Georgina Stegmayer

*omeSOM: a software for clustering and visualization of transcriptional and metabolite data mined from interspecific crosses of crop plants

BackgroundModern biology uses experimental systems that involve the exploration of phenotypic variation as a result of the recombination of several genomes. Such systems are useful to investigate the functional evolution of metabolic networks. One such approach is the analysis of transcript and metabolite profiles. These kinds of studies generate a large amount of data, which require dedicated computational tools for their analysis.ResultsThis paper presents a novel software named *omeSOM (transcript/metabol-ome Self Organizing Map) that implements a neural model for biological data clustering and visualization. It allows the discovery of relationships between changes in transcripts and metabolites of crop plants harboring introgressed exotic alleles and furthermore, its use can be extended to other type of omics data. The software is focused on the easy identification of groups including different molecular entities, independently of the number of clusters formed. The *omeSOM software provides easy-to-visualize interfaces for the identification of coordinated variations in the co-expressed genes and co-accumulated metabolites. Additionally, this information is linked to the most widely used gene annotation and metabolic pathway databases.Conclusions*omeSOM is a software designed to give support to the data mining task of metabolic and transcriptional datasets derived from different databases. It provides a user-friendly interface and offers several visualization features, easy to understand by non-expert users. Therefore, *omeSOM provides support for data mining tasks and it is applicable to basic research as well as applied breeding programs. The software and a sample dataset are available free of charge at http://sourcesinc.sourceforge.net/omesom/.

Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model

With the emergence of the Semantic Web several domain ontologies were developed, which varied not only in their structure but also in the natural language used to define them. The lack of an integrated view of all web nodes and the existence of heterogeneous domain ontologies drive new challenges in the discovery of knowledge resources which are relevant to a users request. New approaches have recently appeared for developing web intelligence and helping users avoid irrelevant results on the web. However, there remains some work to be done. This work makes a contribution by presenting an ANN-based ontology matching model for knowledge source discovery on the Semantic Web. Experimental results obtained on a real case study have shown that this model provides satisfactory responses.

Neural network model for integration and visualization of introgressed genome and metabolite data

The volume of information derived from postgenomic technologies is rapidly increasing. Due to the amount of data involved, novel computational models are needed for introducing order into the massive data sets produced by these new technologies. Data integration is also gaining increasing attention for merging signals in order to discover unknown pathways. These topics require the development of adequate soft computing tools. This work proposes a neural network model for discovering relationships between gene expression and metabolite profiles of introgressed lines. It also provides a simple visualization interface for identification of coordinated variations in mRNA and metabolites. This may be useful when the focus is on the easily identification of groups of different patterns, independently of the number of formed clusters. This kind of analysis may help for the inference of a-priori unknown metabolic pathways involving the grouped data. The model has been used on a case study involving data from tomato fruits.

Data Mining Over Biological Datasets: An Integrated Approach Based on Computational Intelligence

Biology is in the middle of a data explosion. The technical advances achieved by the genomics, metabolomics, transcriptomics and proteomics technologies in recent years have significantly increased the amount of data that are available for biologists to analyze different aspects of an organism. However, *omics data sets have several additional problems: they have inherent biological complexity and may have significant amounts of noise as well as measurement artifacts. The need to extract information from such databases has once again become a challenge. This requires novel computational techniques and models to automatically perform data mining tasks such as integration of different data types, clustering and knowledge discovery, among others. In this article, we will present a novel integrated computational intelligence approach for biological data mining that involves neural networks and evolutionary computation. We propose the use of self-organizing maps for the identification of coordinated patterns variations; a new training algorithm that can include a priori biological information to obtain more biological meaningful clusters; a validation measure that can assess the biological significance of the clusters found; and finally, an evolutionary algorithm for the inference of unknown metabolic pathways involving the selected clusters.

Volterra NN-based behavioral model for new wireless communications devices

This paper presents a neural network-based behavioral model for accurately reproducing the nonlinear and dynamic behavior of new wireless communication devices. Moreover, an efficient procedure to extract a behavioral Volterra model from the parameters of the NN-based model is explained, thus providing a simple way to construct very compact and accurate models, which may provide open information about device performance. Experimental tests try to demonstrate the validity of the proposed approach, for characterization of RF transistors and power amplifiers, showing strong nonlinearities and memory effects in the analyzed bandwidth.

