AIMC Topic: Mutation

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Transfer learning with multiomics integration and deep neural networks reveals drug resistance mechanisms in cancer.

Scientific reports
Drug resistance remains one of the primary challenges in effective cancer therapy. In this study, we employed a deep neural network (DNN)-based transfer learning (TL) approach to predict drug response and uncover drug resistance mechanisms. We integr...

Enabling whole genome sequencing analysis from FFPE specimens in clinical oncology.

Nature communications
The adoption of whole genome sequencing (WGS) in clinical oncology is challenged by low data quality and increased artifacts in standard-of-care formalin-fixed paraffin-embedded (FFPE) samples. Analysis of 56 fresh frozen (FF) and FFPE matched pairs ...

Mechanistic Disruption of the TREM2-DAP12 Transmembrane Complex by Alzheimer's Disease Mutations: A Multiscale Simulation Study.

Journal of chemical information and modeling
Triggering receptor expressed on myeloid cell 2 (TREM2) is an immunomodulatory receptor that plays a critical role in microglial activation through its association with the adaptor protein DNAX-activation protein 12 (DAP12). Variants in TREM2 have be...

The relationship between amyloid-β peptide spectrum and the spastic paraparesis phenotype in autosomal dominant Alzheimer's disease.

Alzheimer's research & therapy
BACKGROUND: More than 300 mutations in presenilin 1 (PSEN1) lead to autosomal dominant Alzheimer's disease (ADAD). PSEN1, as the catalytic subunit of γ-secretase, generates amyloid-β (Aβ) peptides through a sequential proteolysis of the amyloid precu...

AbAgym: a well-curated dataset for the mutational analysis of antibody-antigen complexes.

mAbs
With monoclonal antibodies becoming one of the largest classes of biopharmaceuticals, it is important to have curated data to train computational models that can accelerate their design. Despite the massive amount of mutagenesis data generated on ant...

Regulators of homologous recombination deficiency identified by machine learning using somatic multi-omics data.

Life science alliance
Homologous recombination deficiency (HRD) is a critical biomarker for guiding targeted therapies, yet the full range of somatic alterations driving HRD across cancers remains incompletely characterized. Here, we present a tumor-agnostic machine learn...

A multi-representation deep-learning framework for accurate multicancer classification.

Journal of translational medicine
BACKGROUND: Accurate multicancer classification constitutes a cornerstone of modern oncology, offering critical insights into diagnosis, therapeutic decision-making, and prognostication. Numerous existing approaches, however, remain restricted to lim...

Novel heterozygous mutation in KMT2B causing an unusual phenotypic presentation: a comprehensive clinical and bioinformatic analysis.

Molecular biology reports
BACKGROUND: KMT2B-related dystonia is a childhood-onset movement disorder. This study investigated a novel KMT2B gene variant using whole exome sequencing (WES) and bioinformatics analysis, and expanded the known clinical spectrum of KMT2B-related dy...

Kideraspa: designing variants of staphylococcal protein a based on a diffusion model with kidera factors.

Journal of computer-aided molecular design
The interaction between staphylococcal protein A (SpA) and human immunoglobulin G (IgG) is pivotal in treating diseases such as cancer, inflammation, infections, and autoimmune disorders. However, acquiring natural SpA variants is labor-intensive, tr...

Categorical and phenotypic image synthetic learning as an alternative to federated learning.

Nature communications
Multi-center collaborations are crucial in developing robust and generalizable machine learning models in medical imaging. Traditional methods, such as centralized data sharing or federated learning (FL), face challenges, including privacy issues, co...