AIMC Topic: Computational Biology

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A novel machine learning-based workflow to capture intra-patient heterogeneity through transcriptional multi-label characterization and clinically relevant classification.

Journal of biomedical informatics
OBJECTIVES: Patient classification into specific molecular subtypes is paramount in biomedical research and clinical practice to face complex, heterogeneous diseases. Existing methods, especially for gene expression-based cancer subtyping, often simp...

Computational pathology for breast cancer: Where do we stand for prognostic applications?

Breast (Edinburgh, Scotland)
The very early days of artificial intelligence (AI) in healthcare are behind us. AI is now spreading in the healthcare sector and is gradually being implemented in routine clinical practice. Driven by the increasing digitization of microscope slides,...

Digenic variant interpretation with hypothesis-driven explainable AI.

NAR genomics and bioinformatics
The digenic inheritance hypothesis holds the potential to enhance diagnostic yield in rare diseases. Computational approaches capable of accurately interpreting and prioritizing digenic combinations of variants based on the proband's phenotypes and f...

CR-deal: Explainable Neural Network for circRNA-RBP Binding Site Recognition and Interpretation.

Interdisciplinary sciences, computational life sciences
circRNAs are a type of single-stranded non-coding RNA molecules, and their unique feature is their closed circular structure. The interaction between circRNAs and RNA-binding proteins (RBPs) plays a key role in biological functions and is crucial for...

MORPSO_ECD+ELM: A Unified Framework for Gene Selection and Cancer Classification.

IEEE journal of biomedical and health informatics
Gene selection and cancer classification are inherently multi-objective tasks that require balancing competing objectives, such as maximizing classification accuracy while minimizing irrelevant or redundant genes. Existing methods often optimize a si...

Predicting Mutation-Disease Associations Through Protein Interactions Via Deep Learning.

IEEE journal of biomedical and health informatics
Disease is one of the primary factors affecting life activities, with complex etiologies often influenced by gene expression and mutation. Currently, wet lab experiments have analyzed the mechanisms of mutations, but these are usually limited by the ...

The Large Language Models on Biomedical Data Analysis: A Survey.

IEEE journal of biomedical and health informatics
With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent ...

scDMSC: Deep Multi-View Subspace Clustering for Single-Cell Multi-Omics Data.

IEEE journal of biomedical and health informatics
Single-cell multi-omics sequencing technology comprehensively considers various molecular features to reveal the complexity of cells information. The clustering analysis of multi-omics data provides new insight into cellular heterogeneity. However, m...

AGCLNDA: Enhancing the Prediction of ncRNA-Drug Resistance Association Using Adaptive Graph Contrastive Learning.

IEEE journal of biomedical and health informatics
Non-coding RNAs (ncRNAs), which do not encode proteins, have been implicated in chemotherapy resistance in cancer treatment. Given the high costs and time requirements of traditional biological experiments, there is an increasing need for computation...