AIMC Topic: Computational Biology

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GORetriever: reranking protein-description-based GO candidates by literature-driven deep information retrieval for protein function annotation.

Bioinformatics (Oxford, England)
SUMMARY: The vast majority of proteins still lack experimentally validated functional annotations, which highlights the importance of developing high-performance automated protein function prediction/annotation (AFP) methods. While existing approache...

MolMVC: Enhancing molecular representations for drug-related tasks through multi-view contrastive learning.

Bioinformatics (Oxford, England)
MOTIVATION: Effective molecular representation is critical in drug development. The complex nature of molecules demands comprehensive multi-view representations, considering 1D, 2D, and 3D aspects, to capture diverse perspectives. Obtaining represent...

Multi-task deep latent spaces for cancer survival and drug sensitivity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Cancer is a very heterogeneous disease that can be difficult to treat without addressing the specific mechanisms driving tumour progression in a given patient. High-throughput screening and sequencing data from cancer cell-lines has drive...

HCS-hierarchical algorithm for simulation of omics datasets.

Bioinformatics (Oxford, England)
MOTIVATION: Analysis of the omics data with the help of machine learning (ML) methods is limited by small sample sizes and a large number of variables. One possible approach to deal with such data is using algorithms for feature selection and reducin...

Advancing mRNA subcellular localization prediction with graph neural network and RNA structure.

Bioinformatics (Oxford, England)
MOTIVATION: The asymmetrical distribution of expressed mRNAs tightly controls the precise synthesis of proteins within human cells. This non-uniform distribution, a cornerstone of developmental biology, plays a pivotal role in numerous cellular proce...

BertSNR: an interpretable deep learning framework for single-nucleotide resolution identification of transcription factor binding sites based on DNA language model.

Bioinformatics (Oxford, England)
MOTIVATION: Transcription factors are pivotal in the regulation of gene expression, and accurate identification of transcription factor binding sites (TFBSs) at high resolution is crucial for understanding the mechanisms underlying gene regulation. T...

Bioinformatics and machine learning approaches reveal key genes and underlying molecular mechanisms of atherosclerosis: A review.

Medicine
Atherosclerosis (AS) causes thickening and hardening of the arterial wall due to accumulation of extracellular matrix, cholesterol, and cells. In this study, we used comprehensive bioinformatics tools and machine learning approaches to explore key ge...

New GO-based measures in multiple network alignment.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interaction (PPI) networks provide valuable insights into the function of biological systems. Aligning multiple PPI networks may expose relationships beyond those observable by pairwise comparisons. However, assessing the ...