 25% of the entire human proteome space and are difficult targets in high-resolution wet-lab structural studies, calling for accurate computational predictors. We present a novel sequence-based method ca...

Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets.

IEEE/ACM transactions on computational biology and bioinformatics
Membrane proteins play important roles in various biological processes within organisms. Predicting the functional types of membrane proteins is indispensable to the characterization of membrane proteins. Recent studies have extended to predicting si...

Cheminformatics Based Machine Learning Models for AMA1-RON2 Abrogators for Inhibiting Plasmodium falciparum Erythrocyte Invasion.

Molecular informatics
Malaria remains a dreadful disease by putting every year about 3.4 billion people at risk and resulting into mortality of 627 thousand people worldwide. Existing therapies based upon Quinines and Artemisinin-based combination therapies have started s...

A Machine Learning Approach to Explain Drug Selectivity to Soluble and Membrane Protein Targets.

Molecular informatics
Improved understanding of the forces that determine drug specificity to their targets is important for drug design and discovery, as well as for gaining knowledge about molecular recognition. Here, we present a machine learning approach that includes...

Boi-Ogi-To, a Traditional Japanese Kampo Medicine, Promotes Cellular Excretion of Chloride and Water by Activating Volume-Sensitive Outwardly Rectifying Anion Channels.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
The Japanese Kampo medicine Boi-ogi-to (BOT) is known as an effective therapeutic agent for edema and nephrosis by promoting the excretion of excess body fluids. Despite its empirical effectiveness, scientific evidence supporting its effectiveness re...

Exploring genetic and immune cell dynamics in systemic lupus erythematosus patients with Epstein-Barr virus infection via machine learning.

Rheumatology (Oxford, England)
OBJECTIVES: EBV is a widespread virus implicated in various diseases, including SLE. However, the specific genes and pathways altered in SLE patients with EBV infection remain unclear. We aimed to identify key genes and immune cells in SLE patients w...

Machine Learning Reveals Aneuploidy Characteristics in Cancers: The Impact of BEX4.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Aneuploidy is crucial yet under-explored in cancer pathogenesis. Specifically, the involvement of brain expressed X-linked gene 4 () in microtubule formation has been identified as a potential aneuploidy mechanism. Nevertheless, 's compre...

A transformer-based deep learning survival prediction model and an explainable XGBoost anti-PD-1/PD-L1 outcome prediction model based on the cGAS-STING-centered pathways in hepatocellular carcinoma.

Briefings in bioinformatics
Recent studies suggest cGAS-STING pathway may play a crucial role in the genesis and development of hepatocellular carcinoma (HCC), closely associated with classical pathways and tumor immunity. We aimed to develop models predicting survival and anti...

Machine Learning Diagnostic Model for Hepatocellular Carcinoma Based on Liquid-Liquid Phase Separation and Ferroptosis-Related Genes.

The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology
BACKGROUND/AIMS: Hepatocellular carcinoma (HCC) represents a primary liver malignancy with a multifaceted molecular landscape. The interplay between liquid-liquid phase separation (LLPS) and ferroptosis-a regulated form of cell death-has garnered int...