AIMC Topic: DNA

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ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots.

Journal of chemical information and modeling
Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providi...

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis.

International journal of molecular sciences
Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have fail...

Automating the Illumina DNA library preparation kit for whole genome sequencing applications on the flowbot ONE liquid handler robot.

Scientific reports
Whole-genome sequencing (WGS) is currently making its transition from research tool into routine (clinical) diagnostic practice. The workflow for WGS includes the highly labor-intensive library preparations (LP), one of the most critical steps in the...

DNA Family: Boosting Weight-Sharing NAS With Block-Wise Supervisions.

IEEE transactions on pattern analysis and machine intelligence
Neural Architecture Search (NAS), aiming at automatically designing neural architectures by machines, has been considered a key step toward automatic machine learning. One notable NAS branch is the weight-sharing NAS, which significantly improves sea...

Integrative analysis with machine learning identifies diagnostic and prognostic signatures in neuroblastoma based on differentially DNA methylated enhancers between INSS stage 4 and 4S neuroblastoma.

Journal of cancer research and clinical oncology
INTRODUCTION: Accumulating evidence demonstrates that aberrant methylation of enhancers is crucial in gene expression profiles across several cancers. However, the latent effect of differently expressed enhancers between INSS stage 4S and 4 neuroblas...

EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins.

The protein journal
Protein-DNA and protein-RNA interactions are involved in many biological processes and regulate many cellular functions. Moreover, they are related to many human diseases. To understand the molecular mechanism of protein-DNA binding and protein-RNA b...

Prediction of DNA origami shape using graph neural network.

Nature materials
Unlike proteins, which have a wealth of validated structural data, experimentally or computationally validated DNA origami datasets are limited. Here we present a graph neural network that can predict the three-dimensional conformation of DNA origami...

DeepSF-4mC: A deep learning model for predicting DNA cytosine 4mC methylation sites leveraging sequence features.

Computers in biology and medicine
N-methylcytosine (4mC) is a DNA modification involving the addition of a methyl group to the fourth nitrogen atom of the cytosine base. This modification may influence gene regulation, providing potential insights into gene control mechanisms. Tradit...

RUBICON: a framework for designing efficient deep learning-based genomic basecallers.

Genome biology
Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The performance of basecalling has critical implications for all later step...

Building an ab initio solvated DNA model using Euclidean neural networks.

PloS one
Accurately modeling large biomolecules such as DNA from first principles is fundamentally challenging due to the steep computational scaling of ab initio quantum chemistry methods. This limitation becomes even more prominent when modeling biomolecule...