Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Breast Cancer Survivors
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Breast Cancer Survivors
Authors
Keywords
-
Journal
Frontiers in Genetics
Volume 10, Issue -, Pages -
Publisher
Frontiers Media SA
Online
2019-09-18
DOI
10.3389/fgene.2019.00848
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Using machine-learning approaches to predict non-participation in a nationwide general health check-up scheme
- (2018) Akihiro Shimoda et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
- (2018) Alberto Fernandez et al. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
- Framework of Computer Aided Diagnosis Systems for Cancer Classification Based on Medical Images
- (2018) Enas M. F. El Houby JOURNAL OF MEDICAL SYSTEMS
- A machine learning approach to the accurate prediction of monitor units for a compact proton machine
- (2018) Baozhou Sun et al. MEDICAL PHYSICS
- Organ-specific metastasis of breast cancer: molecular and cellular mechanisms underlying lung metastasis
- (2018) Meysam Yousefi et al. CELLULAR ONCOLOGY
- LONG-TERM HEALTH RISK AFTER BREAST-CANCER RADIOTHERAPY: OVERVIEW OF PASSOS METHODOLOGY AND SOFTWARE
- (2018) Markus Eidemüller et al. RADIATION PROTECTION DOSIMETRY
- Sociodemographic and economic factors are associated with weight gain between before and after cancer diagnosis: results from the prospective population-based NutriNet-Santé cohort
- (2017) Philippine Fassier et al. Oncotarget
- Predicting Breast Cancer Recurrence Using Machine Learning Techniques
- (2016) Pedro Henriques Abreu et al. ACM COMPUTING SURVEYS
- Classification of breast cancer patients using somatic mutation profiles and machine learning approaches
- (2016) Suleyman Vural et al. BMC Systems Biology
- Comparison among dimensionality reduction techniques based on Random Projection for cancer classification
- (2016) Haozhe Xie et al. COMPUTATIONAL BIOLOGY AND CHEMISTRY
- Feature Selection and Cancer Classification via Sparse Logistic Regression with the Hybrid L1/2 +2 Regularization
- (2016) Hai-Hui Huang et al. PLoS One
- A novel sparse coding algorithm for classification of tumors based on gene expression data
- (2015) Morteza Kolali Khormuji et al. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
- Breast cancer risk factors
- (2015) Marzena Kamińska et al. Przeglad Menopauzalny
- Machine learning applications in cancer prognosis and prediction
- (2015) Konstantina Kourou et al. Computational and Structural Biotechnology Journal
- Using Class Imbalance Learning for Software Defect Prediction
- (2013) Shuo Wang et al. IEEE TRANSACTIONS ON RELIABILITY
- Application of machine learning to predict the recurrence-proneness for cervical cancer
- (2013) Chih-Jen Tseng et al. NEURAL COMPUTING & APPLICATIONS
- Analysis the effect of PCA for feature reduction in non-stationary EEG based motor imagery of BCI system
- (2013) Xinyang Yu et al. OPTIK
- Multiclass Imbalance Problems: Analysis and Potential Solutions
- (2012) Shuo Wang et al. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
- MALDI-TOF MS Combined With Magnetic Beads for Detecting Serum Protein Biomarkers and Establishment of Boosting Decision Tree Model for Diagnosis of Colorectal Cancer
- (2012) Chibo Liu et al. International Journal of Medical Sciences
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationPublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More