AI Medical Compendium Journal:
IEEE/ACM transactions on computational biology and bioinformatics

Showing 101 to 110 of 544 articles

PPRTGI: A Personalized PageRank Graph Neural Network for TF-Target Gene Interaction Detection.

IEEE/ACM transactions on computational biology and bioinformatics
Transcription factors (TFs) regulation is required for the vast majority of biological processes in living organisms. Some diseases may be caused by improper transcriptional regulation. Identifying the target genes of TFs is thus critical for underst...

MAHyNet: Parallel Hybrid Network for RNA-Protein Binding Sites Prediction Based on Multi-Head Attention and Expectation Pooling.

IEEE/ACM transactions on computational biology and bioinformatics
RNA-binding proteins (RBPs) can regulate biological functions by interacting with specific RNAs, and play an important role in many life activities. Therefore, the rapid identification of RNA-protein binding sites is crucial for functional annotation...

GenCoder: A Novel Convolutional Neural Network Based Autoencoder for Genomic Sequence Data Compression.

IEEE/ACM transactions on computational biology and bioinformatics
Revolutionary advances in DNA sequencing technologies fundamentally change the nature of genomics. Today's sequencing technologies have opened into an outburst in genomic data volume. These data can be used in various applications where long-term sto...

A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions.

IEEE/ACM transactions on computational biology and bioinformatics
MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pat...

LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease Associations.

IEEE/ACM transactions on computational biology and bioinformatics
Previous studies have proven that circular RNAs (circRNAs) are inextricably connected to the etiology and pathophysiology of complicated diseases. Since conventional biological research are frequently small-scale, expensive, and time-consuming, it is...

Learning From an Artificial Neural Network in Phylogenetics.

IEEE/ACM transactions on computational biology and bioinformatics
We show that an iterative ansatz of deep learning and human intelligence guided simplification may lead to surprisingly simple solutions for a difficult problem in phylogenetics. Distinguishing Farris and Felsenstein trees is a longstanding problem i...

A Novel Multi-Scale Graph Neural Network for Metabolic Pathway Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Predicting the metabolic pathway classes of compounds in the human body is an important problem in drug research and development. For this purpose, we propose a Multi-Scale Graph Neural Network framework, named MSGNN. The framework includes a subgrap...

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

IEEE/ACM transactions on computational biology and bioinformatics
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...

SMGCN: Multiple Similarity and Multiple Kernel Fusion Based Graph Convolutional Neural Network for Drug-Target Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated proce...

Prediction of Drug-Disease Associations Based on Multi-Kernel Deep Learning Method in Heterogeneous Graph Embedding.

IEEE/ACM transactions on computational biology and bioinformatics
Computational drug repositioning can identify potential associations between drugs and diseases. This technology has been shown to be effective in accelerating drug development and reducing experimental costs. Although there has been plenty of resear...