A visual analytics framework for cluster analysis of DNA microarray data

Prova tipográficaCluster analysis of DNA microarray data is an important but difficult task in knowledge discovery processes. Many clustering methods are applied to analysis of data for gene expression, but none of them is able to deal with an absolute way with the challenges that this technology raises. Due to this, many applications have been developed for visually representing clustering algorithm results on DNA microarray data, usually providing dendrogram and heat map visualizations. Most of these applications focus only on the above visualizations, and do not offer further visualization components to the validate the clustering methods or to validate one another. This paper proposes using a visual analytics framework in cluster analysis of gene expression data. Additionally, it presents a new method for finding cluster boundaries based on properties of metric spaces. Our approach presents a set of visualization components able to interact with each other; namely, parallel coordinates, cluster boundary genes, 3D cluster surfaces and DNA microarray visualizations as heat maps. Experimental results have shown that our framework can be very useful in the process of more fully understanding DNA microarray data. The software has been implemented in Java, and the framework is publicly available at http://www. analiticavisual.com/jcastellanos/3DVisualCluster/3D-VisualCluster.This work has been partially funded by the Spanish Ministry of Science and Innovation, the Plan E from the Spanish Government, the European Union from the ERDF (TIN2009-14057-C03-02)

Intelligent business processes composition based on multi-agent systems

This paper proposes a novel model for automatic construction of business processes called IPCASCI (Intelligent business Processes Composition based on multi-Agent systems, Semantics and Cloud Integration). The software development industry requires agile construction of new products able to adapt to the emerging needs of a changing market. In this context, we present a method of software component reuse as a model (or methodology), which facilitates the semi-automatic reuse of web services on a cloud computing environment, leading to business process composition. The proposal is based on web service technology, including: (i) Automatic discovery of web services; (ii) Semantics description of web services; (iii) Automatic composition of existing web services to generate new ones; (iv) Automatic invocation of web services. As a result of this proposal, we have presented its implementation (as a tool) on a real case study. The evaluation of the case study and its results are proof of the reliability of IPCASCI

An Evolutionary and Visual Framework for Clustering of DNA Microarray Data

This paper presents a case study to show the competence of our evolutionary and visual framework for cluster analysis of DNA microarray data. The proposed framework joins a genetic algorithm for hierarchical clustering with a set of visual components of cluster tasks given by a tool. The cluster visualization tool allows us to display different views of clustering results as a means of cluster visual validation. The results of the genetic algorithm for clustering have shown that it can find better solutions than the other methods for the selected data set. Thus, this shows the reliability of the proposed framework

Determining the maximum length of logical rules in a classifier and visual comparison of results

Supervised learning problems can be faced by using a wide variety of approaches supported in machine learning. In recent years there has been an increasing interest in using the evolutionary computation paradigm as the classifier search method, helping the technique of applied machine learning. In this context, the knowledge representation in form of logical rules has been one of the most accepted machine learning approaches, because of its level of expressiveness. This paper proposes an evolutionary framework for rule-based classifier induction and is based on the idea of sequential covering. We introduce genetic programming as the search method for classification-rules. From this approach, we have given results on subjects as maximum rule length, number of rules needed in a classifier and the rule intersection problem. The experiments developed on benchmark clinical data resulted in a methodology to follow in the learning method evaluation. Moreover, the results achieved compared to other methods have shown that our proposal can be very useful in data analysis and classification coming from the medical domain.•The method is based on genetic programming techniques to find rules holding each class in a dataset.•The method is approached to solve the problem of rule intersection from different classes.•The method states the maximum length of a rule to generalize.This work has been carried out under the iCIS project ( CENTRO-07-ST24-FEDER-0 020 03 ), which has been co-financed by QREN, in the scope of the Mais Centro Program and European Union’s FEDER. This work has also been partially supported by the Interreg V-A Spain-Portugal Program (PocTep) and the European Regional Development Fund (ERDF) under the IOTEC project (Grant 0123 IOTEC 3 E). This work has also been supported by the Virtual-Ledgers: Virtual-Ledgers-Tecnologías DLT/Blockchain y Cripto-IOT Project, Junta de Castilla (SA267P18) y León and Project La desigualdad económica en la España contemporánea y sus efectos en los mercados, las empresas y el acceso a los recursos naturales y la tierra, Ministerio de Economía y Competitividad (MEIC HAR2016-75010-R). Reference

A Data Mining Approach Applied to Wireless Sensor Neworks in Greenhouses

[EN] This research presents an innovative multi-agent system based on virtual organizations. It has been designed to manage the information collected by wireless sensor networks for knowledge discovery and decision making in greenhouses. The developed multi-agent system allowed us to take decisions on the basis of the analysis of the historical data obtained from sensors. The proposed approach improves the efficiency of greenhouses by optimizing the use of resources

Intelligent multi-agent system for water reduction in automotive irrigation processes

This paper deals with a multi-agent system (MAS) to automate the gathering and managing of information from potato crops in order to provide a precision irrigation system. The proposal and development of a novel MAS is presented based on different agent subsystems with specific objectives to meet the main objective of the global MAS. The proposed MAS has been developed on the Cloud Computing paradigm and is able to gather data from wireless sensor networks (WSNs) located in potato crops for knowledge discovery and decision making. According to the collected information as historical data by the MAS, it can make decision on an actuator set that modify the irrigation system by updating the areas of the crop with most irrigation needs. The use of these intelligent technologies in rural areas provides a considerable saving of resources and improves the efficiency and effectiveness of agricultural production systems. The architecture has been tested in an agricultural environment in order to optimize irrigation in a potato crop. The results showed a significant reduction in comparison to traditional automotive irrigation.This work was developed as part of “Virtual-Ledgers-Tecnologíıas DLT/Blockchainy Cripto-IOT sobre organizaciones virtuales de agentes ligeros y su aplicación en la eficiencia en el transporte de última milla”, IDSA267P18, project cofinanced by Junta Castilla y León, Consejería de Educación,and FEDER funds. The research of Yeray Mezquita is supported by the pre-doctoral fellowship from the University of Salamanca and Banco Santander

A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis

This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by our proposal on the involved datasets have been very promising when used in classification tasks and compared with other methods

A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis

This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by our proposal on the involved datasets have been very promising when used in classification tasks and compared with other methods

A Framework for Knowledge Discovery from Wireless Sensor Networks in Rural Environments: A Crop Irrigation Systems Case Study

This paper presents the design and development of an innovative multiagent system based on virtual organizations. The multiagent system manages information from wireless sensor networks for knowledge discovery and decision making in rural environments. The multiagent system has been built over the cloud computing paradigm to provide better flexibility and higher scalability for handling both small- and large-scale projects. The development of wireless sensor network technology has allowed for its extension and application to the rural environment, where the lives of the people interacting with the environment can be improved. The use of “smart” technologies can also improve the efficiency and effectiveness of rural systems. The proposed multiagent system allows us to analyse data collected by sensors for decision making in activities carried out in a rural setting, thus, guaranteeing the best performance in the ecosystem. Since water is a scarce natural resource that should not be wasted, a case study was conducted in an agricultural environment to test the proposed system’s performance in optimizing the irrigation system in corn crops. The architecture collects information about the terrain and the climatic conditions through a wireless sensor network deployed in the crops. This way, the architecture can learn about the needs of the crop and make efficient irrigation decisions. The obtained results are very promising when compared to a traditional automatic irrigation system