Deep Learning in Mining Biological Data

Mufti Mahmud, Shamin Kaiser, T.Martin McGinnity, Amir Hussain

Research output: Contribution to journalArticle

Abstract

Abstract
Background: Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from dierent 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. Articial neural network based learning sys-
tems 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. Methods: 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.
Original languageEnglish
Article numberCOGN-D-20-00103R1
JournalCognitive Computation
Publication statusAccepted/In press - 29 Sep 2020

Keywords

  • Deep learning
  • Biiological Data

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  • Cite this

    Mahmud, M., Kaiser, S., McGinnity, T. M., & Hussain, A. (Accepted/In press). Deep Learning in Mining Biological Data. Cognitive Computation, [COGN-D-20-00103R1].