Applicability of semi-supervised learning assumptions for gene ontology terms prediction

Gene Ontology (GO) is one of the most important resources in bioinformatics, aiming to provide a unified framework for the biological annotation of genes and proteins across all species. Predicting GO terms is an essential task for bioinformatics, but the number of available labelled proteins is in several cases insufficient for training reliable machine learning classifiers. Semi-supervised learning methods arise as a powerful solution that explodes the information contained in unlabelled data in order to improve the estimations of traditional supervised approaches. However, semi-supervised learning methods have to make strong assumptions about the nature of the training data and thus, the performance of the predictor is highly dependent on these assumptions. This paper presents an analysis of the applicability of semi-supervised learning assumptions over the specific task of GO terms prediction, focused on providing judgment elements that allow choosing the most suitable tools for specific GO terms. The results show that semi-supervised approaches significantly outperform the traditional supervised methods and that the highest performances are reached when applying the cluster assumption. Besides, it is experimentally demonstrated that cluster and manifold assumptions are complimentary to each other and an analysis of which GO terms can be more prone to be correctly predicted with each assumption, is provided.Postprint (published version

Regularización Laplaciana en el espacio dual para SVMs

Máster Universitario en en Investigación e Innovación en Inteligencia Computacional y Sistemas InteractivosNowadays, Machine Learning (ML) is a field with a great impact because of its usefulness in solving many types of problems. However, today large amounts of data are handled and therefore traditional learning methods can be severely limited in performance. To address this problem, Regularized Learning (RL) is used, where the objective is to make the model as flexible as possible but preserving the generalization properties, so that overfitting is avoided. There are many models that use regularization in their formulations, such as Lasso, or models that use intrinsic regularization, such as the Support Vector Machine (SVM). In this model, the margin of a separating hyperplane is maximized, resulting in a solution that depends only on a subset of the samples called support vectors. This Master Thesis aims to develop an SVM model with Laplacian regularization in the dual space, under the intuitive idea that close patterns should have similar coefficients. To construct the Laplacian term we will use as basis the Fused Lasso model which penalizes the differences of the consecutive coefficients, but in our case we seek to penalize the differences between every pair of samples, using the elements of the kernel matrix as weights. This thesis presents the different phases carried out in the implementation of the new proposal, starting from the standard SVM, followed by the comparative experiments between the new model and the original method. As a result, we see that Laplacian regularization is very useful, since the new proposal outperforms the standard SVM in most of the datasets used, both in classification and regression. Furthermore, we observe that if we only consider the Laplacian term and we set the parameter C (upper bound for the coefficients) as if it were infinite, we also obtain better performance than the standard SVM metho

Semi-supervised clinical text classification with Laplacian SVMs: An application to cancer case management

AbstractObjectiveTo compare linear and Laplacian SVMs on a clinical text classification task; to evaluate the effect of unlabeled training data on Laplacian SVM performance.BackgroundThe development of machine-learning based clinical text classifiers requires the creation of labeled training data, obtained via manual review by clinicians. Due to the effort and expense involved in labeling data, training data sets in the clinical domain are of limited size. In contrast, electronic medical record (EMR) systems contain hundreds of thousands of unlabeled notes that are not used by supervised machine learning approaches. Semi-supervised learning algorithms use both labeled and unlabeled data to train classifiers, and can outperform their supervised counterparts.MethodsWe trained support vector machines (SVMs) and Laplacian SVMs on a training reference standard of 820 abdominal CT, MRI, and ultrasound reports labeled for the presence of potentially malignant liver lesions that require follow up (positive class prevalence 77%). The Laplacian SVM used 19,845 randomly sampled unlabeled notes in addition to the training reference standard. We evaluated SVMs and Laplacian SVMs on a test set of 520 labeled reports.ResultsThe Laplacian SVM trained on labeled and unlabeled radiology reports significantly outperformed supervised SVMs (Macro-F1 0.773 vs. 0.741, Sensitivity 0.943 vs. 0.911, Positive Predictive value 0.877 vs. 0.883). Performance improved with the number of labeled and unlabeled notes used to train the Laplacian SVM (pearson’s ρ=0.529 for correlation between number of unlabeled notes and macro-F1 score). These results suggest that practical semi-supervised methods such as the Laplacian SVM can leverage the large, unlabeled corpora that reside within EMRs to improve clinical text classification

Support Vector Machines in R

Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations.