Extreme learning machines for reverse engineering of gene regulatory networks from expression time series

The reconstruction of gene regulatory networks (GRNs) from genes profiles has a growing interest in bioinformatics for understanding the complex regulatory mechanisms in cellular systems. GRNs explicitly represent the cause-effect of regulation among a group of genes and its reconstruction is today a challenging computational problem. Several methods were proposed, but most of them require different input sources to provide an acceptable prediction. Thus, it is a great challenge to reconstruct a GRN only from temporal gene-expression data. Results: Extreme Learning Machine (ELM) is a new supervised neural model that has gained interest in the last years because of its higher learning rate and better performance than existing supervised models in terms of predictive power. This work proposes a novel approach for GRNs reconstruction in which ELMs are used for modeling the relationships between gene expression time series. Artificial datasets generated with the well-known benchmark tool used in DREAM competitions were used. Real datasets were used for validation of this novel proposal with well-known GRNs underlying the time series. The impact of increasing the size of GRNs was analyzed in detail for the compared methods. The results obtained confirm the superiority of the ELM approach against very recent state-of-the-art methods in the same experimental conditions.Fil: Rubiolo, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

A very simple and fast way to access and validate algorithms in reproducible research

The reproducibility of research in bioinformatics refers to the notion that new methodologies/ algorithms and scientific claims have to be published together with their data and source code, in a way that other researchers may verify the findings to further build more knowledge upon them. The replication and corroboration of research results are key to the scientific process and many journals are discussing the matter nowadays, taking concrete steps in this direction. In this journal itself, a very recent opinion note has appeared highlighting the increasing importance of this topic in bioinformatics and computational biology, inviting the community to further discuss the matter. In agreement with that article, we would like to propose here another step into that direction with a tool that allows the automatic generation of a web interface, named web-demo, directly from source code in a very simple and straightforward way. We believe this contribution can help make research not only reproducible but also more easily accessible. A web-demo associated to a published paper can accelerate an algorithm validation with real data, wide-spreading its use with just a few clicks.Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Pividori, Milton Damián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

A Method to Improve the Analysis of Cluster Ensembles

Clustering is fundamental to understand the structure of data. In the past decade the cluster ensembleproblem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a singleconsensus solution that outperforms all the ensemble members. However, there is disagreement about which arethe best ensemble characteristics to obtain a good performance: some authors have suggested that highly differentpartitions within the ensemble are beneï¬ cial for the ï¬ nal performance, whereas others have stated that mediumdiversity among them is better. While there are several measures to quantify the diversity, a better method toanalyze the best ensemble characteristics is necessary. This paper introduces a new ensemble generation strategyand a method to make slight changes in its structure. Experimental results on six datasets suggest that this isan important step towards a more systematic approach to analyze the impact of the ensemble characteristics onthe overall consensus performance.Fil: Pividori, Milton Damián. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria en Sistemas de Informacion; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria en Sistemas de Informacion; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

A Novel Method to Control the Diversity in Cluster Ensembles

Clustering is fundamental to understand the structure of data. In the past decade the cluster ensemble problem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a single consensus solution that outperforms all the ensemble members. Although disagreement among ensemble partitions (diversity) has been found to be fundamental for success, the literature has arrived to confusing conclusions: some authors suggest that high diversity is beneficial for the final performance, whereas others have indicated that medium is better. While there are several options to measure the diversity, there is no method to control it. This paper introduces a new ensemble generation strategy and a method to smoothly change the ensemble diversity. Experimental results on three datasets suggest that this is an important step towards a more systematic approach to analyze the impact of the ensemble diversity on the overall consensus performance.Sociedad Argentina de Informática e Investigación Operativ

