Zobrazeno 1 - 10 of 27 pro vyhledávání: '"Quang-Hien Kha"'
1
Akademický článek
On the road to explainable AI in drug-drug interactions prediction: A systematic review
Autor: Thanh Hoa Vo, Ngan Thi Kim Nguyen, Quang Hien Kha, Nguyen Quoc Khanh Le
Publikováno v: Computational and Structural Biotechnology Journal, Vol 20, Iss , Pp 2112-2123 (2022)
Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue.
Externí odkaz: https://doaj.org/article/5f8e6fb777fd40d0af821ff69c0cbd93
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2
Akademický článek
Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug–Food Interactions from Chemical Structures
Autor: Quang-Hien Kha, Viet-Huan Le, Truong Nguyen Khanh Hung, Ngan Thi Kim Nguyen, Nguyen Quoc Khanh Le
Publikováno v: Sensors, Vol 23, Iss 8, p 3962 (2023)
Possible drug–food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug–drug interactions (DDIs) and DF
Externí odkaz: https://doaj.org/article/a15fc21151d143c5b81fa68e002ee0fa
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6
An interpretable deep learning model for classifying adaptor protein complexes from sequence information
Autor: Quang-Hien Kha, Thi-Oanh Tran, Trinh-Trung-Duong Nguyen, Van-Nui Nguyen, Khoat Than, Nguyen Quoc Khanh Le
Publikováno v: Methods (San Diego, Calif.). 207
Adaptor proteins (APs) are a family of proteins that aids in intracellular membrane trafficking, and their impairments or defects are closely related to various disorders. Traditional methods to identify and classify APs require time and complex tech
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29d197dd7ca7833ebde44ea8485733d2
https://pubmed.ncbi.nlm.nih.gov/36174933
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7
Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer
Autor: Yung-Chieh Chen, Van Hiep Nguyen, Cheng Yu Chen, Nguyen Quoc Khanh Le, Sho-Jen Cheng, Quang Hien Kha
Publikováno v: International Journal of Molecular Sciences
Volume 22
Issue 17
International Journal of Molecular Sciences, Vol 22, Iss 9254, p 9254 (2021)
Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for patients with non-small-cell lung cancer (NSCLC). We proposed a machin
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c14a83e45aae16d3490376b6e37faaa
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8
Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas
Autor: Quang-Hien Kha, Truong Nguyen Khanh Hung, Nguyen Quoc Khanh Le, Viet-Huan Le
Publikováno v: Cancers; Volume 13; Issue 21; Pages: 5398
Cancers
Cancers, Vol 13, Iss 5398, p 5398 (2021)
Simple Summary Low-grade gliomas (LGG) with the 1p/19q co-deletion mutation have been proven to have a better survival prognosis and response to treatment than individuals without the mutation. Identifying this mutation has a vital role in managing L
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d5fcaa9d49fadf7ce0a967a505002a6
https://pubmed.ncbi.nlm.nih.gov/34771562
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9
Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer
Autor: Nguyen Quoc Khanh Le, Viet-Huan Le, Truong Nguyen Khanh Hung, Quang-Hien Kha
Publikováno v: Cancers, Vol 13, Iss 3616, p 3616 (2021)
Cancers
Volume 13
Issue 14
This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (includi
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4350e6df169c284cc76741043b048459
https://www.mdpi.com/2072-6694/13/14/3616
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10
Akademický článek
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