Dear AWG Members,
I’m introducing a new project idea for the AI/ML AWG focused on mapping molecular signaling across disparate tissues. While most current spaceflight transcriptomic analyses are siloed to single organs, this project aims to leverage multi-modal machine learning to identify systemic “cross-talk” patterns.
Summary Spaceflight induces complex physiological adaptations that are fundamentally interconnected. The OSD-914 (RR-8) dataset provides a unique opportunity for systemic modeling, as it contains small RNA data across 13 different organs for the same subjects. This project will utilize this high-dimensional dataset to determine if molecular stress signatures in peripheral tissues (e.g., Liver) can serve as predictive biomarkers for adaptations in the Central Nervous System (Brain).
Research Question: Can a multi-modal neural network architecture identify predictive transcriptomic signaling links between hepatic metabolic stress and neuro-inflammatory responses in spaceflight models? The hypothesis is that systemic signaling creates identifiable correlations across the OSD-914 organ-wide profiles that are missed by traditional single-tissue differential expression analysis.
Deliverables: The expected deliverables include:
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A standardized cross-tissue data matrix derived from OSD-914.
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An open-source AI pipeline (Python/PyTorch) for multi-organ predictive modeling.
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Explainability maps (SHAP/LIME) identifying the specific miRNA families driving inter-organ correlations.
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A manuscript submission to a peer-reviewed journal (e.g., Nature Scientific Data or Life).
I am looking for collaborators with expertise in Systems Biology, Transcriptomics, or ML Optimization to help with biological validation and feature selection.
If you’re interested in collaborating on this systemic approach, please reply here or reach out directly!
Best regards,
Pratheek Mukkavilli
NASA AI/ML AWG Member
@AIMLawg