ArticlePLoS computational biology2013
Improving breast cancer survival analysis through competition-based multidimensional modeling.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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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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Who cites it
49 citing papers in PubMed.
- Immunohistochemical expression of CANT1 and B3GNT3 in invasive ductal carcinoma of the breast: diagnostic and prognostic significance in lymph node metastasis.Diagnostic pathology · 2026Article
- Relapse-free survival and metastasis-free survival between patients with breast cancer receiving adjuvant or neoadjuvant therapy: A retrospective study in Iran.Caspian journal of internal medicine · 2026Article
- An Azomethine Derivative, BCS3, Targets XIAP and cIAP1/2 to Arrest Breast Cancer Progression Through MDM2-p53 and Bcl-2-Caspase Signaling Modulation.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Pre-operative prediction of BCR-free survival with mRNA variables in prostate cancer.PloS one · 2024Article
- Results of the National Breast Cancer Screening Program in Croatia (2006-2016).Croatian medical journal · 2022Article
- Ten quick tips for biomarker discovery and validation analyses using machine learning.PLoS computational biology · 2022Article
- Spherical Convolutional Neural Networks for Survival Rate Prediction in Cancer Patients.Frontiers in oncology · 2022Article
- Cohort and Trajectory Analysis in Multi-Agent Support Systems for Cancer Survivors.Journal of medical systems · 2021Article
- A systematic genome-wide mapping of oncogenic mutation selection during CRISPR-Cas9 genome editing.Nature communications · 2021Article
- A framework for validating AI in precision medicine: considerations from the European ITFoC consortium.BMC medical informatics and decision making · 2021Article
- Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge.PLoS computational biology · 2021Article
- INCISOR: An Algorithm to Identify Synthetic Rescue Mediators of Resistance to Targeted and Immunotherapy.Methods in molecular biology (Clifton, N.J.) · 2021Article
- ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data.GigaScience · 2020Article
- Cathepsin L interacts with CDK2-AP1 as a potential predictor of prognosis in patients with breast cancer.Oncology letters · 2020Article
- Translational reprogramming marks adaptation to asparagine restriction in cancer.Nature cell biology · 2019Article
- SIGN: similarity identification in gene expression.Bioinformatics (Oxford, England) · 2019Article
- Beyond Synthetic Lethality: Charting the Landscape of Pairwise Gene Expression States Associated with Survival in Cancer.Cell reports · 2019Article
- Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy.Molecular systems biology · 2019Article
- Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers.PLoS computational biology · 2019Review
- Precision medicine review: rare driver mutations and their biophysical classification.Biophysical reviews · 2019Review
Corrections and comments
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Authors and funding
24 authors.
Funding
Abstract
Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.
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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.