In silico network topology-based prediction of gene essentiality

The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance. (C) 2007 Elsevier B.V. All rights reserved.

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

Proteomic View of the Venom from the Fire Ant Solenopsis invicta Buren

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Description of the Immatures of Workers of the Ant Linepithema micans Forel (Hymenoptera: Formicidae)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Description of the Immatures of Workers of the Weaver Ant, Camponotus textor (Hymenoptera: Formicidae)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Description of the immatures of workers of the Ant Camponotus vittatus (Hymenoptera: formicidae)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Description of the immatures of the ant, Myrmelachista catharinae

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Nesting biology and fungiculture of the fungus-growing ant, Mycetagroicus cerradensis: New light on the origin of higher-attine agriculture

The genus Mycetagroicus is perhaps the least known of all fungus-growing ant genera, having been first described in 2001 from museum specimens. A recent molecular phylogenetic analysis of the fungus-growing ants demonstrated that Mycetagroicus is the sister to all higher attine ants (Trachymyrmex, Sericomyrmex, Acromyrmex, Pseudoatta, and Atta), making it of extreme importance for understanding the transition between lower and higher attine agriculture. Four nests of Mycetagroicus cerradensis near Uberlandia, Minas Gerais, Brazil were excavated, and fungus chambers for one were located at a depth of 3.5 meters. Based on its lack of gongylidia (hyphal-tip swellings typical of higher attine cultivars), and a phylogenetic analysis of the ITS rDNA gene region, M. cerradensis cultivates a lower attine fungus in Clade 2 of lower attine (G3) fungi. This finding refines a previous estimate for the origin of higher attine agriculture, an event that can now be dated at approximately 21-25 mya in the ancestor of extant species of Trachymyrmex and Sericomyrmex.

Foraging Behavior and Mortality of Ectatomma brunneum (Hymenoptera, Formicidae) in Simultaneous Exposure to Ant Baits and Conventional Diet in Laboratory

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The Q-D algorithm for transforming series expansions into a corresponding continued fraction: an extension to cope with zero coefficients

An algorithm for deriving a continued fraction that corresponds to two series expansions simultaneously, when there are zero coefficients in one or both series, is given. It is based on using the Q-D algorithm to derive the corresponding fraction for two related series, and then transforming it into the required continued fraction. Two examples are given. (C) 2003 Elsevier B.V. All rights reserved.

Fundamentos de otimização por inteligência de enxames: uma visão geral

Este artigo apresenta uma breve revisão de alguns dos mais recentes métodos bioinspirados baseados no comportamento de populações para o desenvolvimento de técnicas de solução de problemas. As metaheurísticas tratadas aqui correspondem às estratégias de otimização por colônia de formigas, otimização por enxame de partículas, algoritmo shuffled frog-leaping, coleta de alimentos por bactérias e colônia de abelhas. Os princípios biológicos que motivaram o desenvolvimento de cada uma dessas estratégias, assim como seus respectivos algoritmos computacionais, são introduzidos. Duas aplicações diferentes foram conduzidas para exemplificar o desempenho de tais algoritmos. A finalidade é enfatizar perspectivas de aplicação destas abordagens em diferentes problemas da área de engenharia.

Attractiveness of copperleaf-based bait to leaf-cutting ants

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Intraspecific evolution of Rhizoctonia solani AG-1 IA associated with soybean and rice in Brazil based on polymorphisms at the ITS-5.8S rDNA operon

Rhizoctonia solani AG-1 IA causes leaf blight on soybean and rice. Despite the fact that R. solani AG-1 IA is a major pathogen affecting soybean and rice in Brazil and elsewhere in the world, little information is available on its genetic diversity and evolution. This study was an attempt to reveal the origin, and the patterns of movement and amplification of epidemiologically significant genotypes of R. solani AG-1 IA from soybean and rice in Brazil. For inferring intraspecific evolution of R. solani AG-1 IA sampled from soybean and rice, networks of ITS-5.8S rDNA sequencing haplotypes were built using the statistical parsimony algorithm from Clement et al. (2000) Molecular Ecology 9: 1657-1660. Higher haplotype diversity (Nei M 1987, Molecular Evolutionary Genetics Columbia University Press, New york: 512p.) was observed for the Brazilian soybean sample of R. solani AG-1 IA (0.827) in comparison with the rest of the world sample (0.431). Within the south-central American clade (3-2), four haplotypes of R. solani AG-1 IA from Mato Grosso, one from Tocantins, one from Maranhao, and one from Cuba occupied the tips of the network, indicating recent origin. The putative ancestral haplotypes had probably originated either from Mato Grosso or Maranhao States. While 16 distinct haplotypes were found in a sample of 32 soybean isolates of the pathogen, the entire rice sample (n=20) was represented by a single haplotype (haplotype 5), with a worldwide distribution. The results from nested-cladistic analysis indicated restricted gene flow with isolation by distance (or restricted dispersal by distance in nonsexual species) for the south-central American clade (3-2), mainly composed by soybean haplotypes.

Feedforward neural networks based on PPS-wavelet activation functions

Function approximation is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. Neural networks and wavenets have been recently seen as attractive tools for developing efficient solutions for many real world problems in function approximation. In this paper, it is shown how feedforward neural networks can be built using a different type of activation function referred to as the PPS-wavelet. An algorithm is presented to generate a family of PPS-wavelets that can be used to efficiently construct feedforward networks for function approximation.

An adaptive system based on artificial neural networks for NPK fluid fertilizer application

The application process of fluid fertilizers through variable rates implemented by classical techniques with feedback and conventional equipments can be inefficient or unstable. This paper proposes an open-loop control system based on artificial neural network of the type multilayer perceptron for the identification and control of the fertilizer flow rate. The network training is made by the algorithm of Levenberg-Marquardt with training data obtained from measurements. Preliminary results indicate a fast, stable and low cost control system for precision fanning. Copyright (C) 2000 IFAC.