Machine learning in cancer research: implications for personalised medicine
نویسندگان
چکیده
Driven by the growing demand of personalization of medical procedures, data-based, computer-aided cancer research in human patients is advancing at an accelerating pace, providing a broadening landscape of opportunity for Machine Learning methods. This landscape can be observed from the wide-reaching view of population studies down to the genotype detail. In this brief paper, we provide a sweeping glimpse, by no means exhaustive, of the state-of-the-art in this field at the different scales of data measurement and analysis.
منابع مشابه
Exploring Gene Signatures in Different Molecular Subtypes of Gastric Cancer (MSS/ TP53+, MSS/TP53-): A Network-based and Machine Learning Approach
Gastric cancer (GC) is one of the leading causes of cancer mortality, worldwide. Molecular understanding of GC’s different subtypes is still dismal and it is necessary to develop new subtype-specific diagnostic and therapeutic approaches. Therefore developing comprehensive research in this area is demanding to have a deeper insight into molecular processes, underlying these subtypes. In this st...
متن کاملPrediction of Breast Tumor Malignancy Using Neural Network and Whale Optimization Algorithms (WOA)
Introduction: Breast cancer is the most prevalent cause of cancer mortality among women. Early diagnosis of breast cancer gives patients greater survival time. The present study aims to provide an algorithm for more accurate prediction and more effective decision-making in the treatment of patients with breast cancer. Methods: The present study was applied, descriptive-analytical, based on the ...
متن کاملProstate cancer radiomics: A study on IMRT response prediction based on MR image features and machine learning approaches
Introduction: To develop different radiomic models based on radiomic features and machine learning methods to predict early intensity modulated radiation therapy (IMRT) response. Materials and Methods: Thirty prostate patients were included. All patients underwent pre ad post-IMRT T2 weighted and apparent diffusing coefficient (ADC) magnetic resonance imagi...
متن کاملDevelopment of an Ensemble Multi-stage Machine for Prediction of Breast Cancer Survivability
Prediction of cancer survivability using machine learning techniques has become a popular approach in recent years. In this regard, an important issue is that preparation of some features may need conducting difficult and costly experiments while these features have less significant impacts on the final decision and can be ignored from the feature set. Therefore, developing a machine for p...
متن کاملDiagnosing Breast Cancer by Machine Learning
Background and Aim: Cancer and in particular Breast cancer are among the diseases that have the highest mortality rate in Iran after heart disease. The accurate prognosis for Breast cancer is important, and the presence of various symptoms and features of this disease makes it difficult for doctors to diagnose. This study aimed to identify the factors affecting Breast cancer, modeling and ultim...
متن کامل