AIMC Topic: Polymorphism, Single Nucleotide
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Predictive Model of Objective Response to Nivolumab Monotherapy for Advanced Renal Cell Carcinoma by Machine Learning Using Genetic and Clinical Data: The SNiP-RCC Study.
JCO clinical cancer informatics
Apr 25, 2025
PURPOSE: Anti-PD-1 antibodies are widely used for cancer treatment, including in advanced renal cell carcinoma (RCC). However, the therapeutic response varies among patients. This study aimed to predict tumor response to nivolumab anti-PD-1 antibody ...
Breaking down data silos across companies to train genome-wide predictions: A feasibility study in wheat.
Plant biotechnology journal
Apr 20, 2025
Big data, combined with artificial intelligence (AI) techniques, holds the potential to significantly enhance the accuracy of genome-wide predictions. Motivated by the success reported for wheat hybrids, we extended the scope to inbred lines by integ...
EBMGP: a deep learning model for genomic prediction based on Elastic Net feature selection and bidirectional encoder representations from transformer's embedding and multi-head attention pooling.
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Apr 19, 2025
Enhancing early selection through genomic estimated breeding values is pivotal for reducing generation intervals and accelerating breeding programs. Recently, deep learning (DL) approaches have gained prominence in genomic prediction (GP). Here, we i...
Biological Prior Knowledge-Embedded Deep Neural Network for Plant Genomic Prediction.
Genes
Mar 31, 2025
Genomic prediction is a powerful approach that predicts phenotypic traits from genotypic information, enabling the acceleration of trait improvement in plant breeding. Traditional genomic prediction methods have primarily relied on linear mixed mode...
Exploring new drug treatment targets for immune related bone diseases using a multi omics joint analysis strategy.
Scientific reports
Mar 27, 2025
In the field of treatment and prevention of immune-related bone diseases, significant challenges persist, necessitating the urgent exploration of new and effective treatment methods. However, most existing Mendelian randomization (MR) studies are con...
Assessment for antibiotic resistance in : A practical and interpretable machine learning model based on genome-wide genetic variation.
Virulence
Mar 21, 2025
() antibiotic resistance poses a global health threat. Accurate identification of antibiotic resistant strains is essential for the control of infection. In the present study, our goal is to leverage the whole-genome data of to develop practical an...
Machine Learning and Mendelian Randomization Reveal a Tumor Immune Cell Profile for Predicting Bladder Cancer Risk and Immunotherapy Outcomes.
The American journal of pathology
Mar 21, 2025
This study's objective was to develop predictive models for bladder cancer (BLCA) using tumor infiltrated immune cell (TIIC)-related genes. Multiple RNA expression data and scRNA-seq were downloaded from the TCGA and GEO databases. A tissue specifici...
Exploring the application of deep learning methods for polygenic risk score estimation.
Biomedical physics & engineering express
Mar 13, 2025
. Polygenic risk scores (PRS) summarise genetic information into a single number with clinical and research uses. Deep learning (DL) has revolutionised multiple fields, however, the impact of DL on PRSs has been less significant. We explore how DL ca...
Improving genetic variant identification for quantitative traits using ensemble learning-based approaches.
BMC genomics
Mar 12, 2025
BACKGROUND: Genome-wide association studies (GWAS) are rapidly advancing due to the improved resolution and completeness provided by Telomere-to-Telomere (T2T) and pangenome assemblies. While recent advancements in GWAS methods have primarily focused...
Trans-ancestral rare variant association study with machine learning-based phenotyping for metabolic dysfunction-associated steatotic liver disease.
Genome biology
Mar 10, 2025
BACKGROUND: Genome-wide association studies (GWAS) have identified common variants associated with metabolic dysfunction-associated steatotic liver disease (MASLD). However, rare coding variant studies have been limited by phenotyping challenges and ...
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