ArticleF1000Research2016
Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning.
Article in F1000Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 55 citations in OpenAlex.
- Explainable machine learning to compare the overall survival status between patients receiving mastectomy and breast conserving surgeries.Scientific reports · 2025Article
- Predictive Modeling of Long-Term Survivors with Stage IV Breast Cancer Using the SEER-Medicare Dataset.Cancers · 2024Article
- AI Survival Prediction Modeling: The Importance of Considering Treatments and Changes in Health Status over Time.Cancers · 2024Article
- CircTRIM1 encodes TRIM1-269aa to promote chemoresistance and metastasis of TNBC via enhancing CaM-dependent MARCKS translocation and PI3K/AKT/mTOR activation.Molecular cancer · 2024Article
- Her2/EGFR-PDGFR pathway aberrations associated with tamoxifen response in metastatic breast cancer patients.Journal of the Egyptian National Cancer Institute · 2022Article
- TUBB4A interacts with MYH9 to protect the nucleus during cell migration and promotes prostate cancer via GSK3β/β-catenin signalling.Nature communications · 2022Article
- TCOF1 upregulation in triple-negative breast cancer promotes stemness and tumour growth and correlates with poor prognosis.British journal of cancer · 2022Article
- Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.Current genomics · 2021Review
- Predicting chemotherapy response using a variational autoencoder approach.BMC bioinformatics · 2021Article
- Highly expressed SLCO1B3 inhibits the occurrence and development of breast cancer and can be used as a clinical indicator of prognosis.Scientific reports · 2021Article
- Article
- Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics.Frontiers in oncology · 2020Review
- The Emerging Role of the SLCO1B3 Protein in Cancer Resistance.Protein and peptide letters · 2020Review
- Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.Investigative radiology · 2019Article
- Decoding cancer heterogeneity: studying patient-specific signaling signatures towards personalized cancer therapy.Theranostics · 2019Article
- Trimodal distribution of arylamine N-acetyltransferase 1 mRNA in breast cancer tumors: association with overall survival and drug resistance.BMC genomics · 2018Article
- Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning.F1000Research · 2018Article
- Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning.F1000Research · 2016Article
- Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning.Signal transduction and targeted therapyArticle
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT (tamoxifen) and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures exhibited the best performance, however the other agents also predicted survival with acceptable accuracies. A support vector machine (SVM) model of paclitaxel response containing genes
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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.