AIMC Topic: Polymorphism, Single Nucleotide
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Genomic selection in pig breeding: comparative analysis of machine learning algorithms.
Genetics, selection, evolution : GSE
Mar 10, 2025
BACKGROUND: The effectiveness of genomic prediction (GP) significantly influences breeding progress, and employing SNP markers to predict phenotypic values is a pivotal aspect of pig breeding. Machine learning (ML) methods are usually used to predict...
Integrating Artificial Intelligence and Bioinformatics Methods to Identify Disruptive STAT1 Variants Impacting Protein Stability and Function.
Genes
Mar 1, 2025
The Signal Transducer and Activator of Transcription 1 () gene is an essential component of the JAK-STAT signaling pathway. This pathway plays a pivotal role in the regulation of different cellular processes, including immune responses, cell growth,...
Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.
International journal of molecular sciences
Feb 27, 2025
Complex diseases pose challenges in prediction due to their multifactorial and polygenic nature. This study employed machine learning (ML) to analyze genomic data from the UK Biobank, aiming to predict the genomic predisposition to complex diseases l...
Genetic association studies using disease liabilities from deep neural networks.
American journal of human genetics
Feb 21, 2025
The case-control study is a widely used method for investigating the genetic underpinnings of binary traits. However, long-term, prospective cohort studies often grapple with absent or evolving health-related outcomes. Here, we propose two methods, l...
Identification of biomarkers associated with phagocytosis regulatory factors in coronary artery disease using machine learning and network analysis.
Mammalian genome : official journal of the International Mammalian Genome Society
Feb 14, 2025
BACKGROUND: Coronary artery disease (CAD) is the leading cause of death worldwide, and aberrant phagocytosis may be involved in its development. Understanding this aspect may provide new avenues for prompt CAD diagnosis.
Computational Biology
Transcription Factors
Databases, Genetic
Protein Interaction Maps
Gene Regulatory Networks
Biomarkers
Polymorphism, Single Nucleotide
Gene Expression Profiling
Phagocytosis
Humans
MicroRNAs
Receptors, IgG
Toll-Like Receptor 2
Machine Learning
Interleukin-1beta
Syk Kinase
Coronary Artery Disease
M-NET: Transforming Single Nucleotide Variations Into Patient Feature Images for the Prediction of Prostate Cancer Metastasis and Identification of Significant Pathways.
IEEE journal of biomedical and health informatics
Feb 10, 2025
High-performance prediction of prostate cancer metastasis based on single nucleotide variations remains a challenge. Therefore, we developed a novel biologically informed deep learning framework, named M-NET, for the prediction of prostate cancer met...
Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder.
Translational psychiatry
Feb 7, 2025
Although the efficacy of pharmacy in the treatment of attention deficit/hyperactivity disorder (ADHD) has been well established, the lack of predictors of treatment response poses great challenges for personalized treatment. The current study employe...
A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions.
Nature communications
Feb 3, 2025
Cells are regulated at multiple levels, from regulations of individual genes to interactions across multiple genes. Some recent neural network models can connect molecular changes to cellular phenotypes, but their design lacks modeling of regulatory ...
Integrating mitochondrial and lysosomal gene analysis for breast cancer prognosis using machine learning.
Scientific reports
Jan 27, 2025
The impact of mitochondrial and lysosomal co-dysfunction on breast cancer patient outcomes is unclear. The objective of this study is to develop a predictive machine learning (ML) model utilizing mitochondrial and lysosomal co-regulators in order to ...
Analysis of the genetic basis of fiber-related traits and flowering time in upland cotton using machine learning.
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Jan 24, 2025
Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, remains complex. While machine learning (ML) has shown great potential in uncovering the genetic archit...
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