ArticleNPJ microgravity2024
Harmonizing heterogeneous transcriptomics datasets for machine learning-based analysis to identify spaceflown murine liver-specific changes.
Article in NPJ microgravity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Impact of Microgravity on Cytoskeletal Dynamics, Protein Transport, and Signaling Networks: Potential Therapeutic Opportunities for Skin Health.International journal of molecular sciences · 2026Review
- Beyond Earth: Recent Advancements in Microgravity Biomedical and Genetic Research in Saudi Arabia.International journal of molecular sciences · 2026Review
- From Collapse to Active Self-Repair: Integrative Multi-Omics and Machine Learning Analysis Map the Hepatic Metabolic Adaption in Response to Simulated Spaceflight Stress.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer.Nature communications · 2026Article
- Analyzing the relationship between gene expression and phenotype in space-flown mice using a causal inference machine learning ensemble.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
NASA has employed high-throughput molecular assays to identify sub-cellular changes impacting human physiology during spaceflight. Machine learning (ML) methods hold the promise to improve our ability to identify important signals within highly dimensional molecular data. However, the inherent limitation of study subject numbers within a spaceflight mission minimizes the utility of ML approaches. To overcome the sample power limitations, data from multiple spaceflight missions must be aggregated while appropriately addressing intra- and inter-study variabilities. Here we describe an approach to log transform, scale and normalize data from six heterogeneous, mouse liver-derived transcriptomics datasets (n
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.