Gene regulatory network expansion is a task of the foremost importance in computational biology that aims at finding new genes to expand a given known gene regulatory network. To this end we present OneGenE, a novel framework for gene regulatory network expansion that relies on the BOINC platform. OneGenE is an evolution of the NES2RA algorithm, with the aim to overcome its main criticality, i.e. long response time for the final user. To achieve this goal, candidate expansion lists are pre-computed for each gene in the organism and then aggregated at runtime to produce the the final expansion list for a given known gene regulatory network. We validated OneGenE on the expression data of Pseudomonas aeruginosa, comparing its results with the one obtained by NES2RA and through a biological literature review.

Artificial agents might not understand human interests and actions if these agents cannot anticipate how a person understands a situation and, based on this, what could be his/her expectations. In many cases, understanding, expectations and behaviors are constrained, if not driven, by culture. Can we provide human culture to an artificial agent? Can we provide formal representations of different cultures? In this paper we discuss the (elusive) notion of culture and propose an approach based on the notion of trait which, we argue, allows building formal modules suitable to represent culture (broadly understood). We distinguish the trait types (knowledge, rule, behavior, interpretation) that such modules should contain and briefly discuss how they could be organized. Finally, we exemplify the role of a trait module in the flow of information internal to an agent highlighting surprising potentialities.

The aim of this letter is twofold. First, we assess the compressive sensing (CS) approach as a classification tool for multispectral remote sensing images, assuming severe scarcity of training samples (at most, ten for each class). Then, we propose a new strategy to perform domain adaptation using a CS approach for classifying images at large spatial scales (continental mapping). In particular, the “most confusing” training samples in the target domain are collected by exploiting plenty of training samples available in the source domain under the transfer learning framework. For assessing the proposed method, experiments are performed on three remotely sensed images captured by the Landsat 8 satellite in different regions of India. Results obtained using the proposed approach are found to be promising.

Essential proteins are crucial to cellular survival and development. Traditionally, essential proteins are identified by knock-out experiments, which are expensive and often fatal to the target organisms. Regarding this, an important approach to essential protein identification is through computational prediction. In this research, we present a novel computational method, Integrated Edge Weights (IEW), to innovatively predict proteins' essentiality based on essential protein-protein interactions. The experimental results on all three organisms: Saccharomyces cere-visiae (Yeast), Escherichia coli (E. coli), and Caenorhabditis ele-gans (C. elegans) show that IEW achieves better performance than the state-of-the-art methods in terms of precision-recall. Furthermore, we have demonstrated that the highly-ranked protein-protein interactions predicted by our approach tend to be biologically significant in Yeast, E. coli, and C. elegans protein-protein interaction (PPI) networks.

BACKGROUND: RNA-binding proteins interact with specific RNA molecules to regulate important cellular processes. It is therefore necessary to identify the RNA interaction partners in order to understand the precise functions of such proteins. Protein-RNA interactions are typically characterized using in vivo and in vitro experiments but these may not detect all binding partners. Therefore, computational methods that capture the protein-dependent nature of such binding interactions could help to predict potential binding partners in silico. RESULTS: We have developed three methods to predict whether an RNA can interact with a particular RNA-binding protein using support vector machines and different features based on the sequence (the Oli method), the motif score (the OliMo method) and the secondary structure (the OliMoSS method). We applied these approaches to different experimentally-derived datasets and compared the predictions with RNAcontext and RPISeq. Oli outperformed OliMoSS and RPISeq, confirming our protein-specific predictions and suggesting that tetranucleotide frequencies are appropriate discriminative features. Oli and RNAcontext were the most competitive methods in terms of the area under curve. A precision-recall curve analysis achieved higher precision values for Oli. On a second experimental dataset including real negative binding information, Oli outperformed RNAcontext with a precision of 0.73 vs. 0.59. CONCLUSIONS: Our experiments showed that features based on primary sequence information are sufficiently discriminating to predict specific RNA-protein interactions. Sequence motifs and secondary structure information were not necessary to improve these predictions. Finally we confirmed that protein-specific experimental data concerning RNA-protein interactions are valuable sources of information that can be used for the efficient training of models for in silico predictions. The scripts are available upon request to the corresponding author.

In this letter, we explore the effectiveness of a novel regression method in the context of the estimation of biophysical parameters from remotely sensed imagery as an alternative to state-of-the-art regression methods like those based on artificial neural networks and support vector machines. This method, called Gaussian process (GP) regression, formulates the learning of the regressor within a Bayesian framework, where the regression model is derived by assuming the model variables follow a Gaussian prior distribution encoding the prior knowledge about the output function. One of its interesting properties, which gives it a key advantage over state-of-the-art regression methods, is the possibility to tune the free parameters of the model in an automatic way. Experiments were focused on the problem of estimating chlorophyll concentration in subsurface waters. The achieved results suggest that the GP regression method is very promising from both viewpoints of estimation accuracy and free parameter tuning. Moreover, it handles particularly well the problem of limited availability of training samples, typically encountered in biophysical parameter estimation applications.

