AIMC Topic: Machine Learning

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Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics
MicroRNAs (miRNAs) play a significant role in cell differentiation, biological development as well as the occurrence and growth of diseases. Although many computational methods contribute to predicting the association between miRNAs and diseases, the...

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics
Currently, biomedical event extraction has received considerable attention in various fields, including natural language processing, bioinformatics, and computational biomedicine. This has led to the emergence of numerous machine learning and deep le...

De Novo Drug Design by Multi-Objective Path Consistency Learning With Beam A Search.

IEEE/ACM transactions on computational biology and bioinformatics
Generating high-quality and drug-like molecules from scratch within the expansive chemical space presents a significant challenge in the field of drug discovery. In prior research, value-based reinforcement learning algorithms have been employed to g...

A Knowledge Graph-Based Method for Drug-Drug Interaction Prediction With Contrastive Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Precisely predicting Drug-Drug Interactions (DDIs) carries the potential to elevate the quality and safety of drug therapies, protecting the well-being of patients, and providing essential guidance and decision support at every stage of the drug deve...

LEC-Codec: Learning-Based Genome Data Compression.

IEEE/ACM transactions on computational biology and bioinformatics
In this paper, we propose a Learning-based gEnome Codec (LEC), which is designed for high efficiency and enhanced flexibility. The LEC integrates several advanced technologies, including Group of Bases (GoB) compression, multi-stride coding and bidir...

LKLPDA: A Low-Rank Fast Kernel Learning Approach for Predicting piRNA-Disease Associations.

IEEE/ACM transactions on computational biology and bioinformatics
Piwi-interacting RNAs (piRNAs) are increasingly recognized as potential biomarkers for various diseases. Investig-ating the complex relationship between piRNAs and diseases through computational methods can reduce the costs and risks associated with ...

Contrasting Multi-Source Temporal Knowledge Graphs for Biomedical Hypothesis Generation.

IEEE/ACM transactions on computational biology and bioinformatics
Hypothesis Generation (HG) aims to expedite biomedical researches by generating novel hypotheses from existing scientific literature. Most existing studies focused on modeling static snapshots of the corpus, neglecting the temporal evolution of scien...

Parallel Convolutional Contrastive Learning Method for Enzyme Function Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
The function labeling of enzymes has a wide range of application value in the medical field, industrial biology and other fields. Scientists define enzyme categories by enzyme commission (EC) numbers. At present, although there are some tools for enz...

AGML: Adaptive Graph-Based Multi-Label Learning for Prediction of RBP and as Event Associations During EMT.

IEEE/ACM transactions on computational biology and bioinformatics
Increasing evidence has indicated that RNA-binding proteins (RBPs) play an essential role in mediating alternative splicing (AS) events during epithelial-mesenchymal transition (EMT). However, due to the substantial cost and complexity of biological ...

Hyb_SEnc: An Antituberculosis Peptide Predictor Based on a Hybrid Feature Vector and Stacked Ensemble Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Tuberculosis has plagued mankind since ancient times, and the struggle between humans and tuberculosis continues. Mycobacterium tuberculosis is the leading cause of tuberculosis, infecting nearly one-third of the world's population. The rise of pepti...