Latest AI and machine learning research in genetics for healthcare professionals.
Quantum Machine Learning (QML) is a red-hot field that brings novel discoveries and exciting oppor...
Gene expression is a cellular process that plays a fundamental role in human phenotypical variatio...
Predicting phenotypes with complex genetic bases based on a small, interpretable set of variant fe...
The advancements in artificial intelligence in recent years, such as Large Language Models (LLMs),...
Precision medicine has the potential to tailor treatment decisions to individual patients using ma...
Nonlinear data visualization using t-distributed stochastic neighbor embedding (t-SNE) enables the...
SUMMARY: The vast generation of genetic data poses a significant challenge in efficiently uncovering...
Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer t...
Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is...
The application of Shapley values to high-dimensional, time-series-like data is computationally ch...
The task of understanding and interpreting the complex information encoded within genomic sequence...
Reliability in cell type annotation is challenging in single-cell RNA-sequencing data analysis bec...
Antimicrobial resistance (AMR) poses a significant global health threat, resulting in 4.96 million d...
Radiomics is a relatively new field which utilises automatically identified features from radiolog...
Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gen...
Single-cell technologies enable researchers to investigate cell functions at an individual cell leve...
Ischemic stroke (IS) is a leading cause of adult disability that can severely compromise the quality...
N6-methyladenosine (m6A) is one of the most abundant and well-known modifications in messenger RNAs ...
The integration of single-cell RNA sequencing (scRNA-seq) data from multiple experimental batches en...
Predicting molecular processes using deep learning is a promising approach to provide biological ins...
The clinical adoption of small interfering RNAs (siRNAs) has prompted the development of various com...