AIMC Topic: Mutation

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Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2.

Nature biomedical engineering Mar 5, 2025
Most antibodies for treating COVID-19 rely on binding the receptor-binding domain (RBD) of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). However, Omicron and its sub-lineages, as well as other heavily mutated variants, have rendered m...
Humans COVID-19 SARS-CoV-2 Mutation Epitopes Antibodies, Neutralizing COVID-19 Drug Treatment Deep Learning Antibodies, Viral Spike Glycoprotein, Coronavirus Evolution, Molecular Angiotensin-Converting Enzyme 2
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Discovery of mutations predictive of survival benefit from immunotherapy in first-line NSCLC: A retrospective machine learning study of IMpower150 liquid biopsy data.

Computers in biology and medicine Mar 4, 2025
Predictive biomarker identification in cancer treatment has traditionally relied on pre-defined analyses, limiting discoveries to expected biomarkers and potentially overlooking novel ones predictive of therapy response. In this work, we develop a no...
Paclitaxel Liquid Biopsy Clinical Trials, Phase III as Topic Bevacizumab Male Middle Aged Lung Neoplasms Immunotherapy Retrospective Studies Antineoplastic Combined Chemotherapy Protocols Humans Female Carcinoma, Non-Small-Cell Lung Aged Machine Learning Randomized Controlled Trials as Topic Biomarkers, Tumor Mutation
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Machine learning identifies clinical tumor mutation landscape pathways of resistance to checkpoint inhibitor therapy in NSCLC.

Journal for immunotherapy of cancer Mar 3, 2025
BACKGROUND: Immune checkpoint inhibitors (CPIs) have revolutionized cancer therapy for several tumor indications. However, a substantial fraction of patients treated with CPIs derive no benefit or have short-lived responses to CPI therapy. Identifyin...
Humans Machine Learning Female Aged Lung Neoplasms Biomarkers, Tumor Carcinoma, Non-Small-Cell Lung Mutation Male Drug Resistance, Neoplasm Middle Aged Immune Checkpoint Inhibitors
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Performance of Machine Learning Models in Predicting BRAF Alterations Using Imaging Data in Low-Grade Glioma: A Systematic Review and Meta-Analysis.

World neurosurgery Feb 25, 2025
BACKGROUND: Understanding the BRAF alterations preoperatively could remarkably assist in predicting tumor behavior, which leads to a more precise prognostication and management strategy. Recent advances in artificial intelligence (AI) have resulted i...
Proto-Oncogene Proteins B-raf Glioma Neoplasm Grading Mutation Humans Machine Learning Brain Neoplasms
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Identification of a gene signature and prediction of overall survival of patients with stage IV colorectal cancer using a novel machine learning approach.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology Feb 19, 2025
OBJECTIVE: We sought to characterize unique gene signature patterns associated with worse overall survival (OS) among patients with stage IV colorectal cancer (CRC) using a machine learning (ML) approach.
Male Female Humans Middle Aged Aged Registries Machine Learning Neoplasm Staging Survival Rate Prognosis Mutation Colorectal Neoplasms
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Deep learning-driven semi-rational design in phenylalanine ammonia-lyase for enhanced catalytic efficiency.

International journal of biological macromolecules Feb 19, 2025
Phenylalanine ammonia-lyase (PAL) possesses significant potential in agriculture, industry, and the treatment of various diseases, including cancer. In particular, PAL derived from Anabaena variabilis (AvPAL) has been successfully utilized in clinica...
Kinetics Deep Learning Protein Engineering Catalysis Molecular Dynamics Simulation Phenylalanine Ammonia-Lyase Mutation Biocatalysis Anabaena variabilis
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Breaking barriers: noninvasive AI model for BRAF mutation identification.

International journal of computer assisted radiology and surgery Feb 15, 2025
OBJECTIVE: BRAF is the most common mutation found in thyroid cancer and is particularly associated with papillary thyroid carcinoma (PTC). Currently, genetic mutation detection relies on invasive procedures. This study aimed to extract radiomic featu...
Thyroid Cancer, Papillary Proto-Oncogene Proteins B-raf Thyroid Neoplasms Ultrasonography Mutation Humans Artificial Intelligence Machine Learning Female Sensitivity and Specificity Adult Male Middle Aged Deep Learning
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A deep-learning model for predicting tyrosine kinase inhibitor response from histology in gastrointestinal stromal tumor.

The Journal of pathology Feb 14, 2025
Over 90% of gastrointestinal stromal tumors (GISTs) harbor mutations in KIT or PDGFRA that can predict response to tyrosine kinase inhibitor (TKI) therapies, as recommended by NCCN (National Comprehensive Cancer Network) guidelines. However, gene seq...
Aged Male Middle Aged Deep Learning Treatment Outcome Mutation Gastrointestinal Neoplasms Gastrointestinal Stromal Tumors Humans Female Triazines Imatinib Mesylate Proto-Oncogene Proteins c-kit Pyrazoles Tyrosine Kinase Inhibitors Protein Kinase Inhibitors Receptor, Platelet-Derived Growth Factor alpha Pyrroles
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Functionally characterizing obesity-susceptibility genes using CRISPR/Cas9, in vivo imaging and deep learning.

Scientific reports Feb 13, 2025
Hundreds of loci have been robustly associated with obesity-related traits, but functional characterization of candidate genes remains a bottleneck. Aiming to systematically characterize candidate genes for a role in accumulation of lipids in adipocy...
Zebrafish Zebrafish Proteins Adipocytes Animals Deep Learning Larva Genetic Predisposition to Disease Obesity CRISPR-Cas Systems Mutation Lipid Metabolism
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Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.

Science advances Feb 12, 2025
Machine learning (ML) is changing the world of computational protein design, with data-driven methods surpassing biophysical-based methods in experimental success. However, they are most often reported as case studies, lack integration and standardiz...
Proteins Models, Molecular Mutation Supervised Machine Learning Protein Engineering Machine Learning Software Algorithms Computational Biology
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