Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

Abstract

Abstract. Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that hasMachine learning (ML) "learns" a model from past data in order to predict future data (1). The key process is the learning which is one of the artificial intelligences. Many different statistical, probabilistic, and optimization techniques can be implemented as the learning methods such as the logistic regression, artificial neural networks (ANN), K-nearest neighbor (KNN), decision trees (DT) and Naive Bayes. There are two main types of ML learning -supervised learning and unsupervised learning. The supervised learning builds a model by learning from known classes (labeled training data). In contrast, unsupervised learning methods learn the common features from unknown class data (unlabeled training data).ML algorithms have been used for key feature training and recognition and for group classification. The strength of ML methods is it could detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to complex genomic data, especially in cancer studies. For example, ANN and DT have been used in cancer detection and diagnosis for nearly 20 years (2-3). The clinical implication of cancer heterogeneity and various cancer genomic data available motivate the applications of ML for cancer classification using genomic data.SVM learning is one of many ML methods. Compared to the other ML methods SVM is very powerful at recognizing subtle patterns in complex datasets (4). SVM can be used to recognize handwriting, recognize fraudulent credit cards, identify a speaker, as well as detect face (5). Cancer is a genetic disease where the genomic feature patterns or feature function patterns may represent the cancer subtypes, the outcome prognosis, drug benefit prediction, tumorigenesis drivers, or a tumor-specific biological process. Therefore, the Artificial Intelligence of SVM can help us in recognizing these patterns in a variety of applications. SVM ModelSVM is a powerful method for building a classifier. It aims to create a decision boundary between two classes that enables the prediction of labels from one or more feature vectors (6). This decision boundary, known as the hyperplane, is orientated in such a way that it is as far as 41

Elaboration of a multimodal MRI-based radiomics signature for the preoperative prediction of the histological subtype in patients with non-small-cell lung cancer

“…Specifically, as for the small samples like the circumstance in this study, SVM can usually get favorable results using the limited datasets in the training set [39][40][41]. Besides, the generalizability of the SVM classifier is also remarkable in terms of the small and limited datasets [39,40].…”

“…Although the SVM classifier has several drawbacks, including the apparent complexity increase and large time consumption for large database [39,40], its merits are also very apparent. Specifically, as for the small samples like the circumstance in this study, SVM can usually get favorable results using the limited datasets in the training set [39][40][41]. Besides, the generalizability of the SVM classifier is also remarkable in terms of the small and limited datasets [39,40].…”

Metacognitive Training Modulates Default-Mode Network Homogeneity During 8-Week Olanzapine Treatment in Patients With Schizophrenia

“…The hyperplane is orientated that it is as far as possible from the nearest data points from each class. These nearest points are called the support vectors (27).…”

Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses

“…This decision boundary, also referred to as "hyperplane", is orientated in such a way that it is as far as possible from the closest data points from each of the classes. These closest points are called support vectors [23]. Unlike other algorithms based on nonlinear optimization, the danger of getting trapped in local minima is low and the solution is unique and globally optimal [24,25].…”