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2006 International journal on artificial intelligence tools  
Therefore, the fuzzy association rules are extracted locally on a per cluster basis. The paper focuses on the application of the techniques for mining the gene expression data.  ...  The extraction of fuzzy association rules for the description of dependencies and interactions from large data sets as those arising in gene expression data analysis applications perplexes very difficult  ...  Groups of TEI Kavalas" project: "Computational Intelligence techniques for the analysis of Gene Expression Data".  ... 
doi:10.1142/s0218213006002643 fatcat:4i64ac26gnh23ntmgipynk2jke

Classification of Micro Array Gene Expression Proposed using Statistical Approaches

Ms. Selva Mary. G, Asst. Prof. Sachin M. Bojewar
2014 IOSR Journal of Computer Engineering  
In our previous work, microarray gene classification by statistical analysis approach with Fuzzy Inference System (FIS) was proposed for precise classification of genes to their corresponding gene types  ...  Classification analysis of microarray gene expression data has been performed widely to find out the biological features and to differentiate intimately related cell types that usually appear in the diagnosis  ...  A study on effective mining of Association rules from huge databases This paper [3] provides an overview of techniques that are used to improvise the efficiency of Association Rule Mining (ARM) from  ... 
doi:10.9790/0661-16213236 fatcat:ehvzaa6b4nh6tksznhpnwto7ge

Granular Mining and Rough-Fuzzy Pattern Recognition: A Way to Natural Computation

Sankar K. Pal
2012 The IEEE intelligent informatics bulletin  
Bose Fellowship of the Govt. of India, as well as the technical support of Mr. Avatharam Ganivada.  ...  An important use of rough set theory and granular computing in data mining has been in generating logical rules for classification and association.  ...  FUZZY EQUIVALENCE PARTITION MATRIX AND GENE SELECTION An important application of gene expression data in functional genomics is to classify samples according to their gene expression profiles.  ... 
dblp:journals/cib/Pal12 fatcat:rcenhthsqrfibj2ppuarlkjafq

Classification of Micro Array Gene Expression Data using Statistical Analysis Approach with Personalized Fuzzy Inference System

Tamilselvi Madeswaran, G.M.Kadhar Nawaz
2011 International Journal of Computer Applications  
Consequently, the goal is to generate fuzzy rules based on dimensionality reduced data.  ...  Here, we propose a statistical approach for extracting significant genes from the gene expression data set. But, the statistical approach does not correctly identify the important genes.  ...  Some examples for descriptive mining techniques are Clustering, Association Rule Mining and Sequential Pattern mining.  ... 
doi:10.5120/3787-5215 fatcat:q4h3fsm4a5alnggvbw574mj6yy

A Review of Various Methods Used in the Analysis of Functional Gene Expression Data

Houda Fyad, Fatiha Barigou, Karim Bouamrane, Baghdad Atmani
2017 International Journal of Advanced Information Technology  
In this context, this article describes and discusses some techniques used for the functional analysis of gene expression data.  ...  as well as to annotate and to identify the role (function) of those genes.  ...  FUNCTIONAL GENE EXPRESSION DATA ANALYSIS USING DATA MINING To answer the questions of biologists such as: are there clusters according to the genes expression profiles?  ... 
doi:10.5121/ijitcs.2017.7302 fatcat:2tlg3pszqbesbm2ql4sfneppqu

Usage and Research Challenges in the Area of Frequent Pattern in Data Mining

Alagesh Kannan
2013 IOSR Journal of Computer Engineering  
Frequent pattern mining is an important chore in the data mining, which reduces the complexity of the data mining task.  ...  The core area to be concentrated is the minimal representation, contextual analysis and the dynamic identification of the frequent patterns.  ...  Identification of the frequent patterns is a thrust area in the field of data mining which has the applications in association mining, correlation analysis.  ... 
doi:10.9790/0661-1320813 fatcat:tangady5irbazoyqtvzlargq7q

Natural computing methods in bioinformatics: A survey

Francesco Masulli, Sushmita Mitra
2009 Information Fusion  
Often data analysis problems in Bioinformatics concern the fusion of multisensor outputs or the fusion of multi-source information, where one must integrate different kinds of biological data.  ...  We utilize the learning ability of neural networks for adapting, uncertainty handling capacity of fuzzy sets and rough sets for modeling ambiguity, and the search potential of genetic algorithms for efficiently  ...  Gene expression data, coupled with various analysis methods, serves as an indispensable tool for the analysis of protein functions.  ... 
doi:10.1016/j.inffus.2008.12.002 fatcat:hwame6u3svh4jdqb3qfetvzxeq

Food Waste Protein Sequence Analysis using Clustering and Classification Techniques

Vignesh V, KL University, India
2019 International Journal of Advanced Trends in Computer Science and Engineering  
There were many methods followed earlier for the analysis of data but they failed to prove efficiency in higher gathered data and also in noisy data compared to data mining.  ...  This paper clearly describes about the various biological data mining techniques and their inter relations for prediction with the basic concepts of clustering, classification and alignment techniques.  ...  Comparative genomics involves analysis of the gene prospect with the warehouse provided and identify their mutual and distinct perceptions in the gene expression on data and results.  ... 
doi:10.30534/ijatcse/2019/67852019 fatcat:xhzk4p4dofaoxhhsveirebi7by

