AIMC Topic: Genome, Human

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SyMetrics: an integrated machine learning model for evaluating the pathogenicity of synonymous variants in the human genome.

NAR genomics and bioinformatics
Synonymous single nucleotide variants (sSNVs), traditionally seen as neutral, are now recognized for their biological impact. To assess their relevance, we developed SyMetrics, a framework that integrates predictors of splicing, RNA stability, evolut...

Deep contrastive learning enables genome-wide virtual screening.

Science (New York, N.Y.)
Recent breakthroughs in protein structure prediction have opened new avenues for genome-wide drug discovery, yet existing virtual screening methods remain computationally prohibitive. We present DrugCLIP, a contrastive learning framework that achieve...

Graph-based deep reinforcement learning for haplotype assembly with Ralphi.

Genome research
Haplotype assembly is the problem of reconstructing the combination of alleles on the maternally and paternally inherited chromosome copies. Individual haplotypes are essential to our understanding of how combinations of different variants impact phe...

Enabling whole genome sequencing analysis from FFPE specimens in clinical oncology.

Nature communications
The adoption of whole genome sequencing (WGS) in clinical oncology is challenged by low data quality and increased artifacts in standard-of-care formalin-fixed paraffin-embedded (FFPE) samples. Analysis of 56 fresh frozen (FF) and FFPE matched pairs ...

Improved CRISPR/Cas9 off-target prediction with DNABERT and epigenetic features.

PloS one
CRISPR/Cas9 is a powerful genome editing tool, but its clinical application is hindered by off-target effects. Accurate computational prediction of these unintended edits is crucial for ensuring the safety and efficacy of therapeutic applications. Wh...

Image-based DNA sequencing encoding for detecting low-mosaicism somatic mobile element insertions.

Nature communications
Active mobile elements in the human genome can create novel mobile element insertions (MEIs) in somatic tissues. Detection of somatic MEIs, particularly those with low mosaicism, remains a significant challenge due to sequencing artifacts and alignme...

RCANE: a deep learning algorithm for whole-genome pan-cancer somatic copy number aberration prediction using RNA-seq data.

Communications biology
Transcriptome sequencing (RNA-seq) of cancers is widely employed in cancer research to investigate gene expression patterns and their role in disease progression. Somatic copy-number aberrations (SCNAs)-critical genomic drivers of tumorigenesis-can a...

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...

Deep learning deciphers the related role of master regulators and G-quadruplexes in tissue specification.

Scientific reports
G-quadruplexes (GQs) are non-canonical DNA structures encoded by G-flipons with potential roles in gene regulation and chromatin structure. Here, we explore the role of G-flipons in tissue specification. We present a deep learning-based framework for...

Genome-wide methylome modeling via generative AI incorporating long- and short-range interactions.

Science advances
Using millions of methylation segments, we developed DiffuCpG, a generative artificial intelligence (AI) diffusion model designed to solve the critical challenge of missing data in high-throughput methylation technologies. DiffuCpG goes beyond conven...