The Development of a Universal In Silico Predictor of Protein-Protein Interactions

Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation. © 2013 Valente et al.

Analisando a determinação sexual de vertebrados com base em redes de interação entre proteínas

Pós-graduação em Ciências Biológicas (Genética) - IBB

Modelando matematicamente questões ambientais relacionadas com a água a propósito do ensino-aprendizagem de funções na 1ª série do ensino médio

Este trabalho apresenta uma forma possível de se conceber e materializar a Modelagem Matemática como método de ensino-aprendizagem em cursos regulares. Tal perspectiva de Modelagem foi organizada após considerações feitas sobre os obstáculos já apontados por aqueles que nos antecederam na área. Para observar como a professora e os alunos se envolvem em atividades de Modelagem e discutir, à luz de todo o conhecimento já produzido por pesquisas anteriores, os efeitos desse envolvimento para a prática docente no referido método, para a formação geral do educando bem como para o processo de ensino-aprendizagem da Matemática, a proposta de Modelagem foi aplicada em uma turma de primeira série do ensino Médio e avaliada quanto à produção de aprendizagens significativas de funções polinomiais do 10 e 20 graus, função exponencial e logaritmos, com enfoques de ferramentas para a compreensão de questões ambientais relacionadas com a água. Os resultados obtidos apontam que o ensino por Modelagem pode levar o aluno a tomar-se co-participe de seu processo de ensino-aprendizagem e, por conseqüência, ter sua aprendizagem signifIcativa facilitada. Por outro lado, para o professor, entre o reconhecimento das vantagens quanto à utilização da Modelagem para o ensino e a sua aplicação, existe um caminho permeado de estudo e de pesquisa, que, para ser trilhado precisa de disposição e audácia para vencer os obstáculos que se afigurem.

A inserção do uso do computador no processo de modelagem matemática contribuindo para o aprendizado de conhecimentos matemáticos

Nesta pesquisa investigamos de que forma a inserção do uso do computador e do portfólio no processo de Modelagem Matemática, contribui para a aprendizagem de conhecimentos matemáticos a partir das percepções de alunos do ensino médio. Na busca de respostas a esta problemática, traçou-se como objetivo principal uma investigação à inserção do uso do computador no processo de Modelagem Matemática, com auxilio do portfólio para o aprendizado deste processo. A abordagem da pesquisa foi qualitativa. Levantamos um referencial teórico focando em especial pesquisadores da área de Modelagem Matemática como: Biembengut e Hein (2007); Bassanezi (2006), Barbosa (2001, 2004, 2007); Borba e Penteado(2001); Ponte e Canavarro (1997) entre outros, e com alguns autores que abordam mais especificamente a inserção de tecnologias na educação como: Valente (1993); Almeida (2000), Belloni (2005) entre outros. No entrelaçamento das idéias relacionaram-se os elementos (computador, Modelagem e portfólio) para subsidiar um tratamento diferenciado do conhecimento matemático em busca de minimizar, por exemplo, os baixos índices no Sistema de Avaliação do Ensino Básico (SAEB) dos alunos do ensino médio do Estado do Pará em Matemática. Sendo assim, foi necessário rever a forma atual de transposição do ensino dessa disciplina. O histórico da Modelagem é descrito em algumas concepções, buscando pontos de aproximação com as novas tecnologias em especial o computador. A pesquisa de campo foi desenvolvida a partir do curso: Modelagem Matemática: Aprendendo Matemática com a utilização do Computador. Na pesquisa de campo os instrumentos utilizados foram: o portfólio e o questionário. O uso do portfólio na pesquisa foi inspirado a partir de uma idéia em Biembengut e Hein (2007) que dizem haver a necessidade de se criar um relatório no final do processo de Modelagem. No entanto verificou-se que o uso do portfólio extrapola sua utilidade como coleta de dados, já que se constitui também como instrumento de organização e constituição do processo de Modelagem da Matemática. Para a análise dos dados definiu-se categorias de análises do tipo emergentes a partir de Fiorentini e Lorenzato (2007). A pesquisa de campo foi desenvolvida no Laboratório de Informática da Escola Estadual de Ensino Médio Mário Barbosa na área correspondente a Região metropolitana de Belém no Estado do Pará, onde por meio da inserção do uso computador neste processo, potencializou-se a aprendizagem dos conhecimentos matemáticos pelos alunos do ensino médio. Nas atividades desenvolvidas, percebeu-se que o ambiente gerado pelo processo de Modelagem dentro do laboratório de informática, permitiu-se trabalhar de forma coletiva e colaborativa, onde os resultados foram significativos, principalmente, articulado ao uso do computador. Nesta pesquisa mostraremos que a Modelagem e o portfólio estabelecem uma relação de troca, possibilitando dessa forma a condução do processo de Modelagem Matemática de forma dinâmica, facilitando o aprendizado do conteúdo matemático.