In a 2006 TPAMI paper, Wang proposed the Neighborhood Counting Measure, a similarity measure for the k-NN algorithm. In his paper, Wang mentioned the Minimum Risk Metric (MRM), an early distance measure based on the minimization of the risk of misclassification. Wang did not compare NCM to MRM because of its allegedly excessive computational load. In this comment paper, we complete the comparison that was missing in Wang's paper and, from our empirical evaluation, we show that MRM outperforms NCM and that its running time is not prohibitive as Wang suggested.

MOTIVATION: Mislabeled samples often appear in gene expression profile because of the similarity of different sub-type of disease and the subjective misdiagnosis. The mislabeled samples deteriorate supervised learning procedures. The LOOE-sensitivity algorithm is an approach for mislabeled sample detection for microarray based on data perturbation. However, the failure of measuring the perturbing effect makes the LOOE-sensitivity algorithm a poor performance. The purpose of this article is to design a novel detection method for mislabeled samples of microarray, which could take advantage of the measuring effect of data perturbations. RESULTS: To measure the effect of data perturbation, we define an index named perturbing influence value (PIV), based on the support vector machine (SVM) regression model. The Column Algorithm (CAPIV), Row Algorithm (RAPIV) and progressive Row Algorithm (PRAPIV) based on the PIV value are proposed to detect the mislabeled samples. Experimental results obtained by using six artificial datasets and five microarray datasets demonstrate that all proposed methods in this article are superior to LOOE-sensitivity. Moreover, compared with the simple SVM and CL-stability, the PRAPIV algorithm shows an increase in precision and high recall. AVAILABILITY: The program and source code (in JAVA) are publicly available at http://ccst.jlu.edu.cn/CSBG/PIVS/index.htm

Recently, a new machine learning approach that is based on the Gaussian process (GP) theory has been introduced in the literature. According to this approach, the learning of a machine (regressor or classifier) is formulated in terms of a Bayesian estimation problem, where the parameters of the machine are assumed to be random variables which follow jointly a Gaussian distribution. The purpose of this work is to investigate this approach in the context of the estimation of biophysical parameters. Experimental results obtained on synthetic and real data, which simulate the spectral behavior of the chlorophyll concentration in subsurface waters, are reported and compared with those yielded by the general regression neural network (GRNN) and the epsiv-insensitive support vector regression (SVR) methods.

The complexity of the selection procedure of a genetic algorithm that requires reordering, if we restrict the class of the possible fitness functions to varying fitness functions, is , where is the size of the population. The quantum genetic optimization algorithm (QGOA) exploits the power of quantum computation in order to speed up genetic procedures. In QGOA, the classical fitness evaluation and selection procedures are replaced by a single quantum procedure. While the quantum and classical genetic algorithms use the same number of generations, the QGOA requires fewer operations to identify the high-fitness subpopulation at each generation. We show that the complexity of our QGOA is in terms of number of oracle calls in the selection procedure. Such theoretical results are confirmed by the simulations of the algorithm.

In this paper, we present a new variant of the k-nearest neighbor (kNN) classifier based on the maximal margin principle. The proposed method relies on classifying a given unlabeled sample by first finding its k-nearest training samples. A local partition of the input feature space is then carried out by means of local support vector machine (SVM) decision boundaries determined after training a multiclass SVM classifier on the considered k training samples. The labeling of the unknown sample is done by looking at the local decision region to which it belongs. The method is characterized by resulting global decision boundaries of the piecewise linear type. However, the entire process can be kernelized through the determination of the k -nearest training samples in the transformed feature space by using a distance function simply reformulated on the basis of the adopted kernel. To illustrate the performance of the proposed method, an experimental analysis on three different remote sensing datasets is reported and discussed.

MOTIVATION: Classification is widely used in medical applications. However, the quality of the classifier depends critically on the accurate labeling of the training data. But for many medical applications, labeling a sample or grading a biopsy can be subjective. Existing studies confirm this phenomenon and show that even a very small number of mislabeled samples could deeply degrade the performance of the obtained classifier, particularly when the sample size is small. The problem we address in this paper is to develop a method for automatically detecting samples that are possibly mislabeled. RESULTS: We propose two algorithms, a classification-stability algorithm and a leave-one-out-error-sensitivity algorithm for detecting possibly mislabeled samples. For both algorithms, the key structure is the computation of the leave-one-out perturbation matrix. The classification-stability algorithm is based on measuring the stability of the label of a sample with respect to label changes of other samples and the version of this algorithm based on the support vector machine appears to be quite accurate for three real datasets. The suspect list produced by the version is of high quality. Furthermore, when human intervention is not available, the correction heuristic appears to be beneficial.

The goal of a System for Implicit Culture Support (SICS) is to establish an implicit culture phenomenon, namely when the elements of a set behave according to the culture of a generally different group of agents. Earlier work claimed that Implicit Culture support can be seen as a generalization of Collaborative Filtering. In this paper, we recall the concept of Implicit Culture, show how it is useful for automatically exploit tacit knowledge and we present an implementation of a System for Implicit Culture Support.