Fuzzy logic based approaches for gene regulatory network inference [article]

Khalid Raza
2018 arXiv   pre-print
Several computational approaches have been applied for inferring GRN from gene expression data including statistical techniques (correlation coefficient), information theory (mutual information), regression  ...  The fuzzy logic, along with its hybridization with other intelligent approach, is well studied in GRNI due to its several advantages.  ...  The algorithm consists of five stages, starting with gene expression training (stage 1) to extract set of rules (stage 2), sort it (stage 3), and compare the rules for GRN analysis (stage 4), and finally  ... 
arXiv:1804.10775v1 fatcat:ct4yxzdq45ebdobxdsuwb3zlmy

An Efficient Medical Data Classification based on Ant Colony Optimization

Jyotsna Bansal, Divakar Singh, Anju Singh
2014 International Journal of Computer Applications  
In this paper three different dataset named Leukemia, Lung Cancer and Prostate from the UCI machine learning repository are considered and apply efficient association based ant colony optimization for  ...  The data set has been refined according to the attributes. Then final data set is achieved on which we apply the next inabilities.  ...  to efficiently analysis the samples for effective knowledge discovery.  ... 
doi:10.5120/15243-3785 fatcat:oexnsukux5bsjh4lw5ng6m7buu

Negative Information Filtering Algorithm based on Text Content in Multimedia Networks

Chen Wenqing, Fu Weina
2019 International Journal of Performability Engineering  
In the multimedia network environment, it is necessary to effectively filter negative information in the multimedia network and enhance the ability to mine and identify valid data.  ...  Finally, based on the semantic features of text content, the support vector machine algorithm is used to extract negative information features from data.  ...  The mining method of association rules is used to integrate the information of negative information resources, which promotes the improvement of the retrieval efficiency of negative information of multimedia  ... 
doi:10.23940/ijpe.19.11.p26.30613071 fatcat:yyhdr6nwgreijlnv2weifdmrkm

Big Data Analytics in Bioinformatics: A Machine Learning Perspective [article]

Hirak Kashyap, Hasin Afzal Ahmed, Nazrul Hoque, Swarup Roy, Dhruba Kumar Bhattacharyya
2015 arXiv   pre-print
However, there lack standard big data architectures and tools for many important bioinformatics problems, such as fast construction of co-expression and regulatory networks and salient module identification  ...  , detection of complexes over growing protein-protein interaction data, fast analysis of massive DNA, RNA, and protein sequence data, and fast querying on incremental and heterogeneous disease networks  ...  ACKNOWLEDGMENTS The authors would like to thank the Ministry of HRD, Govt. of India for funding as a Centre of Excellence with thrust area in Machine Learning Research and Big Data Analytics for the period  ... 
arXiv:1506.05101v1 fatcat:oix7d5hecbfgthzhepznwyi6fm

Bioinformatics with soft computing

S. Mitra, Y. Hayashi
2006 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)  
The major pattern-recognition and data-mining tasks considered here are clustering, classification, feature selection, and rule generation.  ...  Genomic sequence, protein structure, gene expression microarrays, and gene regulatory networks are some of the application areas described.  ...  Gene expression data being typically high dimensional, it requires appropriate data-mining strategies like feature selection and clustering for further analysis.  ... 
doi:10.1109/tsmcc.2006.879384 fatcat:owim7m6genf6xc7s2bbhjuz7gu

Neural networks and machine learning in bioinformatics - theory and applications

Udo Seiffert, Barbara Hammer, Samuel Kaski, Thomas Villmann
2006 The European Symposium on Artificial Neural Networks  
Despite of a high number of techniques specifically dedicated to bioinformatics problems as well as many successful applications, we are in the beginning of a process to massively integrate the aspects  ...  Within this rather wide area we focus on neural networks and machine learning related approaches in bioinformatics with particular emphasis on integrative research against the background of the above mentioned  ...  In addition, the first author wishes to thank Andrea Matros for the valuable support.  ... 
dblp:conf/esann/SeiffertHKV06 fatcat:jb7m65sxrjcovazeu4fowmv57m


P Arumugam
2017 International Journal of Advanced Research in Computer Science  
This article presents an efficient feature selection based on Modified Fuzzy c-Means clustering with Rough Set Theory (MFCM-RST), the classification will be done based on the SVM classifier.  ...  While the fuzzy set enables efficient handling of overlapping partitions, the concept of rough set deals with uncertainty, vagueness, incompleteness, and indiscernibility in class definition.  ...  A feature selection method based on mutual information is proposed in [21] to select a set of genes from microarray gene expression data.  ... 
doi:10.26483/ijarcs.v8i7.4222 fatcat:hl4wjmodtvhlnlrxqe2rdetvsq
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