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.

Biclustering Gene Expression Data using MSR Difference Threshold

Biclustering is simultaneous clustering of both rows and columns of a data matrix. A measure called Mean Squared Residue (MSR) is used to simultaneously evaluate the coherence of rows and columns within a submatrix. In this paper a novel algorithm is developed for biclustering gene expression data using the newly introduced concept of MSR difference threshold. In the first step high quality bicluster seeds are generated using K-Means clustering algorithm. Then more genes and conditions (node) are added to the bicluster. Before adding a node the MSR X of the bicluster is calculated. After adding the node again the MSR Y is calculated. The added node is deleted if Y minus X is greater than MSR difference threshold or if Y is greater than MSR threshold which depends on the dataset. The MSR difference threshold is different for gene list and condition list and it depends on the dataset also. Proper values should be identified through experimentation in order to obtain biclusters of high quality. The results obtained on bench mark dataset clearly indicate that this algorithm is better than many of the existing biclustering algorithms

Preparation and Characterisation of Spinel Ferrites - Their Incorporation in Rubber Matrix and Evaluation of Properties

The objectives of the proposed work are preparation of ceramic nickel zinc ferrite belonging to the series Ni1-XZnXFe2O4 with x varying from 0 to 1in steps of 0.2, structrural, magnetic and electrical characterization of Ni1-XZnXFe2O4, preparation and evaluation of Cure characteristics of Rubber Ferrite Composites (RFCs), magnetic characterization of RFCs using vibrating sample magnetometer (VSM), electrical characterization of RFCs and estimation of magnetostriction constant form HL parameters. The study deals with the structural and magnetic properties of ceramic fillers, variation of coercivity with composition and the variation of magnetization for different filler loadings are compared and correlated. The dielectric properties of ceramic Ni1-XZnXFe2O4 and rubber ferrite composites containing Ni1-XZnXFe2O4 were evaluated and the ac electrical conductivity (ac) of ceramic as well as composite samples can be calculated by using a simple relationship of the form ac = 2f tan 0r, with the data available from dielectric measurements. The results suggest that the ac electrical conductivity is directly proportional to the frequency

Laser-induced thermal characterization of nano Ag metal dispersed ceramic alumina matrix

In this paper, we report the measurements of thermal diffusivity of nano Ag metal dispersed ceramic alumina matrix sintered at different temperatures using laser induced non-destructive photoacoustic technique. Measurements of thermal diffusivity also have been carried out on specimens with various concentration of nano metal. Analysis of the data is done on the basis of one-dimensional model of Rosencwaig and Gersho. The present measurements on the thermal diffusivity of nano metal dispersed ceramic alumina shows that porosity has a great influence on the heat transport and the thermal diffusivity value. The present analysis also shows that the inclusion of nano metal into ceramic matrix increases its interconnectivity and hence the thermal diffusivity value. The present study on the samples sintered at different temperature shows that the porosity of the ceramics varies considerably with the change in sintering temperature. The results are interpreted in terms of phonon assisted heat transfer mechanism and the exclusion of pores with the increase in sintering temperature.

Laser-induced thermal characterization of nano Ag metal dispersed ceramic alumina matrix

In this paper, we report the measurements of thermal diffusivity of nano Ag metal dispersed ceramic alumina matrix sintered at different temperatures using laser induced non-destructive photoacoustic technique. Measurements of thermal diffusivity also have been carried out on specimens with various concentration of nano metal. Analysis of the data is done on the basis of one-dimensional model of Rosencwaig and Gersho. The present measurements on the thermal diffusivity of nano metal dispersed ceramic alumina shows that porosity has a great influence on the heat transport and the thermal diffusivity value. The present analysis also shows that the inclusion of nano metal into ceramic matrix increases its interconnectivity and hence the thermal diffusivity value. The present study on the samples sintered at different temperature shows that the porosity of the ceramics varies considerably with the change in sintering temperature. The results are interpreted in terms of phonon assisted heat transfer mechanism and the exclusion of pores with the increase in sintering temperature

Laser-induced thermal characterization of nano Ag metal dispersed ceramic alumina matrix

In this paper, we report the measurements of thermal diffusivity of nano Ag metal dispersed ceramic alumina matrix sintered at different temperatures using laser induced non-destructive photoacoustic technique. Measurements of thermal diffusivity also have been carried out on specimens with various concentration of nano metal. Analysis of the data is done on the basis of one-dimensional model of Rosencwaig and Gersho. The present measurements on the thermal diffusivity of nano metal dispersed ceramic alumina shows that porosity has a great influence on the heat transport and the thermal diffusivity value. The present analysis also shows that the inclusion of nano metal into ceramic matrix increases its interconnectivity and hence the thermal diffusivity value. The present study on the samples sintered at different temperature shows that the porosity of the ceramics varies considerably with the change in sintering temperature. The results are interpreted in terms of phonon assisted heat transfer mechanism and the exclusion of pores with the increase in sintering temperature

Qualitative Air Flow Modelling and Analysis of Data Centre Air Conditioning as Multiple Jet Array

Data centre is a centralized repository,either physical or virtual,for the storage,management and dissemination of data and information organized around a particular body and nerve centre of the present IT revolution.Data centre are expected to serve uniinterruptedly round the year enabling them to perform their functions,it consumes enormous energy in the present scenario.Tremendous growth in the demand from IT Industry made it customary to develop newer technologies for the better operation of data centre.Energy conservation activities in data centre mainly concentrate on the air conditioning system since it is the major mechanical sub-system which consumes considerable share of the total power consumption of the data centre.The data centre energy matrix is best represented by power utilization efficiency(PUE),which is defined as the ratio of the total facility power to the IT equipment power.Its value will be greater than one and a large value of PUE indicates that the sub-systems draw more power from the facility and the performance of the data will be poor from the stand point of energy conservation. PUE values of 1.4 to 1.6 are acievable by proper design and management techniques.Optimizing the air conditioning systems brings enormous opportunity in bringing down the PUE value.The air conditioning system can be optimized by two approaches namely,thermal management and air flow management.thermal management systems are now introduced by some companies but they are highly sophisticated and costly and do not catch much attention in the thumb rules.

A Compiler Integrated Assistance for Optimum Data Allocation in Banked Memory Embedded Processors

Bank switching in embedded processors having partitioned memory architecture results in code size as well as run time overhead. An algorithm and its application to assist the compiler in eliminating the redundant bank switching codes introduced and deciding the optimum data allocation to banked memory is presented in this work. A relation matrix formed for the memory bank state transition corresponding to each bank selection instruction is used for the detection of redundant codes. Data allocation to memory is done by considering all possible permutation of memory banks and combination of data. The compiler output corresponding to each data mapping scheme is subjected to a static machine code analysis which identifies the one with minimum number of bank switching codes. Even though the method is compiler independent, the algorithm utilizes certain architectural features of the target processor. A prototype based on PIC 16F87X microcontrollers is described. This method scales well into larger number of memory blocks and other architectures so that high performance compilers can integrate this technique for efficient code generation. The technique is illustrated with an example