AIMC Topic: Computational Biology
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iACP-DPNet: a dual-pooling causal dilated convolutional network for interpretable anticancer peptide identification.
Functional & integrative genomics
Jul 4, 2025
Anticancer peptides (ACPs) are acknowledged for their potential in cancer therapy, attributed to their safety, low side effects, and high target specificity. However, the discovery of ACPs is slowed by the high cost and labor-intensive nature of expe...
Identification of key genes as diagnostic biomarkers for IBD using bioinformatics and machine learning.
Journal of translational medicine
Jul 3, 2025
BACKGROUND: The pathogenesis of inflammatory bowel disease (IBD) involves complex molecular mechanisms, and achieving clinical remission remains challenging. This study aims to identify IBD-potential biomarkers, analyze their correlation with immune ...
Predicting RNA Structure Utilizing Attention from Pretrained Language Models.
Journal of chemical information and modeling
Jul 2, 2025
RNA possesses functional significance that extends beyond the transport of genetic information. The functional roles of noncoding RNA can be mediated through their tertiary and secondary structure, and thus, predicting RNA structure holds great promi...
Integrating bioinformatics and machine learning to investigate the mechanisms by which three major respiratory infectious diseases exacerbate heart failure.
Scientific reports
Jul 2, 2025
Heart failure (HF) is a severe cardiovascular disease often worsened by respiratory infections like influenza, COVID-19, and community-acquired pneumonia (CAP). This study aims to uncover the molecular commonalities among these respiratory diseases a...
Prioritizing perturbation-responsive gene patterns using interpretable deep learning.
Nature communications
Jul 2, 2025
Spatially resolved transcriptomics enables mapping of multiplexed gene expression within tissue contexts. While existing methods prioritize spatially variable genes within a single slice, few address identifying genes with differential spatial expres...
Unveiling diagnostic biomarkers and therapeutic targets in lung adenocarcinoma using bioinformatics and experimental validation.
Scientific reports
Jul 2, 2025
Lung adenocarcinoma (LUAD) is a major challenge in oncology due to its complex molecular structure and generally poor prognosis. The aim of this study was to find diagnostic markers and therapeutic targets for LUAD by integrating differential gene ex...
Identification of exosome-related genes in NSCLC via integrated bioinformatics and machine learning analysis.
Scientific reports
Jul 2, 2025
Exosomes are crucial in the development of non-small cell lung cancer (NSCLC), yet exosome-associated genes in NSCLC remain insufficiently explored. The present study identified 59 exosome-associated differentially expressed genes (EA-DEGs) from the ...
Integrating machine learning and bioinformatics approaches to identify novel diagnostic gene biomarkers for diabetic mice.
Scientific reports
Jul 2, 2025
Diabetes is a complex metabolic disorder, and its pathogenesis involves the interplay of genetic, environmental factors, and lifestyle choices. With the rising prevalence and increasing associated chronic complications, identifying and understanding ...
Iron metabolism and preeclampsia: new insights from bioinformatics analysis.
The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
Jul 1, 2025
OBJECTIVE: Preeclampsia (PE) is a multifactorial systemic pregnancy disease, in which iron metabolism and ferroptosis play significant roles during its pathogenesis. The diagnosis and prevention of PE remain urgent clinical issues that need to be add...
A Machine Learning Approach to Molecular Initiating Event Prediction Using High-Throughput Transcriptomic Chemical Screening Data.
Journal of chemical information and modeling
Jul 1, 2025
Improved scalability of high-throughput RNA-sequencing technologies has contributed to their proposed use in regulatory contexts for chemical hazard identification. However, the high dimensionality and size of these transcriptomic data sets present a...
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