TY - GEN
T1 - An introduction to back propagation learning and its application in classification of genome data sequence
AU - Patel, Medha J.
AU - Mehta, Devarshi
AU - Paterson, Patrick
AU - Rawal, Rakesh
N1 - Publisher Copyright:
© Springer India 2014.
PY - 2014
Y1 - 2014
N2 - The gene classification problem is still active area of research because of the attributes of the genome data, high dimensionality and small sample size. Furthermore, the underlying data distribution is also unknown, so nonparametric methods must be used to solve such problems. Learning techniques are efficient in solving complex biological problems due to characteristics such as robustness, fault tolerances, adaptive learning and massively parallel analysis capabilities, and for a biological system it may be employed as tool for data-driven discovery. In this paper, some concepts related to cognition by examples are discussed.Aclassification technique is proposed in which DNA sequence is analyzed on the basis of sequence characteristics near breakpoint that occur in leukemia. The training dataset is built for supervised classifier and on the basis of that back propagation learning classifier is employed on hypothetical data. Our intension is to employ such techniques for further analysis and research in this domain. The future scope and investigation is also suggested.
AB - The gene classification problem is still active area of research because of the attributes of the genome data, high dimensionality and small sample size. Furthermore, the underlying data distribution is also unknown, so nonparametric methods must be used to solve such problems. Learning techniques are efficient in solving complex biological problems due to characteristics such as robustness, fault tolerances, adaptive learning and massively parallel analysis capabilities, and for a biological system it may be employed as tool for data-driven discovery. In this paper, some concepts related to cognition by examples are discussed.Aclassification technique is proposed in which DNA sequence is analyzed on the basis of sequence characteristics near breakpoint that occur in leukemia. The training dataset is built for supervised classifier and on the basis of that back propagation learning classifier is employed on hypothetical data. Our intension is to employ such techniques for further analysis and research in this domain. The future scope and investigation is also suggested.
KW - Artificial neural network
KW - Cancer classification
KW - Supervised classifier
UR - http://www.scopus.com/inward/record.url?scp=84928041216&partnerID=8YFLogxK
U2 - 10.1007/978-81-322-1602-5_65
DO - 10.1007/978-81-322-1602-5_65
M3 - Conference contribution
AN - SCOPUS:84928041216
T3 - Advances in Intelligent Systems and Computing
SP - 609
EP - 615
BT - 2nd International Conference on Soft Computing for Problem Solving, SocProS 2012, Proceedings
A2 - Babu, B.V.
A2 - Nagar, Atulya
A2 - Bansal, Jagdish Chand
A2 - Pant, Millie
A2 - Deep, Kusum
A2 - Ray, Kanad
A2 - Gupta, Umesh
PB - Springer-Verlag
Y2 - 28 December 2012 through 30 December 2012
ER -