AIMC Topic: Computational Biology

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Generative AI mitigates representation bias and improves model fairness through synthetic health data.

PLoS computational biology
Representation bias in health data can lead to unfair decisions and compromise the generalisability of research findings. As a consequence, underrepresented subpopulations, such as those from specific ethnic backgrounds or genders, do not benefit equ...

NNKcat: deep neural network to predict catalytic constants (Kcat) by integrating protein sequence and substrate structure with enhanced data imbalance handling.

Briefings in bioinformatics
Catalytic constant (Kcat) is to describe the efficiency of catalyzing reactions. The Kcat value of an enzyme-substrate pair indicates the rate an enzyme converts saturated substrates into product during the catalytic process. However, it is challengi...

Exploration of immune-related diagnostic biomarkers in unexplained infertility by bioinformatics analysis and machine learning.

Taiwanese journal of obstetrics & gynecology
OBJECTIVE: We aimed to discover the biomarkers associated with UI and their correlation with immune cell infiltration.

MMsurv: a multimodal multi-instance multi-cancer survival prediction model integrating pathological images, clinical information, and sequencing data.

Briefings in bioinformatics
Accurate prediction of patient survival rates in cancer treatment is essential for effective therapeutic planning. Unfortunately, current models often underutilize the extensive multimodal data available, affecting confidence in predictions. This stu...

Decoupled GNNs based on multi-view contrastive learning for scRNA-seq data clustering.

Briefings in bioinformatics
Clustering is pivotal in deciphering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data. However, it suffers from several challenges in handling the high dimensionality and complexity of scRNA-seq data. Especially when employing gr...

Identifying the DNA methylation preference of transcription factors using ProtBERT and SVM.

PLoS computational biology
Transcription factors (TFs) can affect gene expression by binding to certain specific DNA sequences. This binding process of TFs may be modulated by DNA methylation. A subset of TFs that serve as methylation readers preferentially binds to certain me...

The effects of the post-delay epochs on working memory error reduction.

PLoS computational biology
Accurate retrieval of the maintained information is crucial for working memory. This process primarily occurs during post-delay epochs, when subjects receive cues and generate responses. However, the computational and neural mechanisms that underlie ...

Knowledge-aware contrastive heterogeneous molecular graph learning.

PLoS computational biology
Molecular representation learning is pivotal in predicting molecular properties and advancing drug design. Traditional methodologies, which predominantly rely on homogeneous graph encoding, are limited by their inability to integrate external knowled...

Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency.

PLoS computational biology
With a burgeoning number of artificial intelligence (AI) applications in various fields, biomolecular science has also given a big welcome to advanced AI techniques in recent years. In this broad field, scoring a protein-ligand binding structure to o...

Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data.

Clinical and translational medicine
Analysing the genome, epigenome, transcriptome, proteome, and metabolome within the spatial context of cells has transformed our understanding of tumour spatiotemporal heterogeneity. Advances in spatial multi-omics technologies now reveal complex mol...