miRNAfe: A comprehensive tool for feature extraction in microRNA prediction

miRNAfe is a comprehensive tool to extract features from RNA sequences. It is freely available as a web service, allowing a single access point to almost all state-of-the-art feature extraction methods used today in a variety of works from different authors. It has a very simple user interface, where the user only needs to load a file containing the input sequences and select the features to extract. As a result, the user obtains a text file with the features extracted, which can be used to analyze the sequences or as input to a miRNA prediction software. The tool can calculate up to 80 features where many of them are multidimensional arrays. In order to simplify the web interface, the features have been divided into six pre-defined groups, each one providing information about: primary sequence, secondary structure, thermodynamic stability, statistical stability, conservation between genomes of different species and substrings analysis of the sequences. Additionally, pre-trained classifiers are provided for prediction in different species. All algorithms to extract the features have been validated, comparing the results with the ones obtained from software of the original authors. The source code is freely available for academic use under GPL license at http://sourceforge.net/projects/sourcesinc/files/mirnafe/0.90/. A user-friendly access is provided as web interface at http://fich.unl.edu.ar/sinc/web-demo/mirnafe/. A more configurable web interface can be accessed at http://fich.unl.edu.ar/sinc/web-demo/mirnafe-full/.

Automatic recognition of quarantine citrus diseases

Citrus exports to foreign markets are severely limited today by fruit diseases. Some of them, like citrus canker, black spot and scab, are quarantine for the markets. For this reason, it is important to perform strict controls before fruits are exported to avoid the inclusion of citrus affected by them. Nowadays, technical decisions are based on visual diagnosis of human experts, highly dependent on the degree of individual skills. This work presents a model capable of automatic recognize the quarantine diseases. It is based on the combination of a feature selection method and a classifier that has been trained on quarantine illness symptoms. Citrus samples with citrus canker, black spot, scab and other diseases were evaluated. Experimental work was performed on 212 samples of mandarins from a Nova cultivar. The proposed approach achieved a classification rate of quarantine/not-quarantine samples of over 83% for all classes, even when using a small subset (14) of all the available features (90). The results obtained show that the proposed method can be suitable for helping the task of citrus visual diagnosis, in particular, quarantine diseases recognition in fruits.

High Class-Imbalance in pre-miRNA Prediction: A Novel Approach Based on deepSOM

The computational prediction of novel microRNA within a full genome involves identifying sequences having the highest chance of being a miRNA precursor (pre-miRNA). These sequences are usually named candidates to miRNA. The well-known pre-miRNAs are usually only a few in comparison to the hundreds of thousands of potential candidates to miRNA that have to be analyzed, which makes this task a high class-imbalance classification problem. The classical way of approaching it has been training a binary classifier in a supervised manner, using well-known pre-miRNAs as positive class and artificially defining the negative class. However, although the selection of positive labeled examples is straightforward, it is very difficult to build a set of negative examples in order to obtain a good set of training samples for a supervised method. In this work, we propose a novel and effective way of approaching this problem using machine learning, without the definition of negative examples. The proposal is based on clustering unlabeled sequences of a genome together with well-known miRNA precursors for the organism under study, which allows for the quick identification of the best candidates to miRNA as those sequences clustered with known precursors. Furthermore, we propose a deep model to overcome the problem of having very few positive class labels. They are always maintained in the deep levels as positive class while less likely pre-miRNA sequences are filtered level after level. Our approach has been compared with other methods for pre-miRNAs prediction in several species, showing effective predictivity of novel miRNAs. Additionally, we will show that our approach has a lower training time and allows for a better graphical navegability and interpretation of the results. A web-demo interface to try deepSOM is available at http://fich.unl.edu.ar/sinc/web-demo/deepsom/.

A new index for clustering validation with overlapped clusters

An index to compare clustering solutions with overlapped groups is proposed.The index is carefully designed with an intuitive probabilistic approach.Results with standard datasets for benchmarking are included.It has been applied also to a real application involving social networks.The index can measure correctly the similarity between clustering solutions. External validation indexes allow similarities between two clustering solutions to be quantified. With classical external indexes, it is possible to quantify how similar two disjoint clustering solutions are, where each object can only belong to a single cluster. However, in practical applications, it is common for an object to have more than one label, thereby belonging to overlapped clusters; for example, subjects that belong to multiple communities in social networks. In this study, we propose a new index based on an intuitive probabilistic approach that is applicable to overlapped clusters. Given that recently there has been a remarkable increase in the analysis of data with naturally overlapped clusters, this new index allows to comparing clustering algorithms correctly. After presenting the new index, experiments with artificial and real datasets are shown and analyzed. Results over a real social network are also presented and discussed. The results indicate that the new index can correctly measure the similarity between two partitions of the dataset when there are different levels of overlap in the analyzed clusters.

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 on 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 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 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.