Integrating bioinformatic strategies in spatial life science research.

Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE

Publication: Oxford : Oxford University Press
Original Publication: London ; Birmingham, AL : H. Stewart Publications, [2000-

Biological Science Disciplines* ; Space Flight*; Computational Biology

As space exploration programs progress, manned space missions will become more frequent and farther away from Earth, putting a greater emphasis on astronaut health. Through the collaborative efforts of researchers from various countries, the effect of the space environment factors on living systems is gradually being uncovered. Although a large number of interconnected research findings have been produced, their connection seems to be confused, and many unknown effects are left to be discovered. Simultaneously, several valuable data resources have emerged, accumulating data measuring biological effects in space that can be used to further investigate the unknown biological adaptations. In this review, the previous findings and their correlations are sorted out to facilitate the understanding of biological adaptations to space and the design of countermeasures. The biological effect measurement methods/data types are also organized to provide references for experimental design and data analysis. To aid deeper exploration of the data resources, we summarized common characteristics of the data generated from longitudinal experiments, outlined challenges or caveats in data analysis and provided corresponding solutions by recommending bioinformatics strategies and available models/tools.
(© The Author(s) 2022. Published by Oxford University Press.)

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31900473 National Natural Science Foundation of China; SMFA19B01 State Key Laboratory of Space Medicine Fundamentals and Application; HYZHXM01004 Space Medical Experiment Project of China Manned Space Program

Keywords: association prediction; computational models; data characteristics; disease; space environment factors

Date Created: 20221005 Date Completed: 20221123 Latest Revision: 20230918

20231215

PMC9677476

10.1093/bib/bbac415

36198665