AIMC Topic: Genome, Human
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The limitations of simple gene set enrichment analysis assuming gene independence.
Statistical methods in medical research
Oct 14, 2012
Since its first publication in 2003, the Gene Set Enrichment Analysis method, based on the Kolmogorov-Smirnov statistic, has been heavily used, modified, and also questioned. Recently a simplified approach using a one-sample t-test score to assess en...
Illuminating the Noncoding Genome in Cancer Using Artificial Intelligence.
Cancer research
Jul 2, 2025
Understanding the vast noncoding cancer genome requires cutting-edge, high-resolution, and accessible strategies. Artificial intelligence is revolutionizing cancer research, enabling advanced models to analyze genome regulation. This review examines ...
Endometrial tumorigenesis involves epigenetic plasticity demarcating non-coding somatic mutations and 3D-genome alterations.
Genome biology
May 9, 2025
BACKGROUND: The incidence and mortality of endometrial cancer (EC) is on the rise. Eighty-five percent of ECs depend on estrogen receptor alpha (ERα) for proliferation, but little is known about its transcriptional regulation in these tumors.
ChromActivity: integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types.
Genome biology
May 9, 2025
We introduce ChromActivity, a computational framework for predicting and annotating regulatory activity across the genome through integration of multiple epigenomic maps and various functional characterization datasets. ChromActivity generates genome...
EnrichDO: a global weighted model for Disease Ontology enrichment analysis.
GigaScience
Jan 6, 2025
BACKGROUND: Disease Ontology (DO) has been widely studied in biomedical research and clinical practice to describe the roles of genes. DO enrichment analysis is an effective means to discover associations between genes and diseases. Compared to hundr...
Deep learning insights into distinct patterns of polygenic adaptation across human populations.
Nucleic acids research
Dec 11, 2024
Response to spatiotemporal variation in selection gradients resulted in signatures of polygenic adaptation in human genomes. We introduce RAISING, a two-stage deep learning framework that optimizes neural network architecture through hyperparameter t...
Tiberius: end-to-end deep learning with an HMM for gene prediction.
Bioinformatics (Oxford, England)
Nov 28, 2024
MOTIVATION: For more than 25 years, learning-based eukaryotic gene predictors were driven by hidden Markov models (HMMs), which were directly inputted a DNA sequence. Recently, Holst et al. demonstrated with their program Helixer that the accuracy of...
Towards the genomic sequence code of DNA fragility for machine learning.
Nucleic acids research
Nov 27, 2024
Genomic DNA breakages and the subsequent insertion and deletion mutations are important contributors to genome instability and linked diseases. Unlike the research in point mutations, the relationship between DNA sequence context and the propensity f...
Semi-supervised learning with pseudo-labeling compares favorably with large language models for regulatory sequence prediction.
Briefings in bioinformatics
Sep 23, 2024
Predicting molecular processes using deep learning is a promising approach to provide biological insights for non-coding single nucleotide polymorphisms identified in genome-wide association studies. However, most deep learning methods rely on superv...
Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model.
Bioinformatics (Oxford, England)
Sep 2, 2024
MOTIVATION: 5-Hydroxymethylcytosine (5hmC), a crucial epigenetic mark with a significant role in regulating tissue-specific gene expression, is essential for understanding the dynamic functions of the human genome. Despite its importance, predicting ...
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