Active Deep Learning Techniques for Addressing Logical Problems in Bioinformatics
Editorial
Biological data is exceptionally immense and unpredictable in nature. This biological data has tremendously helped scientific research immensely; this enormous data must be saved and examined. The most important challenge of researchers and scientists in this field is to examine and interpret the DNA sequences which contain adenine (A), thymine (T), cytosine (C) and guanine (G). Living organisms consists of cells which consist of numerous complex substances like chromosomes, deoxyribonucleic acid(DNA) and genes that shape the character/identity(heredity) which could be passed on to the different cells. This character/identify serves to figure out certain qualities like what may be cause for disease, hair color, what may be the reason for having six fingers in hand instead of five etc. All these characteristics are referred as genes and the investigation of the genes is known as genetics. Cell is the fundamental building block of life. Every human body contains more than thirty trillion cells. A huge number of genetic instructions are held in these cells which will make proteins. DNA holds the complete hereditary information which can be used in drug discovery and diagnosis. A Deep Learning performs reckonings in a distributed manner on a spatially enlarged grid. It contrasts from the conventional approach [1] to parallel processing in which a problem is divided into independent sub tasks; each one is solved by different Bioinformatics & Proteomics Open Access Journal
References
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Kiran Sree P, Ramesh Babu I, Usha Devi N (2009) Investigating an Artificial Immune System to Strengthen the Protein Structure Prediction and Protein Coding Region Identification using Cellular Automata Classifier**.** International Journal of Bioinformatics Research and Applications 5(6): 647- 662.
-
Kiran Sree, Ramesh Babu (2010) Identification of Promoter Region in Genomic DNA Using Cellular Automata Based Text Clustering. The International Arab Journal of Information Technology (IAJIT) 7(1): 75-78.
-
Kiran Sree P, Usha Devi N (2016) Additive Cellular Automata Augmented with Deep Learning for Pattern Reorganization. J Adv Res Comp Tech Soft Appl 3: 1-3.
-
Kiran Sree P, Ramesh Babu I, Usha Devi N (2014) A Fast Multiple Attractor Cellular Automata with Modified Clonal Classifier for Coding Region Prediction in Human Genome, Journal of Bioinformatics and Intelligent Control 3(2): 128-136.
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Kiran Sree P, Ramesh Babu I, Usha Devi N (2014) A Fast Multiple Attractor Cellular Automata with Modified Clonal Classifier Promoter Region Prediction in Eukaryotes. Journal of Bioinformatics and Intelligent Control 3(2): 123-127.
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