AIMC Topic: Genomics

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Recent strategies and methodological advances for microbial natural product research in the post-genomics era.

Archives of microbiology
Microorganisms remain a prolific source of bioactive compounds, yet discovery efforts are often hindered by traditional methods and the repeated isolation of known molecules. In the post-genomics era, advances in genome mining and multi-omics technol...

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...

A macro-micro-macro radiogenomic framework identifies FIBCD1 as a key immune-modulating biomarker in breast cancer.

Journal of translational medicine
BACKGROUND: Breast cancer prognosis remains challenging due to tumor heterogeneity and the limited predictive power of conventional clinical models. Integrating imaging features with molecular data may improve individualized risk stratification and c...

Machine learning driven multiomics analysis identifies disulfidptosis associated molecular subtypes in ovarian cancer.

Scientific reports
Precision oncology enables molecularly guided cancer therapy through multi-omics profiling, AI-driven classification, and biomarker-targeted interventions. Disulfidptosis has emerged as a promising therapeutic target, yet no ovarian cancer classifica...

Multi-omics strategies for biomarker discovery and application in personalized oncology.

Molecular biomedicine
Multi-omics strategies, integrating genomics, transcriptomics, proteomics, and metabolomics, have revolutionized biomarker discovery and enabled novel applications in personalized oncology. Despite rapid technological developments, a comprehensive sy...

AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions.

Clinical and experimental medicine
Cancer's staggering molecular heterogeneity demands innovative approaches beyond traditional single-omics methods. The integration of multi-omics data, spanning genomics, transcriptomics, proteomics, metabolomics and radiomics, can improve diagnostic...

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...

Machine learning for genomic prediction of growth traits in aquaculture: a case study of the Australasian snapper (Chrysophrys auratus).

BMC bioinformatics
BACKGROUND: Chrysophrys auratus (family: Sparidae), commonly known as Australasian snapper, is a warm-water species being developed as a candidate for aquaculture in New Zealand. Genomic selection of elite snapper offers significant potential to acce...

DNALONGBENCH: a benchmark suite for long-range DNA prediction tasks.

Nature communications
Modeling long-range DNA dependencies is crucial for understanding genome structure and function across diverse biological contexts. However, effectively capturing these dependencies, which may span millions of base pairs in tasks such as three-dimens...