AIMC Topic: Polymorphism, Single Nucleotide
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Cross-Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide-Level Deep Neural Network.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Jul 30, 2024
Cross-species prediction of TF binding remains a major challenge due to the rapid evolutionary turnover of individual TF binding sites, resulting in cross-species predictive performance being consistently worse than within-species performance. In thi...
Analyzing Medicago spp. seed morphology using GWAS and machine learning.
Scientific reports
Jul 30, 2024
Alfalfa is widely recognized as an important forage crop. To understand the morphological characteristics and genetic basis of seed morphology in alfalfa, we screened 318 Medicago spp., including 244 Medicago sativa subsp. sativa (alfalfa) and 23 oth...
Artificial Intelligence-Driven Prediction Revealed CFTR Associated with Therapy Outcome of Breast Cancer: A Feasibility Study.
Oncology
Jul 18, 2024
INTRODUCTION: In silico tools capable of predicting the functional consequences of genomic differences between individuals, many of which are AI-driven, have been the most effective over the past two decades for non-synonymous single nucleotide varia...
An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes.
PloS one
Jul 15, 2024
INTRODUCTION: Postoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. While several surgical risk calculators have been developed, none have so far been ...
Tracing the genealogy origin of geographic populations based on genomic variation and deep learning.
Molecular phylogenetics and evolution
Jul 2, 2024
Assigning a query individual animal or plant to its derived population is a prime task in diverse applications related to organismal genealogy. Such endeavors have conventionally relied on short DNA sequences under a phylogenetic framework. These met...
Variant Effect Prediction in the Age of Machine Learning.
Cold Spring Harbor perspectives in biology
Jul 1, 2024
Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus...
Machine learning for genomic and pedigree prediction in sugarcane.
The plant genome
Jun 26, 2024
Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non-additive genetic effects is essential in genome predicti...
Integrated Assays of Genome-Wide Association Study, Multi-Omics Co-Localization, and Machine Learning Associated Calcium Signaling Genes with Oilseed Rape Resistance to .
International journal of molecular sciences
Jun 25, 2024
(Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant resistance to Ss pertains to quantitative disease resistance (QDR) controlled by multiple minor gene...
Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets.
Scientific reports
Jun 25, 2024
Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple la...
Dynamic Addressing Molecular Robot (DAMR): An Effective and Efficient Trial-and-Error Approach for the Analysis of Single Nucleotide Polymorphisms.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Jun 17, 2024
Accurate and efficient molecular recognition plays a crucial role in the fields of molecular detection and diagnostics. Conventional trial-and-error-based molecular recognition approaches have always been challenged in distinguishing minimal differen...
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