AIMC Topic: Computational Biology
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Predicting miRNA-Disease Associations Based on Spectral Graph Transformer With Dynamic Attention and Regularization.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Extensive research indicates that microRNAs (miRNAs) play a crucial role in the analysis of complex human diseases. Recently, numerous methods utilizing graph neural networks have been developed to investigate the complex relationships between miRNAs...
Identifying Associations Between Small Nucleolar RNAs and Diseases via Graph Convolutional Network and Attention Mechanism.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Research has shown that small nucleolar RNAs (snoRNAs) play crucial roles in various biological processes, and understanding disease pathogenesis by studying their relationship with diseases is beneficial. Currently, known associations are insufficie...
Self-Supervised Pre-Training via Multi-View Graph Information Bottleneck for Molecular Property Prediction.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Molecular representation learning has remarkably accelerated the development of drug analysis and discovery. It implements machine learning methods to encode molecule embeddings for diverse downstream drug-related tasks. Due to the scarcity of labele...
Machine learning-based analysis of programmed cell death types and key genes in intervertebral disc degeneration.
Apoptosis : an international journal on programmed cell death
Dec 4, 2024
Intervertebral disc degeneration (IVDD) is intricately associated with various forms of programmed cell death (PCD). Identifying key PCD types and associated genes is essential for understanding the molecular mechanisms underlying IVDD and discoverin...
Transformers significantly improve splice site prediction.
Communications biology
Dec 4, 2024
Mutations that affect RNA splicing significantly impact human diversity and disease. Here we present a method using transformers, a type of machine learning model, to detect splicing from raw 45,000-nucleotide sequences. We generate embeddings with r...
Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration.
Frontiers in immunology
Dec 4, 2024
BACKGROUND: Low back pain resulting from intervertebral disc degeneration (IVDD) represents a significant global social problem. There are notable differences in the distribution of lymphatic vessels (LV) in normal and pathological intervertebral dis...
Exploring an novel diagnostic gene of trastuzumab-induced cardiotoxicity based on bioinformatics and machine learning.
Scientific reports
Dec 3, 2024
Trastuzumab (Tra)-induced cardiotoxicity (TIC) is a serious side effect of cancer chemotherapy, which can seriously harm the health of cancer patients. However, there is currently a lack of effective and reliable biomarkers for the early diagnosis of...
DeepEnhancerPPO: An Interpretable Deep Learning Approach for Enhancer Classification.
International journal of molecular sciences
Dec 2, 2024
Enhancers are short genomic segments located in non-coding regions of the genome that play a critical role in regulating the expression of target genes. Despite their importance in transcriptional regulation, effective methods for classifying enhance...
Accurate prediction of colorectal cancer diagnosis using machine learning based on immunohistochemistry pathological images.
Scientific reports
Dec 2, 2024
Colorectal cancer (CRC) ranks as the third most prevalent tumor and the second leading cause of mortality. Early and accurate diagnosis holds significant importance in enhancing patient treatment and prognosis. Machine learning technology and bioinfo...
Parallel development of object recognition in newborn chicks and deep neural networks.
PLoS computational biology
Dec 2, 2024
How do newborns learn to see? We propose that visual systems are space-time fitters, meaning visual development can be understood as a blind fitting process (akin to evolution) in which visual systems gradually adapt to the spatiotemporal data distri...
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