Querying Biological Sequences Docking Using Different Constraint Programming’s: a Survey

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-22 Number-1
Year of Publication : 2015
Authors : B.Mallikarjuna Reddy, P.Chandrasekhar, M.Ramakrishna Reddy


B.Mallikarjuna Reddy, P.Chandrasekhar, M.Ramakrishna Reddy "Querying Biological Sequences Docking Using Different Constraint Programming’s: a Survey". International Journal of Computer Trends and Technology (IJCTT) V22(1):46-52, April 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Technology with its usability varies with time and constraint, for which these day’s we call technology of Software as changing technology. Considering the Human race and its available technological services still yet need to be explored. Hence in the above context, we try to put forward the Biological human constraints like Blood Group, DNA Sample, PH level and many other symptomatic records as recognition of pattern matching by using the data mining tool with soft computing. Different numbers of soft computing tools are available in market that is run based on data mining domain. Combine the different number of soft computing tools using hybridization create the new soft computing tools. Generally Fuzzy set theory handling the issues related understandability patterns, insufficient and noisy data. Fuzzy set theory provides the faster solutions. Machine learning, membership functions, neural networks, artificial intelligence domains has many number of learning methodologies. These domains related techniques are providing the data rich environment solutions. Consider the mixed biological data transactions and we select the different number of approaches. In mixed data extract the useful biological sequences data with subgroup discovery iterative genetic algorithm, Cluster based fuzzy genetic algorithm mining framework, hierarchical fuzzy rule based systems. Some challenges of data mining and application of software computing methodologies.

[1] María José del Jesus, Pedro González, Francisco Herrera, and Mikel Mesonero, Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing in fuzzy systems, VOL. 15, NO. 4, AUGUST 2007, page 578-592.
[2] Chun-Hao Chen, Vincent S. Tseng, Tzung-Pei Hong, Cluster-Based Evaluation in Fuzzy-Genetic Data Mining on Fuzzy Systems, VOL. 16, NO. 1, FEBRUARY 2008, page 249 -262.
[3] Alberto Fernández, Victoria López, María José del Jesus, and Francisco Herrera, On the Usefulness of Fuzzy Rule Based Systems Based on Hierarchical Linguistic Fuzzy Partitions, Granular Computing and Intell. Sys., ISRL 13, pp. 155–184, 2011
[4] Ludwig Krippahl and Fábio Madeira, Improving Protein Docking with Constraint Programming and Coevolution Data, the preprint for biology, http://biorxiv.org/ on April 6, 2014
[5]Sushmitha mitra, Sankar, Pabitra Mitra, Data mining in soft computing framework: A Survey on neural networks, vol13.no.1, January, 2002.
[6] Sara C. Madeira and Arlindo L. Oliveira, Biclustering Algorithms for Biological Data Analysis: A Survey ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. 1, NO. 1, JANUARYMARCH 2004

Fuzzy set theory, membership functions, fuzzy rules, KDD, learning techniques, genetic algorithm.