A5103076698- Computational Drug Discovery Methods
- Extracellular vesicles in disease
- Bioinformatics and Genomic Networks
- Virus-based gene therapy research
- Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Galectins and Cancer Biology
- Viral Infections and Immunology Research
- Microbial Natural Products and Biosynthesis
- Machine Learning in Bioinformatics
- vaccines and immunoinformatics approaches
- RNA and protein synthesis mechanisms
Kyushu Institute of Technology
2022-2025
Nagoya University
2025
SimuTech Group (United States)
2023
BackgroundNovel biomarkers (BMs) are urgently needed for bronchial asthma (BA) with various phenotypes and endotypes.ObjectiveWe sought to identify novel BMs reflecting tissue pathology from serum extracellular vesicles (EVs).MethodsWe performed data-independent acquisition of EVs 4 healthy controls, noneosinophilic (NEA) patients, eosinophilic (EA) patients BA. We confirmed EA-specific via validation in 61 BA 23 controls. To further validate these findings, we 6 chronic rhinosinusitis...
Abstract Background Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict by network propagation with trans-omics analyses. Methods The prediction based topological relationship, network-based proximity, and transcriptional correlation between diseases drugs. SyndrumNET was applied analyzing six including asthma, diabetes, hypertension,...
Identifying effective therapeutic targets poses a challenge in drug discovery, especially for uncharacterized diseases without known (e.g. rare diseases, intractable diseases). This study presents novel machine learning approach employing multimodal vector-quantized variational autoencoders (VQ-VAEs) predicting target molecules across diseases. To address the lack of target-disease associations, we incorporate information on or proteins indications (applicable diseases) semi-supervised (SSL)...
A critical element of drug development is the identification therapeutic targets for diseases. However, depletion a serious problem.In this study, we propose novel concept target repositioning, an extension to predict new various Predictions were performed by trans-disease analysis which integrated genetically perturbed transcriptomic signatures (knockdown 4345 genes and overexpression 3114 genes) disease-specific gene 79 The method, takes into account similarities among diseases, enabled us...
In recent years, drug combination therapy, which utilizes the synergistic effects of combining multiple drugs, has been attracting attention for medical treatment multifactorial diseases such as cancer. The advantage therapy is that it expected to enhance therapeutic efficacy, but disadvantage blind drugs may cause harmful side effects. Therefore, necessary identify optimal drugs. this study, we develop a computational method predict combinations from viewpoint regulation target molecules....