Angiogenesis goes computational – The future way forward to discover new angiogenic targets?

Abhishek Subramanian, Pooya Zakeri, Mira Mousa, Halima Alnaqbi, Fatima Yousif Alshamsi, Leo Bettoni, Ernesto Damiani, Habiba Alsafar, Yvan Saeys, Peter Carmeliet

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations

Abstract

Multi-omics technologies are being increasingly utilized in angiogenesis research. Yet, computational methods have not been widely used for angiogenic target discovery and prioritization in this field, partly because (wet-lab) vascular biologists are insufficiently familiar with computational biology tools and the opportunities they may offer. With this review, written for vascular biologists who lack expertise in computational methods, we aspire to break boundaries between both fields and to illustrate the potential of these tools for future angiogenic target discovery. We provide a comprehensive survey of currently available computational approaches that may be useful in prioritizing candidate genes, predicting associated mechanisms, and identifying their specificity to endothelial cell subtypes. We specifically highlight tools that use flexible, machine learning frameworks for large-scale data integration and gene prioritization. For each purpose-oriented category of tools, we describe underlying conceptual principles, highlight interesting applications and discuss limitations. Finally, we will discuss challenges and recommend some guidelines which can help to optimize the process of accurate target discovery.

Original languageBritish English
Pages (from-to)5235-5255
Number of pages21
JournalComputational and Structural Biotechnology Journal
Volume20
DOIs
StatePublished - Jan 2022

Keywords

  • Angiogenesis
  • Biological networks
  • Functional enrichment
  • Gene prioritization
  • Single-cell multi-omics
  • Unsupervised and supervised data fusion

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