AI Medical Compendium Journal:
BMC genomics

Showing 31 to 40 of 132 articles

Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction.

BMC genomics
BACKGROUND: DNA methylation is a form of epigenetic modification that impacts gene expression without modifying the DNA sequence, thereby exerting control over gene function and cellular development. The prediction of DNA methylation is vital for und...

Using published pathway figures in enrichment analysis and machine learning.

BMC genomics
Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. Here, we highlight the utility of PFOCR in disease research in...

DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction.

BMC genomics
Human leukocyte antigen (HLA) is closely involved in regulating the human immune system. Despite great advance in detecting classical HLA Class I binders, there are few methods or toolkits for recognizing non-classical HLA Class I binders. To fill in...

plotnineSeqSuite: a Python package for visualizing sequence data using ggplot2 style.

BMC genomics
BACKGROUND: The visual sequence logo has been a hot area in the development of bioinformatics tools. ggseqlogo written in R language has been the most popular API since it was published. With the popularity of artificial intelligence and deep learnin...

Drug-target binding affinity prediction using message passing neural network and self supervised learning.

BMC genomics
BACKGROUND: Drug-target binding affinity (DTA) prediction is important for the rapid development of drug discovery. Compared to traditional methods, deep learning methods provide a new way for DTA prediction to achieve good performance without much k...

DCSE:Double-Channel-Siamese-Ensemble model for protein protein interaction prediction.

BMC genomics
BACKGROUND: Protein-protein interaction (PPI) is very important for many biochemical processes. Therefore, accurate prediction of PPI can help us better understand the role of proteins in biochemical processes. Although there are many methods to pred...

Deep learning for de-convolution of Smad2 versus Smad3 binding sites.

BMC genomics
BACKGROUND: The transforming growth factor beta-1 (TGF β-1) cytokine exerts both pro-tumor and anti-tumor effects in carcinogenesis. An increasing body of literature suggests that TGF β-1 signaling outcome is partially dependent on the regulatory tar...

scDLC: a deep learning framework to classify large sample single-cell RNA-seq data.

BMC genomics
BACKGROUND: Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for e...

SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.

BMC genomics
BACKGROUND: Protein-protein interactions (PPIs) dominate intracellular molecules to perform a series of tasks such as transcriptional regulation, information transduction, and drug signalling. The traditional wet experiment method to obtain PPIs info...

Sequence-based drug-target affinity prediction using weighted graph neural networks.

BMC genomics
BACKGROUND: Affinity prediction between molecule and protein is an important step of virtual screening, which is usually called drug-target affinity (DTA) prediction. Its accuracy directly influences the progress of drug development. Sequence-based d...