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

Structural Restricted Boltzmann Machine for image denoising and classification

Restricted Boltzmann Machines are generative models that consist of a layer of hidden variables connected to another layer of visible units, and they are used to model the distribution over visible variables. In order to gain a higher representability power, many hidden units are commonly used, which, in combination with a large number of visible units, leads to a high number of trainable parameters. In this work we introduce the Structural Restricted Boltzmann Machine model, which taking advantage of the structure of the data in hand, constrains connections of hidden units to subsets of visible units in order to reduce significantly the number of trainable parameters, without compromising performance. As a possible area of application, we focus on image modelling. Based on the nature of the images, the structure of the connections is given in terms of spatial neighbourhoods over the pixels of the image that constitute the visible variables of the model. We conduct extensive experiments on various image domains. Image denoising is evaluated with corrupted images from the MNIST dataset. The generative power of our models is compared to vanilla RBMs, as well as their classification performance, which is assessed with five different image domains. Results show that our proposed model has a faster and more stable training, while also obtaining better results compared to an RBM with no constrained connections between its visible and hidden units

Benchmarking the Effectiveness and Efficiency of Machine Learning Algorithms for Record Linkage

Record linkage which refers to the identification of the same entities across several databases in the absence of an unique identifier is a crucial step for data integration. In this research, we study the effectiveness and efficiency of different machine learning algorithms (SVM, Random Forest, and neural networks) to link databases in a controlled experiment. We control for % of heterogeneity in data and size of training dataset. We evaluate the algorithms based on (1) quality of linkages such as F1 score based on a one threshold model and (2) size of uncertain regions that need manual review based on a two threshold model. We find that random forests performed very well both in terms of traditional metrics like F1 score (99.2% - 95.9%) as well as manual review set size (7.1% - 21%) for error rates from 0% to 60%. Though in terms of F1 scores, the algorithms (Random Forests, SVMs and Neural Nets) fared fairly similar, random forests outperformed the next best model by 28% on average in terms of the percentage of pairs that need manual review