Genetics

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

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Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.

DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DN...

A New Graph Autoencoder-Based Consensus-Guided Model for scRNA-seq Cell Type Detection.

Single-cell RNA sequencing (scRNA-seq) technology is famous for providing a microscopic view to help...

Snippet Policy Network V2: Knee-Guided Neuroevolution for Multi-Lead ECG Early Classification.

Early time series classification predicts the class label of a given time series before it is comple...

Genomic Machine Learning Meta-regression: Insights on Associations of Study Features With Reported Model Performance.

Many studies have been conducted with the goal of correctly predicting diagnostic status of a disord...

promSEMBLE: Hard Pattern Mining and Ensemble Learning for Detecting DNA Promoter Sequences.

Accurate identification of DNA promoter sequences is of crucial importance in unraveling the underly...

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances.

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to repli...

CIRI-Deep Enables Single-Cell and Spatial Transcriptomic Analysis of Circular RNAs with Deep Learning.

Circular RNAs (circRNAs) are a crucial yet relatively unexplored class of transcripts known for thei...

Advances in next-generation sequencing and emerging technologies for hematologic malignancies.

Innovations in molecular diagnostics have often evolved through the study of hematologic malignancie...

Molecular Investigation and Preliminary Validation of Candidate Genes Associated with Neurological Damage in Heat Stroke.

Heat stroke (HS) is a severe medical condition characterized by a systemic inflammatory response tha...

Machine learning in RNA structure prediction: Advances and challenges.

RNA molecules play a crucial role in various biological processes, with their functionality closely ...

ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.

The incredible capabilities of generative artificial intelligence models have inevitably led to thei...

DRBpred: A sequence-based machine learning method to effectively predict DNA- and RNA-binding residues.

DNA-binding and RNA-binding proteins are essential to an organism's normal life cycle. These protein...

Transfer learning to leverage larger datasets for improved prediction of protein stability changes.

Amino acid mutations that lower a protein's thermodynamic stability are implicated in numerous disea...

miTDS: Uncovering miRNA-mRNA interactions with deep learning for functional target prediction.

MicroRNAs (miRNAs) are vital in regulating gene expression through binding to specific target sites ...

Reservoir Computing With Dynamic Reservoir using Cascaded DNA Memristors.

This article proposes molecular and DNA memristors where the state is defined by a single output var...

Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data.

Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plastic...

GlioPredictor: a deep learning model for identification of high-risk adult IDH-mutant glioma towards adjuvant treatment planning.

Identification of isocitrate dehydrogenase (IDH)-mutant glioma patients at high risk of early progre...

Artificial intelligence and database for NGS-based diagnosis in rare disease.

Rare diseases (RDs) are rare complex genetic diseases affecting a conservative estimate of 300 milli...

[Development of prognostic clinical and genetic models of the risk of low bone mineral density using neural network training].

BACKGROUND: Osteoporosis is a common age-related disease with disabling consequences, the early diag...

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