Pattern recognition issues on anisotropic smoothed particle hydrodynamics

This is a preliminary theoretical discussion on the computational requirements of the state of the art smoothed particle hydrodynamics (SPH) from the optics of pattern recognition and artificial intelligence. It is pointed out in the present paper that, when including anisotropy detection to improve resolution on shock layer, SPH is a very peculiar case of unsupervised machine learning. On the other hand, the free particle nature of SPH opens an opportunity for artificial intelligence to study particles as agents acting in a collaborative framework in which the timed outcomes of a fluid simulation forms a large knowledge base, which might be very attractive in computational astrophysics phenomenological problems like self-propagating star formation.

A path- and label-cost propagation approach to speedup the training of the optimum-path forest classifier

In general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies that make effort to employ machine learning algorithms in the context of big data classification problems. The research on this area ranges from Graphics Processing Units-based implementations to mathematical optimizations, being the main drawback of the former approaches to be dependent on the graphic video card. Here, we propose an architecture-independent optimization approach for the optimum-path forest (OPF) classifier, that is designed using a theoretical formulation that relates the minimum spanning tree with the minimum spanning forest generated by the OPF over the training dataset. The experiments have shown that the approach proposed can be faster than the traditional one in five public datasets, being also as accurate as the original OPF. (C) 2014 Elsevier B. V. All rights reserved.

Classificação de tábuas de madeira usando processamento de imagens digitais e aprendizado de máquina

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

Segmentação interativa de imagens utilizando competição e cooperação entre partículas

Pós-graduação em Ciência da Computação - IBILCE

Active Learning to Maximize Area Under the ROC Curve

In active learning, a machine learning algorithmis given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal is then to judiciously choose which examples in U to have labeled in order to optimize some performance criterion, e.g. classification accuracy. We study how active learning affects AUC. We examine two existing algorithms from the literature and present our own active learning algorithms designed to maximize the AUC of the hypothesis. One of our algorithms was consistently the top performer, and Closest Sampling from the literature often came in second behind it. When good posterior probability estimates were available, our heuristics were by far the best.

Bandit-Based Algorithms for Budgeted Learning

We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ labels but has to pay for each attribute that is specified. This learning model is appropriate in many areas, including medical applications. We present new algorithms for choosing which attributes to purchase of which examples in the budgeted learning model based on algorithms for the multi-armed bandit problem. All of our approaches outperformed the current state of the art. Furthermore, we present a new means for selecting an example to purchase after the attribute is selected, instead of selecting an example uniformly at random, which is typically done. Our new example selection method improved performance of all the algorithms we tested, both ours and those in the literature.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Determination of trace elements in bovine semen samples by inductively coupled plasma mass spectrometry and data mining techniques for identification of bovine class

The reproductive performance of cattle may be influenced by several factors, but mineral imbalances are crucial in terms of direct effects on reproduction. Several studies have shown that elements such as calcium, copper, iron, magnesium, selenium, and zinc are essential for reproduction and can prevent oxidative stress. However, toxic elements such as lead, nickel, and arsenic can have adverse effects on reproduction. In this paper, we applied a simple and fast method of multi-element analysis to bovine semen samples from Zebu and European classes used in reproduction programs and artificial insemination. Samples were analyzed by inductively coupled plasma spectrometry (ICP-MS) using aqueous medium calibration and the samples were diluted in a proportion of 1:50 in a solution containing 0.01% (vol/vol) Triton X-100 and 0.5% (vol/vol) nitric acid. Rhodium, iridium, and yttrium were used as the internal standards for ICP-MS analysis. To develop a reliable method of tracing the class of bovine semen, we used data mining techniques that make it possible to classify unknown samples after checking the differentiation of known-class samples. Based on the determination of 15 elements in 41 samples of bovine semen, 3 machine-learning tools for classification were applied to determine cattle class. Our results demonstrate the potential of support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) chemometric tools to identify cattle class. Moreover, the selection tools made it possible to reduce the number of chemical elements needed from 15 to just 8.

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.