Cluster Ensembles for Big Data Mining Problems

Mining big data involves several problems and new challenges, in addition to the huge volume of information. One the one hand, these data generally come from autonomous and decentralized sources, thus its dimensionality is heterogeneous and diverse, and generally involves privacy issues. On the other hand, algorithms for mining data such as clustering methods, have particular characteristics that make them useful for different types of data mining problems. Due to the huge amount of information, the task of choosing a single clustering approach becomes even more difficult. For instance, k-means, a very popular algorithm, always assumes spherical clusters in data; hierarchical approaches can be used when there is interest in finding this type of structure; expectationmaximization iteratively adjusts the parameters of a statistical model to fit the observed data. Moreover, all these methods work properly only with relatively small data sets. Large-volume data often make their application unfeasible, not to mention if data come from autonomous sources that are constantly growing and evolving. In the last years, a new clustering approach has emerged, called consensus clustering or cluster ensembles. Instead of running a single algorithm, this approach produces, at first, a set of data partitions (ensemble) by employing different clustering techniques on the same original data set. Then, this ensemble is processed by a consensus function, which produces a single consensus partition that outperforms individual solutions in the input ensemble. This approach has been successfully employed for distributed data mining, what makes it very interesting and applicable in the big data context. Although many techniques have been proposed for large data sets, most of them mainly focus on making individual components more efficient, instead of improving the whole consensus approach for the case of big data.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Discovering network relations in big time series with application to bioinformatics

Big Data concerns large-volume, complex and growing data sets, with multiple and autonomous sources. It is now rapidly expanding in all science and engineering domains. Time series represent an important class of big data that can be obtained from several applications, such as medicine (electrocardiogram), environmental (daily temperature), financial (weekly sales totals, and prices of mutual funds and stocks), as well as from many areas, such as socialnetworks and biology. Bioinformatics seeks to provide tools and analyses that facilitate understanding of living systems, by analyzing and correlating biological information. In particular, as increasingly large amounts of genes information have become available in the last years, more efficient algorithms for dealing with such big data in genomics are required. There is an increasing interest in this field for the discovery of the network of regulations among a group of genes, named Gene Regulation Networks (GRN), by analyzing the genes expression profiles represented as timeseries. In it has been proposed the GRNNminer method, which allows discovering the subyacent GRN among a group of genes, through the proper modeling of the temporal dynamics of the gene expression profiles with artificial neural networks. However, it implies building and training a pool of neural models for each possible gentogen relationship, which derives in executing a very large set of experiments with O( n 2 ) order, where n is the total of involved genes. This work presents a proposal for dramatically reducing such experiments number to O( (n/k)2 ) when big timeseries is involved for reconstructing a GRN from such data, by previously clustering genes profiles in k groups using selforganizing maps (SOM). This way, the GRNNminer can be applied over smaller sets of timeseries, only those appearing in the same cluster.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Cluster Ensembles for Big Data Mining Problems

Mining big data involves several problems and new challenges, in addition to the huge volume of information. One the one hand, these data generally come from autonomous and decentralized sources, thus its dimensionality is heterogeneous and diverse, and generally involves privacy issues. On the other hand, algorithms for mining data such as clustering methods, have particular characteristics that make them useful for different types of data mining problems. Due to the huge amount of information, the task of choosing a single clustering approach becomes even more difficult. For instance, k-means, a very popular algorithm, always assumes spherical clusters in data; hierarchical approaches can be used when there is interest in finding this type of structure; expectationmaximization iteratively adjusts the parameters of a statistical model to fit the observed data. Moreover, all these methods work properly only with relatively small data sets. Large-volume data often make their application unfeasible, not to mention if data come from autonomous sources that are constantly growing and evolving. In the last years, a new clustering approach has emerged, called consensus clustering or cluster ensembles. Instead of running a single algorithm, this approach produces, at first, a set of data partitions (ensemble) by employing different clustering techniques on the same original data set. Then, this ensemble is processed by a consensus function, which produces a single consensus partition that outperforms individual solutions in the input ensemble. This approach has been successfully employed for distributed data mining, what makes it very interesting and applicable in the big data context. Although many techniques have been proposed for large data sets, most of them mainly focus on making individual components more efficient, instead of improving the whole consensus approach for the case of big data.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Discovering network relations in big time series with application to bioinformatics

