[IJCST-V10I1P5]:C. Sunitha Ram, Swetha Gayathri Kuchimanchi

IAEME PUBLICATION, 2013

Microarray gene expression datasets are more preferred form of disease diagnostic system than morphology. But some of the real world gene expression dataset consists of numerous missing values due to insufficient resolution, image corruption, dust or scratches on the slides or experimental error during the laboratory process. To apply any computational method for analyzing such missing valued data results in loss of biological meaning. To overcome this difficulty, this paper proposes a method “local least square imputation” for identification of missing values. This method uses local similarity structures and performs least square optimization for finding out the missing values. Further this paper uses multiclass gene expression data as they are rarely addressed in the literature. Fuzzy Rough set based f-information measure is used for gene selection and SVM is used for sample classification. From the simulation study, it is found that the proposed approach enhances the classification accuracy without the loss of its inherent biological meaning. Statistical analysis of the test result shows that the proposed method outperforms other approaches reported.

Informatica (lithuanian Academy of Sciences), 2004

Extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing, accessing, analysis, and effectiveness usage of stored knowledge and data. Intelligent methods such as neural networks, fuzzy sets, decision trees, and expert systems are, slowly but steadily, applied in the medical fields. Recently, rough set theory is a new intelligent technique was used for the discovery of data dependencies, data reduction, approximate set classification, and rule induction from databases.

2005

Classification of voluminous protein data based on the structural and functional properties is a challenging task for researchers in bioinformatics field. In this paper a faster, accurate and efficient classification tool Rough Set Protein Classifier has been developed which has a classification accuracy of 97.7%. It is a hybridized tool comprising Sequence Arithmetic, Rough Set Theory and Concept Lattice. It reduces the domain search space to 9% without losing the potentiality of classification of proteins.

2015

Soft computing is a consortium of techniques that work together to setup flexible information processing capability for handling real-life ambiguous situations. It aims at solving problems involving uncertainty and imprecision mimicking the human like decision making. Fuzzy set theory is an approach that has been widely adopted in such situations. Rough Set Theory (RST) is another soft computing approach that uses sets to represent vague or incomplete knowledge and provide a framework for approximation of concepts. It has been widely used to deal with imprecision in health sciences such as in patient diagnosis and disease classification. In this paper we present a review of rough set theory and its applications in disease diagnosis with several examples using real data sets. Key-Words: Rough Set Theory, Soft Computing, Vague data, Imprecision, Health Sciences, Disease diagnosis

International Journal of System Dynamics Applications, 2013

As the micro array databases increases in dimension and results in complexity, identifying the most informative genes is a challenging task. Such difficulty is often related to the huge number of genes with very few samples. Research in medical data mining addresses this problem by applying techniques from data mining and machine learning to the micro array datasets. In this paper Unsupervised Tolerance Rough Set based Quick Reduct (U-TRS-QR), a diverse feature selection algorithm, which extends the existing equivalent rough sets for unsupervised learning, is proposed. Genes selected by the proposed method leads to a considerably improved class predictions in wide experiments on two gene expression datasets: Brain Tumor and Colon Cancer. The results indicate consistent improvement among 12 classifiers.

2009

Rough sets offer an effective approach of managing uncertainties and can be employed for tasks such as data dependency analysis, feature identification, dimensionality reduction, and pattern classification. As these tasks are common in many medical applications it is only natural that rough sets, despite their relative 'youth'compared to other techniques, provide a suitable method in such applications.

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010

Several information measures such as entropy, mutual information, and f-information have been shown to be successful for selecting a set of relevant and nonredundant genes from a high-dimensional microarray data set. However, for continuous gene expression values, it is very difficult to find the true density functions and to perform the integrations required to compute different information measures. In this regard, the concept of the fuzzy equivalence partition matrix is presented to approximate the true marginal and joint distributions of continuous gene expression values. The fuzzy equivalence partition matrix is based on the theory of fuzzy-rough sets, where each row of the matrix represents a fuzzy equivalence partition that can automatically be derived from the given expression values. The performance of the proposed approach is compared with that of existing approaches using the class separability index and the predictive accuracy of the support vector machine. An important finding, however, is that the proposed approach is shown to be effective for selecting relevant and nonredundant continuous-valued genes from microarray data.

Multiple DNA Sequence alignment is one of the most important bioinformatics problems where the search space is too large for exact algorithms to be possible. This paper proposes a new approach for multiple sequence alignment using a hybrid model of Genetic Algorithm and Rough Sets. Genetic Algorithms try to evolve initial alignment to find better one with better similarity score. The resulting alignment of genetic algorithm contain gaps in random locations of sequences and these gaps are translated to missing bases when the alignment is converted to suitable representation for Rough Sets analysis. Rough sets try to cluster DNA sequence in the presence of missing bases and build the Modified Similarity Relation that is dependent on the number of missing bases with respect to the number of the whole defined attributes for each sequence. The discernibility matrix has been constructed to compute the minimal sets of reducts, which used to extract the minimal sets of decision rules that describe similarity relations between sequences. These rules are used later as basis to compare between sequences, rule-to-rule rather than residue-to-residue comparing. Fragments of DNA sequences of swine influenza A (H1N1) virus are aligned using our hybrid model of Genetic Algorithm and Rough Sets.

2002

Biological research is currently undergoing a revolution. With the advent of microarray technology the behavior of thousands of genes can be measured simultaneously. This capability opens a wide range of research opportunities in biology, but the technology generates a vast amount of data that cannot be handled manually. Computational analysis is thus a prerequisite for the success of this technology, and research and development of computational tools for microarray analysis are of great importance.

Fuzzy Systems, 2017

This chapter aims to study the use of Hybridization of intelligent techniques in the areas of bioinformatics and computational molecular biology. These areas have risen from the needs of biologists to utilize and help interpret the vast amounts of data that are constantly being gathered in genomic research. Also describes the kind of methods which were developed by the research community in order to search, classify and mine different available biological databases and simulate biological experiments. This chapter also presents the hybridization of intelligent systems involving neural networks, fuzzy systems, neuro-fuzzy system, rough set theory, swam intelligence and genetic algorithm. The key idea was to demonstrate the evolution of intelligence in bioinformatics. The developed hybridization of intelligent techniques was applied to the real world applications. The hybridization of intelligent systems performs better than the individual approaches. Hence these approaches might be e...