Building bioinformatics web applications with Streamlit

Chanin Nantasenamat, Avratanu Biswas, J. M. Nápoles-Duarte, Mitchell I. Parker, Roland L. Dunbrack

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

1 Scopus citations

Abstract

In recent years, we have witnessed an exponential growth in the generation of data in the biological sciences. To harness such big biological data, computational and machine learning pipelines have become instrumental for exploratory data analysis, predictive modeling, and data-driven decision-making. Open-source web development frameworks like Streamlit enable the scientific community to build and share web applications by using less code, thereby speeding up research and development. This chapter explores the usage of Streamlit for the development of software and tools in the field of bioinformatics. The chapter also highlights and goes into depth on selected use cases.

Original languageEnglish
Title of host publicationCheminformatics, QSAR and Machine Learning Applications for Novel Drug Development
PublisherElsevier
Pages679-699
Number of pages21
ISBN (Electronic)9780443186387
ISBN (Print)9780443186394
DOIs
StatePublished - Jan 1 2023

Keywords

  • Bioinformatics
  • Cheminformatics
  • Drug discovery
  • Streamlit
  • Web applications

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