AIMC Topic: Genomics

Clear Filters Showing 71 to 80 of 1094 articles

A deep learning model for preoperative risk stratification of pancreatic ductal adenocarcinoma based on genomic predictors of liver metastasis.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) frequently presents with occult metastatic disease which can undermine the benefit of local treatment. Improved preoperative tools may enhance risk stratification.

Computational Strategies to Enhance Vitamin B Biosynthesis Potential of Microbes.

Current microbiology
Vitamin B is a vital nutrient for the normal functioning of many metabolic processes. It has a characteristic corrinoid ring structure with a cobalt ion. Its complex chemical structure demands 30 enzyme-catalyzed steps for synthesis. Deficiency of th...

Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Trait Genome-to-Phenome (G2P) dimensionality and "breeding context" combine to influence the realised prediction skill of different whole genome prediction (WGP) methods. Theory and empirical evidence both suggest there is likely to be "No Free Lunch...

Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.

Scientific reports
In the field of cancer therapy, the diversity and heterogeneity of cancer genomes in clinical patients complicate and challenge the effective use of non-targeted drugs, as these drugs often fail to address specific genetic events. Recent advancements...

Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies.

Genome research
Recent advances in spatially resolved single-omic and multi-omics technologies have led to the emergence of computational tools to detect and predict spatial domains. Additionally, histological images and immunofluorescence (IF) staining of proteins ...

Integrating multi-omics and machine learning for disease resistance prediction in legumes.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Multi-omics assisted prediction of disease resistance mechanisms using machine learning has the potential to accelerate the breeding of resistant legume varieties. Grain legumes, such as soybean (Glycine max (L.) Merr.), chickpea (Cicer arietinum L.)...

3Mont: A multi-omics integrative tool for breast cancer subtype stratification.

PloS one
Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types,...

A Innovative Strategy for Identifying Subtypes Through the Analysis of Multi-Omics Data with Adversarial Autoencoders.

Journal of computational biology : a journal of computational molecular cell biology
Cancer is a disease that is both complex and diverse, and effective diagnosis and treatment require an accurate depiction of tumor subtypes. Traditional methods of cancer identification, which rely on clinical and histopathological criteria, have lim...

Radiogenomic insights suggest that multiscale tumor heterogeneity is associated with interpretable radiomic features and outcomes in cancer patients.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: To develop radiogenomic subtypes and determine the relationships between radiomic phenotypes and multiomics molecular characteristics.

Pan-omics insights into abiotic stress responses: bridging functional genomics and precision crop breeding.

Functional & integrative genomics
Crop production has been regarded as the major goal of agricultural activities, but the rapidly growing population and climate change have become more complex in the agricultural systems. Abiotic stress greatly affects crop productivity globally; dev...