Genetics

Diagnosing migraine from genome-wide genotype data: a machine learning analysis.

Migraine has an assumed polygenic basis, but the genetic risk variants identified in genome-wide association studies only explain a proportion of the heritability. We aimed to develop machine learning models, capturing non-additive and interactive effects, to address the missing heritability. This was a cross-sectional population-based study of participants in the second and third Trøndelag Health...

Invited Article: Al guided Dual Antiplatelet Therapy and Anticoagulation.

Artificial intelligence (AI) has emerged as a transformative tool in healthcare through data analysis, pattern recognition and predictive modeling capabilities. AI-driven approaches have the potential to positively transform patient care through personalized treatment regimens comprising antiplatelet and anticoagulant therapy. This review explores the integration of AI in guiding antithrombotic th...

Revisiting the approaches to DNA damage detection in genetic toxicology: insights and regulatory implications.

Genetic toxicology is crucial for evaluating the potential risks of chemicals and drugs to human health and the environment. The emergence of high-thr...

Technological advancement spurs Komagataella phaffii as a next-generation platform for sustainable biomanufacturing.

Biomanufacturing stands as a cornerstone of sustainable industrial development, necessitating a shift toward non-food carbon feedstocks to alleviate a...

iEnhancer-DS: Attention-based improved densenet for identifying enhancers and their strength.

Enhancers are short DNA fragments that enhance gene expression by binding to transcription factors. Accurately identifying enhancers and their strengt...

Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models.

OBJECTIVES: Early prediction of critical COVID-19 disease is crucial for an optimal clinical management. The objective of this study was to optimize p...

Fluorescence-based spectrometric and imaging methods and machine learning analyses for microbiota analysis.

Most microbiota determination (skin, gut, soil, etc.) are currently conducted in a laboratory using expensive equipment and lengthy procedures, includ...

Artificial Intelligence Measured Tumor Burden and Pre-Treatment Circulating Tumor DNA in Human Papilloma Virus-Associated Oropharynx Cancer.

BACKGROUND: Artificial intelligence (AI)-based imaging analysis and circulating tumor-associated DNA (ctDNA) are both being used diagnostically in HPV...

Semisupervised adaptive learning models for IDH1 mutation status prediction.

The mutation status of isocitrate dehydrogenase1 (IDH1) in glioma is critical information for the diagnosis, treatment, and prognosis. Accurately dete...

Generative and predictive neural networks for the design of functional RNA molecules.

RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing syst...

AI designed, mutation resistant broad neutralizing antibodies against multiple SARS-CoV-2 strains.

In this study, we developed a digital twin for SARS-CoV-2 by integrating diverse data and metadata with multiple data types and processing strategies,...

Use of polyethylene glycol precipitation and ultracentrifugation to enhance the sensitivity of hepatitis B virus DNA detection.

BACKGROUND: Sensitive molecular detection of hepatitis B virus (HBV) DNA is crucial for diagnosing and managing occult hepatitis. To improve the sensi...

Recent advances in omics and the integration of multi-omics in osteoarthritis research.

Osteoarthritis (OA) is a complex disorder driven by the combination of environmental and genetic factors. Given its high global prevalence and heterog...

Evolutionary Dynamics and Functional Differences in Clinically Relevant Pen β-Lactamases from spp.

Antimicrobial resistance (AMR) is a global threat, with species contributing significantly to difficult-to-treat infections. The Pen family of β-lact...

Integrating genetic variation with deep learning provides context for variants impacting transcription factor binding during embryogenesis.

Understanding how genetic variation impacts transcription factor (TF) binding remains a major challenge, limiting our ability to model disease-associa...

Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci.

To compare the comprehensive performance of conventional logistic regression (LR) and seven machine learning (ML) algorithms in Noise-Induced Hearing ...

scCompass: An Integrated Multi-Species scRNA-seq Database for AI-Ready.

Emerging single-cell sequencing technology has generated large amounts of data, allowing analysis of cellular dynamics and gene regulation at the sing...

Artificial intelligence in clinical genetics: current practice and attitudes among the clinical genetics workforce.

PURPOSE: Artificial intelligence (AI) applications for clinical genetics hold the potential to improve patient care through supporting diagnostics and...

GRACE: Unveiling Gene Regulatory Networks With Causal Mechanistic Graph Neural Networks in Single-Cell RNA-Sequencing Data.

Reconstructing gene regulatory networks (GRNs) using single-cell RNA sequencing (scRNA-seq) data holds great promise for unraveling cellular fate deve...

ProFun-SOM: Protein Function Prediction for Specific Ontology Based on Multiple Sequence Alignment Reconstruction.

Protein function prediction is crucial for understanding species evolution, including viral mutations. Gene ontology (GO) is a standardized representa...