Genetics

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

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Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning-Based Advanced Perspectives.

Mental disorders are a representative type of brain disorder, including anxiety, major depressive de...

A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation.

Colorectal cancer (CRC) is a prevalent gastrointestinal tumor worldwide with high morbidity and mort...

Mouse-Geneformer: A deep learning model for mouse single-cell transcriptome and its cross-species utility.

Deep learning techniques are increasingly utilized to analyze large-scale single-cell RNA sequencing...

Multitask learning model for predicting non-coding RNA-disease associations: Incorporating local and global context.

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are crucial non-coding RNAs involved in variou...

CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.

BACKGROUND: Transcription factors (TFs) regulate the genes' expression by binding to DNA sequences. ...

Pharmacological potential of bioactive compounds in extract: A comprehensive review.

The pharmacological potential of bioactive compounds found in the plant, , have diverse medicinal ap...

Accurate Prediction of CRISPR/Cas13a Guide Activity Using Feature Selection and Deep Learning.

CRISPR/Cas13a serves as a key tool for nucleic acid tests; therefore, accurate prediction of its act...

Tlalpan 2020 Case Study: Enhancing Uric Acid Level Prediction with Machine Learning Regression and Cross-Feature Selection.

Uric acid is a key metabolic byproduct of purine degradation and plays a dual role in human health....

Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial.

Anti-angiogenic (AA) therapy is a cornerstone of metastatic clear cell renal cell carcinoma (ccRCC) ...

A novel seven-tier framework for the classification of MEFV missense variants using adaptive and rigid classifiers.

There is a great discrepancy between the clinical categorization of MEFV gene variants and in silico...

A multimodal framework for assessing the link between pathomics, transcriptomics, and pancreatic cancer mutations.

In Pancreatic Ductal Adenocarcinoma (PDAC), predicting genetic mutations directly from histopatholog...

Artificial Neural Network - Multi-Objective Genetic Algorithm based optimization for the enhanced pigment accumulation in Synechocystis sp. PCC 6803.

BACKGROUND: Natural colorants produced by the cyanobacterium include carotenoids, chlorophyll a and ...

Bioactivity of Juglans regia kernel extracts optimized using response surface method and artificial neural Network-Genetic algorithm integration.

In this study, the biological activities of the extracts obtained under optimum extraction condition...

Emerging rapid detection methods for the monitoring of cardiovascular diseases: Current trends and future perspectives.

Cardiovascular diseases (CVDs) persist as the foremost cause of global mortality, necessitating adva...

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry.

The deployment of artificial intelligence (AI) is revolutionizing neuropharmacology and drug develop...

Deep learning prioritizes cancer mutations that alter protein nucleocytoplasmic shuttling to drive tumorigenesis.

Genetic variants can affect protein function by driving aberrant subcellular localization. However, ...

SERS-based approaches in the investigation of bacterial metabolism, antibiotic resistance, and species identification.

Surface-enhanced Raman scattering (SERS) is an inelastic scattering phenomenon that occurs when phot...

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