ArticleFrontiers in bioengineering and biotechnology2020
A Machine Learning Approach for Tracing Tumor Original Sites With Gene Expression Profiles.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencing.Scientific reports · 2023Article
- A Machine Learning Method to Trace Cancer Primary Lesion Using Microarray-Based Gene Expression Data.Frontiers in oncology · 2022Article
- A Novel Algorithm for Detecting Microsatellite Instability Based on Next-Generation Sequencing Data.Frontiers in oncology · 2022Article
- DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins.Frontiers in pharmacology · 2021Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Some carcinomas show that one or more metastatic sites appear with unknown origins. The identification of primary or metastatic tumor tissues is crucial for physicians to develop precise treatment plans for patients. With unknown primary origin sites, it is challenging to design specific plans for patients. Usually, those patients receive broad-spectrum chemotherapy, while still having poor prognosis though. Machine learning has been widely used and already achieved significant advantages in clinical practices. In this study, we classify and predict a large number of tumor samples with uncertain origins by applying the random forest and Naive Bayesian algorithms. We use the precision, recall, and other measurements to evaluate the performance of our approach. The results have showed that the prediction accuracy of this method was 90.4 for 7,713 samples. The accuracy was 80% for 20 metastatic tumors samples. In addition, the 10-fold cross-validation is used to evaluate the accuracy of classification, which reaches 91%.
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Registered trials
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