Genetics

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 underlying mechanisms that regulate gene transcription. Initiation of transcription is controlled through regulatory transcription factors binding to promoter core regions in the DNA sequence. Detection of promoter regions is necessary if we are to build genetic regulatory networks for biomedical and clini...

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 disorder using the combination of genomic data and machine learning. It is often hard to judge which components of a study led to better results and whether better reported results represent a true improvement or an uncorrected bias inflating performance. We extracted information about the methods used an...

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 replicate the functions of their natural counterparts. ...

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 their tissue- and cell-type-specific expression patter...

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

Innovations in molecular diagnostics have often evolved through the study of hematologic malignancies. Examples include the pioneering characterizatio...

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 that may precipitate multi-organ dysfunction, with a ...

Machine learning in RNA structure prediction: Advances and challenges.

RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advanceme...

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

The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Wit...

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 proteins have diverse functions in various biological pro...

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 diseases, and engineered proteins with enhanced stabili...

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 on messenger RNAs (mRNAs), a process closely tied ...

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 variable. In past molecular and DNA memristors, the s...

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

Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptat...

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 progression is critical for radiotherapy treatment plann...

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 million people worldwide. Recent Next-Generation Sequen...

[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 diagnosis of which is difficult due to its long and hi...

Integrating Machine Learning and Mendelian Randomization Determined a Functional Neurotrophin-Related Gene Signature in Patients with Lower-Grade Glioma.

Recent researches reported that neurotrophins can promote glioma growth/invasion but the relevant model for predicting patients' survival in Lower-Gra...

Mesophilic Argonaute-Mediated Polydisperse Droplet Biosensor for Amplification-Free, One-Pot, and Multiplexed Nucleic Acid Detection Using Deep Learning.

Detection of nucleic acids from a single multiplexed and amplification-free test is critical for ensuring food safety, clinical diagnostics, and envir...

Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables.

Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optim...

DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery.

Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibit...