Genetics

Counterions in RNA structure: Structural bioinformatics analysis to identify the role of Mg2+ ions in base pair formation

Contribution of metal ions on nucleic acids structures and functions is undeniable. Among the available metal ions like Na+, K+, Ca2+, Mg2+ etc., the role that play the Mg2+ ion is very significant related to the stability of the structures of RNA and this is quite well studied. But it is not possible to grasp the entire functionality of Mg2+ ion in the structure of RNA. So, to have a better und...

BioM2: biologically informed multi-stage machine learning for phenotype prediction using omics data.

Navigating the complex landscape of high-dimensional omics data with machine learning models presents a significant challenge. The integration of biological domain knowledge into these models has shown promise in creating more meaningful stratifications of predictor variables, leading to algorithms that are both more accurate and generalizable. However, the wider availability of machine learning t...

Heterogeneous biomedical entity representation learning for gene-disease association prediction.

Understanding the genetic basis of disease is a fundamental aspect of medical research, as genes are the classic units of heredity and play a crucial ...

A genome-scale deep learning model to predict gene expression changes of genetic perturbations from multiplex biological networks.

Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. Howeve...

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.

Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory p...

scHyper: reconstructing cell-cell communication through hypergraph neural networks.

Cell-cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring ...

Designing interpretable deep learning applications for functional genomics: a quantitative analysis.

Deep learning applications have had a profound impact on many scientific fields, including functional genomics. Deep learning models can learn complex...

scGHSOM: Hierarchical clustering and visualization of single-cell and CRISPR data using growing hierarchical SOM

High-dimensional single-cell data poses significant challenges in identifying underlying biological patterns due to the complexity and heterogeneity...

GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning

Genetic variants (GVs) are defined as differences in the DNA sequences among individuals and play a crucial role in diagnosing and treating genetic ...

The Genomic Code: The genome instantiates a generative model of the organism

How does the genome encode the form of the organism? What is the nature of this genomic code? Inspired by recent work in machine learning and neuros...

U-learning for Prediction Inference via Combinatory Multi-Subsampling: With Applications to LASSO and Neural Networks

Epigenetic aging clocks play a pivotal role in estimating an individual's biological age through the examination of DNA methylation patterns at nume...

The developmental and evolutionary characteristics of transcription factor binding site clustered regions based on an explainable machine learning model.

Gene expression is temporally and spatially regulated by the interaction of transcription factors (TFs) and cis-regulatory elements (CREs). The uneven...

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence.

Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removin...

Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.

The intricacies of the human genome, manifested as a complex network of genes, transcend conventional representations in text or numerical matrices. T...

Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions.

Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3...

LSTM Autoencoder-based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction

Artificial Intelligence (AI) has emerged as a key driver of precision agriculture, facilitating enhanced crop productivity, optimized resource use, ...

A Benchmark Dataset for Multimodal Prediction of Enzymatic Function Coupling DNA Sequences and Natural Language

Predicting gene function from its DNA sequence is a fundamental challenge in biology. Many deep learning models have been proposed to embed DNA sequ...

Scaling Up Single Image Dehazing Algorithm by Cross-Data Vision Alignment for Richer Representation Learning and Beyond

In recent years, deep neural networks tasks have increasingly relied on high-quality image inputs. With the development of high-resolution represent...

A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs.

Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regu...

SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq

The rapid development of spatial transcriptomics (ST) technologies is revolutionizing our understanding of the spatial organization of biological ti...