AIMC Topic: Membrane Proteins
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Computational design of soluble and functional membrane protein analogues.
Nature
Jun 19, 2024
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...
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
May 12, 2024
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 ...
Exploiting protein language models for the precise classification of ion channels and ion transporters.
Proteins
Apr 24, 2024
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
Apr 23, 2024
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
Apr 12, 2024
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...
Computational drug development for membrane protein targets.
Nature biotechnology
Feb 15, 2024
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 ...
Integrated Bioinformatics and Validation Reveal and Its Related Molecules as Potential Identifying Genes in Liver Cirrhosis.
Biomolecules
Dec 21, 2023
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...
Prediction of interactions between cell surface proteins by machine learning.
Proteins
Dec 5, 2023
Cells detect changes in their external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions betwee...
Identifying Explainable Machine Learning Models and a Novel SFRP2 Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer.
International journal of molecular sciences
Nov 29, 2023
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...
MPA-Pred: A machine learning approach for predicting the binding affinity of membrane protein-protein complexes.
Proteins
Nov 10, 2023
Membrane protein-protein interactions are essential for several functions including cell signaling, ion transport, and enzymatic activity. These interactions are mainly dictated by their binding affinities. Although several methods are available for ...
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