Deep learning in mining biological data

Mahmud, M. ORCID: 0000-0002-2037-8348, Kaiser, M.S., McGinnity, T.M. ORCID: 0000-0002-9897-4748 and Hussain, A., 2021. Deep learning in mining biological data. Cognitive Computation. ISSN 1866-9956

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Abstract

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorised in three broad types (i.e., images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data intensive machine learning techniques. Artificial neural network based learning systems are well known for their pattern recognition capabilities and lately their deep architectures - known as deep learning (DL) - have been successfully applied to solve many complex pattern recognition problems. To investigate how DL - especially its different architectures - has contributed and utilised in the mining of biological data pertaining to those three types, a meta analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures' applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

Item Type: Journal article
Publication Title: Cognitive Computation
Creators: Mahmud, M., Kaiser, M.S., McGinnity, T.M. and Hussain, A.
Publisher: Springer
Date: 5 January 2021
ISSN: 1866-9956
Identifiers:
NumberType
10.1007/s12559-020-09773-xDOI
1382810Other
Divisions: Schools > School of Science and Technology
Record created by: Jill Tomkinson
Date Added: 06 Jan 2021 15:51
Last Modified: 06 Jan 2021 15:51
URI: http://irep.ntu.ac.uk/id/eprint/41954

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