ArticleCancers2019
Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach.
Article in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed.
- The emerging role of machine learning-based methods in cancer classification using microRNA.Biochemistry and biophysics reports · 2026Review
- Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Identification of Gene Expression in Different Stages of Breast Cancer with Machine Learning.Cancers · 2024Article
- BRCA Mutations and MicroRNA Expression Patterns in the Peripheral Blood of Breast Cancer Patients.ACS omega · 2024Article
- The Role of MicroRNAs in Breast Cancer and the Challenges of Their Clinical Application.Diagnostics (Basel, Switzerland) · 2023Review
- Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer.Scientific reports · 2023Article
- Blood Inflammatory Biomarkers Differentiate Inpatient and Outpatient Coronavirus Disease 2019 From Influenza.Open forum infectious diseases · 2023Article
- Review
- Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms.International journal of molecular sciences · 2022Review
- Identification of miRNA biomarkers for breast cancer by combining ensemble regularized multinomial logistic regression and Cox regression.BMC bioinformatics · 2022Article
- Towards Multiplexed and Multimodal Biosensor Platforms in Real-Time Monitoring of Metabolic Disorders.Sensors (Basel, Switzerland) · 2022Review
- Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB.Biomedicines · 2022Article
- A Model for Predicting Cervical Cancer Using Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2022Article
- Article
- Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma.BMC cancer · 2021Article
- Potential role of micro ribonucleic acids in screening for anal cancer in human papilloma virus and human immunodeficiency virus related malignancies.World journal of gastrointestinal pathophysiology · 2021Review
- Regulation of autophagy by microRNAs in human breast cancer.Journal of biomedical science · 2021Review
- Article
- Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Certain small noncoding microRNAs (miRNAs) are differentially expressed in normal tissues and cancers, which makes them great candidates for biomarkers for cancer. Previously, a selected subset of miRNAs has been experimentally verified to be linked to breast cancer. In this paper, we validated the importance of these miRNAs using a machine learning approach on miRNA expression data. We performed feature selection, using Information Gain (IG), Chi-Squared (CHI2) and Least Absolute Shrinkage and Selection Operation (LASSO), on the set of these relevant miRNAs to rank them by importance. We then performed cancer classification using these miRNAs as features using Random Forest (RF) and Support Vector Machine (SVM) classifiers. Our results demonstrated that the miRNAs ranked higher by our analysis had higher classifier performance. Performance becomes lower as the rank of the miRNA decreases, confirming that these miRNAs had different degrees of importance as biomarkers. Furthermore, we discovered that using a minimum of three miRNAs as biomarkers for breast cancers can be as effective as using the entire set of 1800 miRNAs. This work suggests that machine learning is a useful tool for functional studies of miRNAs for cancer detection and diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.