A negative selection heuristic to predict new transcriptional targets

Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique

As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/.

De novo reconstruction of gene regulatory networks from time series data, an approach based on formal methods

Reverse engineering of gene regulatory relationships from genomics data is a crucial task to dissect the complex underlying regulatory mechanism occurring in a cell. From a computational point of view the reconstruction of gene regulatory networks is an undetermined problem as the large number of possible solutions is typically high in contrast to the number of available independent data points. Many possible solutions can fit the available data, explaining the data equally well, but only one of them can be the biologically true solution. Several strategies have been proposed in literature to reduce the search space and/or extend the amount of independent information. In this paper we propose a novel algorithm based on formal methods, mathematically rigorous techniques widely adopted in engineering to specify and verify complex software and hardware systems. Starting with a formal specification of gene regulatory hypotheses we are able to mathematically prove whether a time course experiment belongs or not to the formal specification, determining in fact whether a gene regulation exists or not. The method is able to detect both direction and sign (inhibition/activation) of regulations whereas most of literature methods are limited to undirected and/or unsigned relationships. We empirically evaluated the approach on experimental and synthetic datasets in terms of precision and recall. In most cases we observed high levels of accuracy outperforming the current state of art, despite the computational cost increases exponentially with the size of the network. We made available the tool implementing the algorithm at the following url: http://www.bioinformatics.unisannio.it.

Learning a Markov Logic network for supervised gene regulatory network inference

Background

Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules.

Results

We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate “regulates”, starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a pairwise SVM while providing relevant insights on the predictions.

Conclusions

The numerical studies show that MLN achieves very good predictive performance while opening the door to some interpretability of the decisions. Besides the ability to suggest new regulations, such an approach allows to cross-validate experimental data with existing knowledge.