AIMC Topic: Genomics

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Radiogenomics: transforming lung cancer care through non-invasive imaging and genomic integration.

Medical oncology (Northwood, London, England)
Radiogenomics links quantitative features from routine CT and PET/CT with tumor genomics to non-invasively profile non-small cell lung cancer (NSCLC). This review synthesizes the current workflow-from image acquisition and segmentation to feature ext...

Obscured-ensemble models for genomic prediction.

PloS one
Genomic Prediction (GP) uses dense whole-genome marker sets from lines of a crop to predict agronomic traits for untested genotypes. In recent years, deep learning (DL) approaches for genomic prediction have demonstrated state-of-the-art results. How...

Genetic and Genomic Testing in Cardiovascular Disease: A Policy Statement From the American Heart Association.

Circulation
The rapid advancement of genomic and precision medicine has expanded the role of genetics and genomics in the diagnosis, risk stratification, and management of cardiovascular diseases. With the decreasing cost and increasing accessibility of genetic ...

Recent Advances in Integrating Machine Learning with Omics Approaches in Food Science and Nutrition Research.

Journal of agricultural and food chemistry
Omics technologies are revolutionizing food and nutrition research by enabling high-throughput analysis of food components and microorganisms and revealing the intricate relationships between food and human health. Machine learning (ML) methods are p...

Comparative evaluation of SNP-weighted, Bayesian, and machine learning models for genomic prediction in Holstein cattle.

BMC genomics
BACKGROUND: Genomic Best Linear Unbiased Prediction (GBLUP) assumes that all SNPs contribute equally to genetic variance, including those with minimal impact, limiting its accuracy. A major challenge in animal breeding is to develop more scientific m...

OmniCLIC: A Unified Omics Contrastive Learning Framework for Effective Integration and Classification of Multiomics Data.

Journal of chemical information and modeling
Integrating multiomics data for cancer subtype classification remains a critical yet challenging task due to the high dimensionality, heterogeneity, and limited interpretability of omics features. To address these limitations, we propose OmniCLIC, a ...

Beyond single biomarkers: multi-omics strategies to predict immunotherapy outcomes in blood cancers.

Clinical and experimental medicine
Immunotherapy has revolutionized hematologic cancer treatment, yet responses remain unpredictable due to primary resistance, relapse, and life-threatening toxicities. Conventional biomarkers fail to capture the complexity of tumor-immune interactions...

Evaluating machine learning approaches for host prediction using H3 influenza genomic data.

PloS one
BACKGROUND: H3 influenza A viruses (IAV) have been shown to frequently cross the species barrier which can be an important factor in sustained transmission and spread. Machine learning methods have been widely explored for host prediction of IAV usin...

Global genomic survey of Kentucky: discovery of a chromosomeborne and the emergence of ST314, an MDR clone mediated by the IncR plasmid.

Emerging microbes & infections
Antimicrobial resistance (AMR) in enterica serotype Kentucky ( Kentucky) is a global challenge, with increasing resistance to cephalosporins, ciprofloxacin, and carbapenems significantly limiting treatment strategies, yet its worldwide dissemination...

Rapid key gene discovery for bacterial shape: a cross-species machine learning approach.

BMC microbiology
Accurately identifying genes responsible for specific functions is a cornerstone of biological research, but current methods are often limited to single-species analyses. Here, we present a novel method, called Genomic and Phenotype-based machine lea...