Recent Advances of Deep Learning in Biology

Muhammad Shahid Iqbal*, Iftikhar Ahmad, Tamoor Khan, Suleman Khan, Muneer Ahmad, Lulu Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Citations (Scopus)


The combined influence of new computational tools and techniques with an increase of massive data sets transforms many research fields and can lead to technological breakthroughs that billions of people can make use of it. The past few years have seen remarkable developments in machine learning and especially in deep learning (DL). Techniques developed within those two fields (DL and biology) can now analyze and learn in different formats from a large number of real-world examples. Even though there are a large number of deep learning algorithms, also implemented extensively and are increasing through frameworks and libraries. A large number of open-source applications from academia, business, start-ups, or wider open-source communities speeds up applications development in this area (DL and Biology). This chapter covers a summary of the new concepts and comparisons, as well as developments in deep learning and the use of the biological dataset. It also describes drug-treated and diseased cells capable of effectively scaling computations and efficiently in the era of cell biology. In this chapter, the author introduces deep learning and emerging biological developments, discussion of technology for specifically attraction of deep learning in the biology field. The chapter concludes considering deep learning and current attraction in biology, cell, images, and bioinformatics data set.

Original languageEnglish
Title of host publicationDeep Learning for Unmanned Systems
EditorsAnis Koubaa, Ahmad Taher Azar
Place of PublicationCham
Number of pages24
ISBN (Electronic)9783030779399
ISBN (Print)9783030779382, 9783030779412
Publication statusPublished - 2 Oct 2021

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503


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