Latest AI and machine learning research in genetics for healthcare professionals.
Glioblastoma (GBM) is classified into subtypes according to the molecular expression profile; the proneural subtype has a relatively good prognosis, and the mesenchymal type is the most aggressive form with the worst prognosis. GBM undergoes proneural-mesenchymal transition (PMT) during its evolution or in response to changes in the microenvironment or therapeutic interventions. PMT is accompanied...
Non-linear interactions among single nucleotide polymorphisms (SNPs), genes, and pathways play an important role in human diseases, but identifying these interactions is a challenging task. Neural networks are state-of-the-art predictors in many domains due to their ability to analyze big data and model complex patterns, including non-linear interactions. In genetics, visible neural networks are p...
The role of genetic susceptibility in early warning and precise treatment of diabetic kidney disease (DKD) requires further investigation. A case-cont...
We present a protein engineering approach to directed evolution with machine learning that integrates a new semi-supervised neural network fitness pre...
Although genetic variant effects often interact nonadditively, strategies to uncover epistasis remain in their infancy. Here we develop low-signal sig...
is a highly versatile and resilient pathogen that can infect different tissues and rapidly develop resistance to multiple drugs. Ceftazidime-avibacta...
A method to directly predict the number of nucleic acid bases in a single-stranded DNA (ssDNA) or a genomic DNA has been proposed with a combination o...
BACKGROUND: When genes are translated into proteins, mutations in the gene sequence can lead to changes in protein structure and function as well as i...
BACKGROUND: Genomic sequencing and annotation, morphological characterization, and host range analyses of bacteriophage (phage) isolates are crucial t...
RNA has the potential to serve as a drug target, requiring RNA-small molecule binding affinity to screen potential drugs generally. However, accuratel...
Addressing the challenge of achieving a global circular bioeconomy requires efficient and robust bio-based processes operating at different scales. Th...
PURPOSEÂ OF REVIEW: This review explores the transformative potential of artificial intelligence (AI) and next-generation sequencing (NGS) in allergy d...
Machine learning techniques offer promising avenues for enhancing animal breeding programs by leveraging genomic and phenotypic data to predict valuab...
Chronic rhinosinusitis with nasal polyps (CRSwNP) is a prevalent inflammatory disease where immunomodulation plays a pivotal role. However, immuno-tra...
Prime editing (PE) is a precise tool for introducing genetic mutations in eukaryotes. Extending the efficient editing scope and mitigating undesired b...
Age-related hearing loss (ARHL) is one of the most common health conditions among the elderly population. This study used machine learning to screen f...
The black-box nature of deep learning has increasingly drawn attention to the reliability and uncertainty of predictive models. Currently, several unc...
In the realm of medicinal chemistry, the predominant challenge in molecular design lies in managing extensive molecular data sets and effectively scre...
Polygenic scores, which estimate an individual's genetic propensity for a disease or trait, have the potential to become part of genomic healthcare. N...
Endometrial adenocarcinoma carries a bleak prognosis, and the molecular markers that evaluate the progression of endometrial adenocarcinoma to advance...