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Deep Learning in Mining Biological Data.

Mufti Mahmud1,2, M Shamim Kaiser3, T Martin McGinnity1,4

  • 1Department of Computer Science, Nottingham Trent University, Clifton, NG11 8NS Nottingham, UK.

Cognitive Computation
|January 11, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning (DL) techniques are revolutionizing biological data analysis by uncovering complex patterns in images, signals, and sequences. This study reviews DL applications, tools, and challenges in mining vast multimodal biological datasets.

Keywords:
BioimagingBrain–Machine InterfacesDeep learning performance comparisonMedical imagingOmicsOpen access data sourcesOpen-source tools

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Life sciences generate vast, complex multimodal biological data (images, signals, sequences).
  • Sophisticated machine learning, particularly deep learning (DL), is crucial for pattern recognition in this data.
  • Artificial neural networks and their deep architectures excel at complex pattern recognition tasks.

Purpose of the Study:

  • To investigate the utilization and contribution of various deep learning architectures in mining multimodal biological data.
  • To provide a comprehensive analysis of DL applications across different biological domains.
  • To explore open-access data sources and open-source DL tools for biological data mining.

Main Methods:

  • Meta-analysis of existing research on deep learning in biological data mining.
  • Critical analysis of DL architectures applied to biological images, signals, and sequences.
  • Exploration and comparative investigation of open-source DL tools (qualitative, quantitative, benchmarking).

Main Results:

  • Identified diverse applications of DL architectures for pattern recognition in biological data.
  • Cataloged available open-access biological datasets and popular open-source DL tools.
  • Provided comparative insights into the performance and characteristics of various DL tools.

Conclusions:

  • Deep learning offers powerful capabilities for extracting insights from complex biological data.
  • Availability of open-source tools and data facilitates the application of DL in life sciences.
  • Further research is needed to address open challenges and explore future perspectives in DL for biological data mining.