AIMC Topic: Genomics

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HyDRA: gene prioritization via hybrid distance-score rank aggregation.

Bioinformatics (Oxford, England)
UNLABELLED: Gene prioritization refers to a family of computational techniques for inferring disease genes through a set of training genes and carefully chosen similarity criteria. Test genes are scored based on their average similarity to the traini...

A novel representation of genomic sequences for taxonomic clustering and visualization by means of self-organizing maps.

Bioinformatics (Oxford, England)
MOTIVATION: Self-organizing maps (SOMs) are readily available bioinformatics methods for clustering and visualizing high-dimensional data, provided that such biological information is previously transformed to fixed-size, metric-based vectors. To inc...

Kernel methods for large-scale genomic data analysis.

Briefings in bioinformatics
Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today's explosive data growth in genomics. They provide a practical and principled approach to learning how a large numbe...

Genomic prediction of feed efficiency in boars by deep learning.

G3 (Bethesda, Md.)
Pork is the most widely consumed meat globally, and the industry has achieved substantial genetic advancements for several traits using genomic selection. However, traditional linear genomic prediction models may be inadequate for predicting complex ...

A generic pipeline for CADD score generation: chickenCADD and turkeyCADD.

G3 (Bethesda, Md.)
Combined Annotation Dependent Depletion (CADD) is a machine learning approach used to predict the deleteriousness of genetic variants across a genome. By integrating diverse genomic features, CADD assigns a PHRED-like rank score to each potential var...

Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning.

Genetics
Gene expression patterns are determined to a large extent by transcription factor (TF) binding to noncoding regulatory regions in the genome. However, gene expression cannot yet be systematically predicted from genome sequences, in part because nonfu...

Integrative strategies in drug discovery: Harnessing genomics, deep learning, and computer-aided drug design.

Computational biology and chemistry
The development of novel drugs increasingly relies on advanced omics technologies, including genomics, transcriptomics, proteomics, and metabolomics. These approaches provide insights into genetic mutations, biomarkers, and disease pathways. However,...

[Opportunities and challenges in the pathological diagnosis of pediatric tumors in the molecular and artificial intelligence era].

Zhonghua bing li xue za zhi = Chinese journal of pathology
Pediatric tumors differ significantly from adult cancers, possessing unique developmental origins, histological features, and molecular genetic changes. With the rapid advancement of multi-omics technologies, such as genomics, transcriptomics, proteo...

Genomic and hyperspectral imaging-based prediction blending enables selection for reduced deoxynivalenol content in wheat grains.

G3 (Bethesda, Md.)
Breeding for low deoxynivalenol (DON) mycotoxin content in wheat is challenging due to the complexity of the trait and phenotyping limitations. Since phenomic prediction relies on nonadditive effects and genomic prediction on additive effects, their ...

Bridging BioSciences and technology: The impact of AI & GenAI in life sciences and agribusiness.

Gene
The intersection of biosciences and technology has yielded transformative advancements, and Generative Artificial Intelligence (GenAI) started to stand at the forefront of this synergy. In the field of life sciences, GenAI is emerging as a catalyst, ...