Clinical and Experimental Pediatrics

The Korean Pediatric Society (대한소아청소년과학회)
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Knowledge-guided artificial intelligence technologies for decoding complex multiomics interactions in cells

  • Lee, Dohoon (Bioinformatics Institute, Seoul National University) ;
  • Kim, Sun (Interdisciplinary Program in Bioinformatics, Seoul National University)
  • Received : 2021.09.15
  • Accepted : 2021.10.21
  • Published : 2022.05.15
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Abstract

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

Keywords

Acknowledgement

This research was supported by the Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (MSIT) (NRF-2014M3C9A3063541); a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health & Welfare, Republic of Korea (HI15C3224) and the Bio & Medical Technology Development Program of the NRF (NRF-2019M3E5D4065965); and a grant from the Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Korean government(MSIT) (no. 2021-0-01343, Artificial Intelligence Graduate School Program, Seoul National University).

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