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Lauren M. Sanders

Project Scientist, NASA Open Science Data Repository

Contents

E-mail: lauren.m.sanders@nasa.gov
Affiliation: NASA Ames Space Biosciences Research Branch

Biography

Dr. Lauren Sanders is the Project Scientist for the NASA Open Science Data Repository (OSDR). Her work focuses on ensuring the scientific integrity of the space biological data captured in OSDR and maintaining the findability, accessibility, interoperability, and reusability (FAIR) of the database. Dr. Sanders is also the co-lead of the Artificial Intelligence for Life in Space initiative (AI4LS). The AI4LS team uses advanced computational methods such as machine learning, deep learning, and artificial intelligence to model, predict, and mitigate spaceflight risks. Dr. Sanders holds a Ph.D. in Biomolecular Engineering and Bioinformatics from UC Santa Cruz, where her thesis focused on multi-omic analyses of cancer data and 3D organoid research on the developmental origins of pediatric brain cancers.

Education

Ph.D. Biomolecular Engineering and Bioinformatics, University of California, Santa Cruz, 2020

B.A. Molecular and Cell Biology; B.A. Classical Languages, University of California, Berkeley, 2014

Professional Links

Selected Publications and White Papers

  1. Sanders LM, Chok H, Samson F, Acuna AU, Polo S-HL, Boyko V, et al. Batch effect correction methods for NASA GeneLab transcriptomic datasets. Frontiers in Astronomy and Space Sciences. 2023;10. doi:10.3389/fspas.2023.1200132
  2. Morris JH, Soman K, Akbas RE, Zhou X, Smith B, Meng EC, … Sanders LM … et al. The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information. Bioinformatics. 2023;39. doi:10.1093/bioinformatics/btad080
  3. Sanders LM, Scott RT, Yang JH, Qutub AA, Martin HG, Berrios DC, et al. Biological Research and Self-Driving Labs in Deep Space supported by Artificial Intelligence. Nature Machine Intelligence. 2023.
  4. Scott RT, Sanders LM, Antonsen EL, Hastings JJA, Park S-M, Mackintosh G, et al. Biomonitoring and Precision Health in Deep Space supported by Artificial Intelligence. Nature Machine Intelligence. 2023.
  5. Sanders LM, Chandra R, Zebarjadi N, Beale HC, Lyle AG, Rodriguez A, et al. Machine learning multi-omics analysis reveals cancer driver dysregulation in pan-cancer cell lines compared to primary tumors. Communications Biology. 2022;5: 1–11.
  6. Reynolds RJ, Scott RT, Turner RT, Iwaniec UT, Bouxsein ML, Sanders LM, et al. Validating Causal Diagrams of Human Health Risks for Spaceflight: An Example Using Bone Data from Rodents. Biomedicines. 2022;10: 2187.
  7. Duckworth P, O’Donoghue O, Scheibenreif L, Ughi G, Hoarfrost A, Budd S, … Sanders LM. Federated causal inference for out-of-distribution generalization in predicting physiological effects of radiation exposure. Frontier Development Lab Technical Memorandum; 2021 Aug. Available: https://drive.google.com/file/d/1xM8USgeYs-NNbB83JSUl4EnfMG09W1Se/view
  8. Scott RT, Sanders LM, Berrios DC, Gebre S, Lopez D, Costes SV. Open Science for the Next Decade of Life and Physical Sciences Research for Deep Space Exploration. A White Paper Submitted on 23 December 2021 to the Committee on Biological and Physical Sciences in Space for the 2023-2032 Decadal Survey. 2021. Available: http://surveygizmoresponseuploads.s3.amazonaws.com/fileuploads/623127/6392490/103-2bed5af5dd4cc17654149c1d1ac3f34b_ScottRyanT.pdf
  9. Sanders LM, Scott RT, Costes SV. Machine Learning, Artificial Intelligence and Data Modeling for the Next Decade of Space Biology Research and Astronaut Health Support. A White Paper Submitted on 23 December 2021, to the Committee on Biological and Physical Sciences in Space for the 2023-2032 Decadal Survey. 2021. Available: http://surveygizmoresponseuploads.s3.amazonaws.com/fileuploads/623127/6392490/153-d4da81bc2b0f81447a9adb0dcb878e64_SandersLaurenM.pdf
  10. Sanders LM, Qutub A. Topical: Development of new algorithms for space biology. Life and Physical Sciences 2023-2032 Decadal Topical. 2021. Available: http://surveygizmoresponseuploads.s3.amazonaws.com/fileuploads/623127/6378869/217-4826dc0ada8fb5a9ec7d5a1167b9523b_SandersLaurenM.pdf
  11. Sanders LM, Cheney A, Seninge L, van den Bout A, Chen M, Beale HC, et al. Identification of a differentiation stall in epithelial mesenchymal transition in histone H3-mutant diffuse midline glioma. Gigascience. 2020;9. doi:10.1093/gigascience/giaa136
  12. Pfeil J, Sanders LM, Anastopoulos I, Lyle AG, Weinstein AS, Xue Y, et al. Hydra: A mixture modeling framework for subtyping pediatric cancer cohorts using multimodal gene expression signatures. PLoS Comput Biol. 2020;16: e1007753.