AIMC Topic: Mutation

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Deep profiling of gene expression across 18 human cancers.

Nature biomedical engineering Dec 17, 2024
Clinical and biological information in large datasets of gene expression across cancers could be tapped with unsupervised deep learning. However, difficulties associated with biological interpretability and methodological robustness have made this im...
Mutation Deep Learning Gene Expression Regulation, Neoplastic Unsupervised Machine Learning Gene Expression Profiling Transcriptome Humans Neoplasms
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Increased chloroplast occupancy in bundle sheath cells of rice hap3H mutants revealed by Chloro-Count: a new deep learning-based tool.

The New phytologist Dec 12, 2024
There is an increasing demand to boost photosynthesis in rice to increase yield potential. Chloroplasts are the site of photosynthesis, and increasing their number and size is a potential route to elevate photosynthetic activity. Notably, bundle shea...
Oryza Plant Proteins Chloroplasts Photosynthesis Deep Learning Plant Leaves Mutation
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Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics.

Nature cancer Dec 11, 2024
Large-scale omics profiling has uncovered a vast array of somatic mutations and cancer-associated proteins, posing substantial challenges for their functional interpretation. Here we present a network-based approach centered on FunMap, a pan-cancer f...
Neural Networks, Computer Machine Learning Humans Proteomics Neoplasms Proteogenomics Mutation Protein Interaction Maps Gene Regulatory Networks
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Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study.

The Lancet. Oncology Dec 6, 2024
BACKGROUND: Accurate detection of driver gene mutations is crucial for treatment planning and predicting prognosis for patients with lung cancer. Conventional genomic testing requires high-quality tissue samples and is time-consuming and resource-con...
Mutation High-Throughput Nucleotide Sequencing Deep Learning China Lung Neoplasms Prognosis Retrospective Studies Female Male Middle Aged Aged Biomarkers, Tumor Humans
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Machine Learning Guided Rational Design of a Non-Heme Iron-Based Lysine Dioxygenase Improves its Total Turnover Number.

Chembiochem : a European journal of chemical biology Dec 5, 2024
Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolutio...
Molecular Dynamics Simulation Lysine Protein Engineering Dioxygenases Machine Learning Iron Mutation
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Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

Cell communication and signaling : CCS Dec 5, 2024
BACKGROUND: Epidermal growth factor receptor (EGFR) T790M mutation often occurs during long durational erlotinib treatment of non-small cell lung cancer (NSCLC) patients, leading to drug resistance and disease progression. Identification of new selec...
Apoptosis Mutation Xenograft Model Antitumor Assays ErbB Receptors Signal Transduction Carcinoma, Non-Small-Cell Lung Mice, Nude Protein Kinase Inhibitors Oleanolic Acid Machine Learning Animals Antineoplastic Agents Mice Cell Proliferation Cell Line, Tumor Lung Neoplasms Humans
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Machine learning based on multiplatform tests assists in subtype classification of mature B-cell neoplasms.

British journal of haematology Dec 3, 2024
Mature B-cell neoplasms (MBNs) are clonal proliferative diseases encompassing over 40 subtypes. The WHO classification (morphology, immunology, cytogenetics and molecular biology) provides comprehensive diagnostic understandings. However, MBN subtypi...
Machine Learning Retrospective Studies Leukemia, Lymphocytic, Chronic, B-Cell East Asian People Humans Lymphoma, Follicular CD5 Antigens Lymphoma, Mantle-Cell High-Throughput Nucleotide Sequencing Leukemia, Hairy Cell Immunohistochemistry Waldenstrom Macroglobulinemia Lymphoma, B-Cell Neprilysin Mutation Flow Cytometry Bone Marrow
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MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data.

Genome biology Dec 2, 2024
We present MoCHI, a tool to fit interpretable models using deep mutational scanning data. MoCHI infers free energy changes, as well as interaction terms (energetic couplings) for specified biophysical models, including from multimodal phenotypic data...
Mutation Thermodynamics Epistasis, Genetic Allosteric Regulation Machine Learning Humans Software Neural Networks, Computer Models, Genetic
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Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.

Frontiers in immunology Nov 28, 2024
BACKGROUND: Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.
Gene Expression Regulation, Neoplastic Mutation Adenocarcinoma of Lung Male Prognosis Genomics Immunotherapy Gene Expression Profiling Female Humans Computational Biology Multiomics Lung Neoplasms Biomarkers, Tumor Machine Learning Precision Medicine
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GFPrint™: A machine learning tool for transforming genetic data into clinical insights.

PloS one Nov 27, 2024
The increasing availability of massive genetic sequencing data in the clinical setting has triggered the need for appropriate tools to help fully exploit the wealth of information these data possess. GFPrint™ is a proprietary streaming algorithm desi...
Humans Neoplasms Machine Learning Software Databases, Genetic Algorithms Mutation
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