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Improving Antifreeze Proteins Prediction With Protein Language Models and Hybrid Feature Extraction Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate identification of antifreeze proteins (AFPs) is crucial in developing biomimetic synthetic anti-icing materials and low-temperature organ preservation materials. Although numerous machine learning-based methods have been proposed for AFPs pr...

Reinforced Metapath Optimization in Heterogeneous Information Networks for Drug-Target Interaction Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Graph neural networks offer an effective avenue for predicting drug-target interactions. In this domain, researchers have found that constructing heterogeneous information networks based on metapaths using diverse biological datasets enhances predict...

GenoM7GNet: An Efficient N-Methylguanosine Site Prediction Approach Based on a Nucleotide Language Model.

IEEE/ACM transactions on computational biology and bioinformatics
N-methylguanosine (m7G), one of the mainstream post-transcriptional RNA modifications, occupies an exceedingly significant place in medical treatments. However, classic approaches for identifying m7G sites are costly both in time and equipment. Meanw...

CTsynther: Contrastive Transformer Model for End-to-End Retrosynthesis Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Retrosynthesis prediction is a fundamental problem in organic chemistry and drug synthesis. We proposed an end-to-end deep learning model called CTsynther (Contrastive Transformer for single-step retrosynthesis prediction model) that could provide si...

Relation Extraction in Biomedical Texts: A Cross-Sentence Approach.

IEEE/ACM transactions on computational biology and bioinformatics
Relation extraction, a crucial task in understanding the intricate relationships between entities in biomedical domains, has predominantly focused on binary relations within single sentences. However, in practical biomedical scenarios, relationships ...

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 ...

MMD-DTA: A Multi-Modal Deep Learning Framework for Drug-Target Binding Affinity and Binding Region Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
The prediction of drug-target affinity (DTA) plays a crucial role in drug development and the identification of potential drug targets. In recent years, computer-assisted DTA prediction has emerged as a significant approach in this field. In this stu...

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...

KGRACDA: A Model Based on Knowledge Graph from Recursion and Attention Aggregation for CircRNA-Disease Association Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
CircRNA is closely related to human disease, so it is important to predict circRNA-disease association (CDA). However, the traditional biological detection methods have high difficulty and low accuracy, and computational methods represented by deep l...

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...