#GRAIL DNA SEQUENCE ANALYSIS SOFTWARE#
Loha, S.K., Lowa, S.T., Mohamada, M.S., et al.: A review of software for predicting gene function. Venter, J.C., et al.: The sequence of the human genome. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. Moreover, various parameters for measuring the accuracy of gene prediction programs will also be discussed. Hybrid soft computing approaches combine the results of several soft computing programs to deliver better accuracy than individual single soft computing approaches. Soft computing approaches include Genetic algorithm, Hidden Markov Model, Fast Fourier Transformation, Support vector Machine, Dynamic programming and Artificial Neural Network. This chapter describes various gene structure prediction programmes which are based on individual/hybrid soft computing approaches. Ab intio gene prediction is a difficult method which uses signal and content sensors to make predictions while homology based method makes use of homology with known genes.
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Gene prediction in very crucial especially for disease identification in human, which will help a lot in bio-medical research.
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Gene finding is more difficult in eukaryotes as compared to prokaryotes due to presence of introns. Due to lack of genome annotation and high-throughput experimental approaches, computational gene prediction has always been one of the challenging areas for bioinformatics/computational biology scientists. These public databases contribute in genome annotation which helps in discovering the new genes and finding their function. The flooding of gene sequencing projects lead to the deposition of large amount of genomic data in public databases.