Ning Yu, Zeng Yu, Bing Li, Feng Gu, Yi Pan, Journal of Information Processing Systems Vol. 12, No. 1, pp. 1-34, Mar. 2016  

https://doi.org/10.3745/JIPS.04.0023
Keywords: Cloud computing, Comparative Methods, Deep Learning, Fourier Transform, Gene Identification, Gene Prediction, Hidden Markov Model, Machine Learning, Protein-Coding Region, Support Vector Machine
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Abstract

Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.

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