Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

INFO2009 2012-13 Resource Group 6 Flashcards

An inspirational and educational flashcard resource for secondary school children. Can be used as flashcards or as a matching activity (depending on how cards are cut out).

COMP1205 Student Presentations M-P

Tuesday 13th May Building 34 Room 3001, 16.15-17.45 Elena & Rikki/Jian Presenting: Groups: M, N, O, P Marking Groups: Q, R, S, T Schedule and Topics 16.15-16.20: Introduction and protocol for the session 16.20 Group M: Serious games – gaming as a driver for applications online 16.40 Group N: Open Education OERs 17.00 Group O: Big Data – the big picture 17.20 Group P: Rights and equality in the workplace 17.40-18.00: Wash-up: feedback session for presentation groups

WAIS Seminar: Research Discussion: Open Data at the Hampshire Hub

This is a research discussion about the Hampshire Hub - see http://protohub.net/. The aim is to find out more about the project, and discuss future collaboration and sharing of ideas. Mark Braggins (Hampshire Hub Partnership) will introduce the Hampshire Hub programme, setting out its main objectives, work done to-date, next steps including the Hampshire data store (which will use the PublishMyData linked data platform), and opportunities for University of Southampton to engage with the programme , including the forthcoming Hampshire Hackathons Bill Roberts (Swirrl) will give an overview of the PublishMyData platform, and how it will help deliver the objectives of the Hampshire Hub. He will detail some of the new functionality being added to the platform Steve Peters (DCLG Open Data Communities) will focus on developing a web of data that blends and combines local and national data sources around localities, and common topics/themes. This will include observations on the potential employing emerging new, big data sources to help deliver more effective, better targeted public services. Steve will illustrate this with practical examples of DCLG’s work to publish its own data in a SPARQL end-point, so that it can be used over the web alongside related 3rd party sources. He will share examples of some of the practical challenges, particularly around querying and re-using geographic LinkedData in a federated world of SPARQL end-point.

WAIS Seminar:Let's SoFWIReD!

Title: Let’s SoFWIReD! Time: Wed, 21 May 2014 11:00-11:50 Location: Building 32, Room 3077 Speaker: Dr Sepi Chakaveh Abstract The information age as we know it has its roots in several enabling technologies – most of all the World Wide Web – for the provision of truly global connectivity. The emergence of a Web of Big Data in terms of the publication and analysis of Open Data provides new insights about the impact of the Web in our society. The second most important technology in this regard has been the emergence of streaming processes based on new and innovative compression methods such as MP3 so that audio and video content becomes accessible to everyone on the Web. The SoFWIReD team is developing comprehensive, interoperable platforms for data and knowledge driven processing of Open Data and will investigate aspects of collective intelligence. Insights generated in the project will form the basis for supporting companies through consulting, organisational development, and software solutions so that they can master the collective intelligence transition. The seminar will present how the project addresses the research topics of web observatory, dynamic media objects, crowd-sourced open data and Internet services. At the end of a talk a number of demos will be shown in the context of SoFWIReD’s Dynamic Media Object.

Ciencias de la complejidad: estado actual

Los aportes teóricos y aplicados de la complejidad en economía han tomado tantas direcciones y han sido tan frenéticos en las últimas décadas, que no existe un trabajo reciente, hasta donde conocemos, que los compile y los analice de forma integrada. El objetivo de este proyecto, por tanto, es desarrollar un estado situacional de las diferentes aplicaciones conceptuales, teóricas, metodológicas y tecnológicas de las ciencias de la complejidad en la economía. Asimismo, se pretende analizar las tendencias recientes en el estudio de la complejidad de los sistemas económicos y los horizontes que las ciencias de la complejidad ofrecen de cara al abordaje de los fenómenos económicos del mundo globalizado contemporáneo.

Del re-ensamblaje de lo social a la movilización pública: caso Karina del Pozo

El presente trabajo investigativo tiene como objetivo principal dar respuesta a la interrogante acerca de en qué casos y condiciones las interacciones virtuales contribuyen a conformar movilizaciones reales a partir de la información que circula en redes sociales. Para cumplirlo, en primer lugar, se partió de una revisión teórica de autores trascendentales como Howard Rheingold, Manuel Castells, Antonio Damasio, Pierre Levy o Antonio Negri, lo que dio como resultado un primer hallazgo: el vínculo entre la comunicación, las nuevas redes sociales, los medios tradicionales y las emociones que se gestan en ellos y que pueden hacer eco en el individuo hasta promover su movilización y la acción social. Sobre esta base teórica, el siguiente paso fue determinar cómo aquello se presentaba en un grupo definido de usuarios de redes sociales, concretamente el colectivo que hizo que el caso de Karina del Pozo fuera tendencia. Para ello, con la aplicación de las herramientas que arrojaron datos cuantificables como el Big Data y el Important Data, se procedió al trabajo de campo que constó de dos momentos. El primero de ellos, la fase de recopilación de datos; y el segundo, de análisis e interpretación sobre los resultados obtenidos. Como deducciones del estudio a partir del planteamiento teórico y de la investigación, la movilización hacia un determinado objetivo más allá de las redes sociales, es el discurso y del relato periodístico en medios tradicionales que generaron una empatía narrativa, situando al espectador en un lugar virtualmente cercano al hecho. Estos elementos además de ser una respuesta al asesinato de Karina del Pozo fueron un cuestionamiento a la sociedad y a sus prácticas, al machismo, a la violencia de género, pero además significaron la manifestación de estas mismas condiciones cuando se empezó a culpabilizar a la víctima, atribuyéndole la responsabilidad de los hechos que acabaron con su vida. Estas emociónes, valores, pensamientos o sentimientos similares, reflejados en la adopción de una determinada posición frente a un mismo hecho. En el caso de quienes se movilizaron, se estaría cumpliendo la presencia de emociones que van desde el miedo, la ira, la indignación y en contraposición, la solidaridad y la esperanza por cambiar una realidad a través de la acción.