Genetics

Latest AI and machine learning research in genetics for healthcare professionals.

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Showing 3319-3339 of 10,470 articles
adabmDCA: adaptive Boltzmann machine learning for biological sequences.

BACKGROUND: Boltzmann machines are energy-based models that have been shown to provide an accurate s...

Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms.

Tendinopathy is a complex multifaceted tendinopathy often associated with overuse and with its high ...

The promise of automated machine learning for the genetic analysis of complex traits.

The genetic analysis of complex traits has been dominated by parametric statistical methods due to t...

Identifying N7-methylguanosine sites by integrating multiple features.

Recent studies reported that N7-methylguanosine (m7G) plays a vital role in gene expression regulati...

Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.

Charting an organs' biological atlas requires us to spatially resolve the entire single-cell transcr...

Population pharmacokinetic model selection assisted by machine learning.

A fit-for-purpose structural and statistical model is the first major requirement in population phar...

SquiggleNet: real-time, direct classification of nanopore signals.

We present SquiggleNet, the first deep-learning model that can classify nanopore reads directly from...

Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer.

Background Preoperative mediastinal staging is crucial for the optimal management of clinical stage ...

Automated Machine-Learning Framework Integrating Histopathological and Radiological Information for Predicting IDH1 Mutation Status in Glioma.

Diffuse gliomas are the most common malignant primary brain tumors. Identification of isocitrate deh...

Deep learning for cancer type classification and driver gene identification.

BACKGROUND: Genetic information is becoming more readily available and is increasingly being used to...

An Ensemble Deep Learning based Predictor for Simultaneously Identifying Protein Ubiquitylation and SUMOylation Sites.

BACKGROUND: Several computational tools for predicting protein Ubiquitylation and SUMOylation sites ...

Machine Learning Approach to Calculate Electronic Couplings between Quasi-diabatic Molecular Orbitals: The Case of DNA.

Diabatization of one-electron states in flexible molecular aggregates is a great challenge due to th...

PIC-Me: paralogs and isoforms classifier based on machine-learning approaches.

BACKGROUND: Paralogs formed through gene duplication and isoforms formed through alternative splicin...

Interpretable machine learning for genomics.

High-throughput technologies such as next-generation sequencing allow biologists to observe cell fun...

AI delivers Michaelis constants as fuel for genome-scale metabolic models.

Michaelis constants (Km) are essential to predict the catalytic rate of enzymes, but are not widely ...

Prioritization of Mycotoxins Based on Their Genotoxic Potential with an In Silico-In Vitro Strategy.

Humans are widely exposed to a great variety of mycotoxins and their mixtures. Therefore, it is impo...

Deep learning allows genome-scale prediction of Michaelis constants from structural features.

The Michaelis constant KM describes the affinity of an enzyme for a specific substrate and is a cent...

Correlation of serum microRNA-122 level with the levels of Alanine aminotransferase and HBV-DNA in Chronic HBV-infected patients.

The microRNA-122 (miR-122) is a liver-specific microRNA that can be used as a potential molecular m...

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