AIMC Topic: Genomics

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AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
We developed an adaptive and unified stacking genomic selection framework and designed a model interpretation strategy to identify the candidate significant SNPs of target traits. Genomic selection (GS) is an important technique in modern molecular b...

varCADD: large sets of standing genetic variation enable genome-wide pathogenicity prediction.

Genome medicine
BACKGROUND: Machine learning and artificial intelligence are increasingly being applied to identify phenotypically causal genetic variation. These data-driven methods require comprehensive training sets to deliver reliable results. However, large unb...

In silico prediction of variant effects: promises and limitations for precision plant breeding.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Sequence-based AI models show great potential for prediction of variant effects at high resolution, but their practical value in plant breeding remains to be confirmed through rigorous validation studies. Plant breeding has traditionally relied on ph...

A deep ensemble framework for human essential gene prediction by integrating multi-omics data.

Scientific reports
Essential genes are necessary for the survival or reproduction of a living organism. The prediction and analysis of gene essentiality can advance our understanding of basic life and human diseases, and further boost the development of new drugs. We p...

Proactive process evaluation of precision medicine platforms: a roadmap.

BMJ health & care informatics
BACKGROUND: Precision and genomic medicine have significant potential to improve population health. However, despite rapid technological development and increasing data complexity, practical applications of precision medicine remain limited. There is...

Evaluating the representational power of pre-trained DNA language models for regulatory genomics.

Genome biology
BACKGROUND: The emergence of genomic language models (gLMs) offers an unsupervised approach to learning a wide diversity of cis-regulatory patterns in the non-coding genome without requiring labels of functional activity generated by wet-lab experime...

TECM-ChI: A TECM network-based method for chromatin interaction prediction.

Gene
Chromatin interactions refer to regulatory relationships formed between chromatin regions through physical contact or spatial proximity, playing a crucial role in genome function, structure, and the development of diseases. In cancer research, for ex...

Comprehensive multi-omics and machine learning framework for glioma subtyping and precision therapeutics.

Scientific reports
Glioma is a highly heterogeneous and aggressive brain tumour that demands an integrated understanding of its molecular and immunological landscape. We collected multi-omics data from 575 TCGA diffuse-glioma patients (156 IDH-wild-type WHO-grade 4 gli...

The correlation of liquid biopsy genomic data to radiomics in colon, pancreatic, lung and prostatic cancer patients.

European journal of cancer (Oxford, England : 1990)
INTRODUCTION: With the advances in artificial intelligence (AI) and precision medicine, radiomics has emerged as a promising tool in the field of oncology. Radiogenomics integrates radiomics with genomic data, potentially offering a non-invasive meth...

Multi-task genomic prediction using gated residual variable selection neural networks.

BMC bioinformatics
BACKGROUND: The recent development of high-throughput sequencing techniques provide massive data that can be used in genome-wide prediction (GWP). Although GWP is effective on its own, the incorporation of traditional polygenic pedigree information i...