Personalized anti-tumor drug efficacy prediction based on clinical data
Heliyon, ISSN: 2405-8440, Vol: 10, Issue: 6, Page: e27300
2024
- 1Citations
- 6Captures
- 1Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations1
- Citation Indexes1
- Captures6
- Readers6
- Mentions1
- News Mentions1
- News1
Most Recent News
Study Findings on Personalized Medicine Described by Researchers at Anhui Jianzhu University (Personalized anti-tumor drug efficacy prediction based on clinical data)
2024 MAR 22 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Drug Daily -- New research on personalized medicine is the subject of
Article Description
Anti-tumor drug efficacy prediction poses an unprecedented challenge to realizing personalized medicine. This paper proposes to predict personalized anti-tumor drug efficacy based on clinical data. Specifically, we encode the clinical text as numeric vectors featured with hidden topics for patients using Latent Dirichlet Allocation model. Then, to classify patients into two classes, responsive or non-responsive to a drug, drug efficacy predictors are established by machine learning based on the Latent Dirichlet Allocation topic representation. To evaluate the proposed method, we collected and collated clinical records of lung and bowel cancer patients treated with platinum. Experimental results on the data sets show the efficacy and effectiveness of the proposed method, suggesting the potential value of clinical data in cancer precision medicine. We hope that it will promote the research of drug efficacy prediction based on clinical data.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2405844024033310; http://dx.doi.org/10.1016/j.heliyon.2024.e27300; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85187535514&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/38500995; https://linkinghub.elsevier.com/retrieve/pii/S2405844024033310; https://dx.doi.org/10.1016/j.heliyon.2024.e27300
Elsevier BV
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