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Membrane Proteins

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Integrated Bioinformatics and Validation Reveal and Its Related Molecules as Potential Identifying Genes in Liver Cirrhosis.

Biomolecules
Liver cirrhosis remains a significant global public health concern, with liver transplantation standing as the foremost effective treatment currently available. Therefore, investigating the pathogenesis of liver cirrhosis and developing novel therapi...

Identifying Explainable Machine Learning Models and a Novel SFRP2 Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer.

International journal of molecular sciences
Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management st...

Identification of membrane protein types via deep residual hypergraph neural network.

Mathematical biosciences and engineering : MBE
A membrane protein's functions are significantly associated with its type, so it is crucial to identify the types of membrane proteins. Conventional computational methods for identifying the species of membrane proteins tend to ignore two issues: Hig...

Computational drug development for membrane protein targets.

Nature biotechnology
The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine ...

Exploiting protein language models for the precise classification of ion channels and ion transporters.

Proteins
This study introduces TooT-PLM-ionCT, a comprehensive framework that consolidates three distinct systems, each meticulously tailored for one of the following tasks: distinguishing ion channels (ICs) from membrane proteins (MPs), segregating ion trans...

Integrated multi-omics analysis and machine learning developed a prognostic model based on mitochondrial function in a large multicenter cohort for Gastric Cancer.

Journal of translational medicine
BACKGROUND: Gastric cancer (GC) is a common and aggressive type of cancer worldwide. Despite recent advancements in its treatment, the prognosis for patients with GC remains poor. Understanding the mechanisms of cell death in GC, particularly those r...

Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making.

Histopathology
The histopathological classification of melanocytic tumours with spitzoid features remains a challenging task. We confront the complexities involved in the histological classification of these tumours by proposing machine learning (ML) algorithms tha...

Deep neural network for the prediction of KRAS, NRAS, and BRAF genotypes in left-sided colorectal cancer based on histopathologic images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: The KRAS, NRAS, and BRAF genotypes are critical for selecting targeted therapies for patients with metastatic colorectal cancer (mCRC). Here, we aimed to develop a deep learning model that utilizes pathologic whole-slide images (WSIs) to ...

Machine Learning Derived Collective Variables for the Study of Protein Homodimerization in Membrane.

Journal of chemical theory and computation
The accurate calculation of equilibrium constants for protein-protein association is of fundamental importance to quantitative biology and remains an outstanding challenge for computational biophysics. Traditionally, equilibrium constants have been c...

Computational design of soluble and functional membrane protein analogues.

Nature
De novo design of complex protein folds using solely computational means remains a substantial challenge. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topolog...