Big Data concerns large-volume, complex and growing data sets, with multiple and autonomous sources. It is now rapidly expanding in all science and engineering domains. Time series represent an important class of big data that can be obtained from several applications, such as medicine (electrocardiogram), environmental (daily temperature), financial (weekly sales totals, and prices of mutual funds and stocks), as well as from many areas, such as socialnetworks and biology. Bioinformatics seeks to provide tools and analyses that facilitate understanding of living systems, by analyzing and correlating biological information. In particular, as increasingly large amounts of genes information have become available in the last years, more efficient algorithms for dealing with such big data in genomics are required. There is an increasing interest in this field for the discovery of the network of regulations among a group of genes, named Gene Regulation Networks (GRN), by analyzing the genes expression profiles represented as timeseries. In it has been proposed the GRNNminer method, which allows discovering the subyacent GRN among a group of genes, through the proper modeling of the temporal dynamics of the gene expression profiles with artificial neural networks. However, it implies building and training a pool of neural models for each possible gentogen relationship, which derives in executing a very large set of experiments with O( n 2 ) order, where n is the total of involved genes. This work presents a proposal for dramatically reducing such experiments number to O( (n/k)2 ) when big timeseries is involved for reconstructing a GRN from such data, by previously clustering genes profiles in k groups using selforganizing maps (SOM). This way, the GRNNminer can be applied over smaller sets of timeseries, only those appearing in the same cluster.Sociedad Argentina de Informática e Investigación Operativa (SADIO

A Novel Method to Control the Diversity in Cluster Ensembles

Clustering is fundamental to understand the structure of data. In the past decade the cluster ensemble problem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a single consensus solution that outperforms all the ensemble members. Although disagreement among ensemble partitions (diversity) has been found to be fundamental for success, the literature has arrived to confusing conclusions: some authors suggest that high diversity is beneficial for the final performance, whereas others have indicated that medium is better. While there are several options to measure the diversity, there is no method to control it. This paper introduces a new ensemble generation strategy and a method to smoothly change the ensemble diversity. Experimental results on three datasets suggest that this is an important step towards a more systematic approach to analyze the impact of the ensemble diversity on the overall consensus performance.Sociedad Argentina de Informática e Investigación Operativ

Improving clustering with metabolic pathway data

Background: It is a common practice in bioinformatics to validate each group returned by a clustering algorithm through manual analysis, according to a-priori biological knowledge. This procedure helps finding functionally related patterns to propose hypotheses for their behavior and the biological processes involved. Therefore, this knowledge is used only as a second step, after data are just clustered according to their expression patterns. Thus, it could be very useful to be able to improve the clustering of biological data by incorporating prior knowledge into the cluster formation itself, in order to enhance the biological value of the clusters. Results: A novel training algorithm for clustering is presented, which evaluates the biological internal connections of the data points while the clusters are being formed. Within this training algorithm, the calculation of distances among data points and neurons centroids includes a new term based on information from well-known metabolic pathways. The standard self-organizing map (SOM) training versus the biologically-inspired SOM (bSOM) training were tested with two real data sets of transcripts and metabolites from Solanum lycopersicum and Arabidopsis thaliana species. Classical data mining validation measures were used to evaluate the clustering solutions obtained by both algorithms. Moreover, a new measure that takes into account the biological connectivity of the clusters was applied. The results of bSOM show important improvements in the convergence and performance for the proposed clustering method in comparison to standard SOM training, in particular, from the application point of view. Conclusions: Analyses of the clusters obtained with bSOM indicate that including biological information during training can certainly increase the biological value of the clusters found with the proposed method. It is worth to highlight that this fact has effectively improved the results, which can simplify their further analysis.Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Lopez, Mariana Gabriela. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kamenetzky, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; ArgentinaFil: Carrari, Fernando Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentin