For an always up-to-date publication list, see my Google Scholar profile or My Bibliography on NCBI.

I aim to make all of my publications available to anyone who is interested. If you cannot access one of these articles because it is behind a journal paywall, first check bioRxiv and PubMedCentral, then contact me by email or Twitter if you can’t find it in either of those places.

  1. Waldron, L. Data and Statistical Methods To Analyze the Human Microbiome. mSystems 3, (2018).

  2. Robertson, M. M. et al. Using Registry Data to Construct a Comparison Group for Programmatic Effectiveness Evaluation - the New York City HIV Care Coordination Program. Am. J. Epidemiol. (2018). doi:10.1093/aje/kwy103

  3. Raimann, J. G. et al. Meta-analysis and commentary: Preemptive correction of arteriovenous access stenosis. Hemodial Int 22, 279–280 (2018).

  4. McIver, L. J. et al. bioBakery: a meta’omic analysis environment. Bioinformatics 34, 1235–1237 (2018).

  5. Geistlinger, L. et al. Widespread modulation of gene expression by copy number variation in skeletal muscle. Sci Rep 8, 1399 (2018).

  6. Ramos, M. et al. Software for the Integration of Multiomics Experiments in Bioconductor. Cancer Res. 77, e39–e42 (2017).

  7. Pasolli, E. et al. Accessible, curated metagenomic data through ExperimentHub. Nat. Methods 14, 1023–1024 (2017).

  8. Gao, J. et al. Multiparametric Quantitative Ultrasound Imaging in Assessment of Chronic Kidney Disease. J Ultrasound Med 36, 2245–2256 (2017).

  9. Waldron, L., Riester, M., Ramos, M., Parmigiani, G. & Birrer, M. The Doppelgänger Effect: Hidden Duplicates in Databases of Transcriptome Profiles. J. Natl. Cancer Inst. 108, (2016).

  10. Waldron, L. & Riester, M. Meta-Analysis in Gene Expression Studies. Methods Mol. Biol. 1418, 161–176 (2016).

  11. Spratt, D. E. et al. Racial/Ethnic Disparities in Genomic Sequencing. JAMA Oncol 2, 1070–1074 (2016).

  12. Re, A., Waldron, L. & Quattrone, A. Control of Gene Expression by RNA Binding Protein Action on Alternative Translation Initiation Sites. PLoS Comput. Biol. 12, e1005198 (2016).

  13. Pasolli, E., Truong, D. T., Malik, F., Waldron, L. & Segata, N. Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights. PLoS Comput. Biol. 12, e1004977 (2016).

  14. Nelms, B. D. et al. CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types. Genome Biol. 17, 201 (2016).

  15. Kannan, L. et al. Public data and open source tools for multi-assay genomic investigation of disease. Brief. Bioinformatics 17, 603–615 (2016).

  16. Gao, J. et al. Shear Wave Elastography of the Spleen for Monitoring Transjugular Intrahepatic Portosystemic Shunt Function: A Pilot Study. J Ultrasound Med 35, 951–958 (2016).

  17. Vathipadiekal, V. et al. Creation of a Human Secretome: A Novel Composite Library of Human Secreted Proteins: Validation Using Ovarian Cancer Gene Expression Data and a Virtual Secretome Array. Clin. Cancer Res. 21, 4960–4969 (2015).

  18. Tyekucheva, S. et al. Comparing Platforms for Messenger RNA Expression Profiling of Archival Formalin-Fixed, Paraffin-Embedded Tissues. J Mol Diagn 17, 374–381 (2015).

  19. Morgan, X. C. et al. Associations between host gene expression, the mucosal microbiome, and clinical outcome in the pelvic pouch of patients with inflammatory bowel disease. Genome Biol. 16, 67 (2015).

  20. Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).

  21. Börnigen, D. et al. A reproducible approach to high-throughput biological data acquisition and integration. PeerJ 3, e791 (2015).

  22. Zhao, S. D., Parmigiani, G., Huttenhower, C. & Waldron, L. Más-o-menos: a simple sign averaging method for discrimination in genomic data analysis. Bioinformatics 30, 3062–3069 (2014).

  23. Waldron, L., Riester, M. & Birrer, M. Molecular subtypes of high-grade serous ovarian cancer: the holy grail? J. Natl. Cancer Inst. 106, (2014).

  24. Waldron, L. et al. Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. J. Natl. Cancer Inst. 106, (2014).

  25. Riester, M. et al. Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples. J. Natl. Cancer Inst. 106, (2014).

  26. Re, A., Workman, C. T., Waldron, L., Quattrone, A. & Brunak, S. Lineage-specific interface proteins match up the cell cycle and differentiation in embryo stem cells. Stem Cell Res 13, 316–328 (2014).

  27. Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl. Acad. Sci. U.S.A. 111, E2329-2338 (2014).

  28. Bernau, C. et al. Cross-study validation for the assessment of prediction algorithms. Bioinformatics 30, i105-112 (2014).

  29. Tickle, T. L., Segata, N., Waldron, L., Weingart, U. & Huttenhower, C. Two-stage microbial community experimental design. ISME J 7, 2330–2339 (2013).

  30. Koren, O. et al. A guide to enterotypes across the human body: meta-analysis of microbial community structures in human microbiome datasets. PLoS Comput. Biol. 9, e1002863 (2013).

  31. Hui, A. B. Y. et al. Potentially prognostic miRNAs in HPV-associated oropharyngeal carcinoma. Clin. Cancer Res. 19, 2154–2162 (2013).

  32. Goswami, R. S. et al. MicroRNA signature obtained from the comparison of aggressive with indolent non-Hodgkin lymphomas: potential prognostic value in mantle-cell lymphoma. J. Clin. Oncol. 31, 2903–2911 (2013).

  33. Ganzfried, B. F. et al. curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome. Database (Oxford) 2013, bat013 (2013).

  34. Yamauchi, M. et al. Assessment of colorectal cancer molecular features along bowel subsites challenges the conception of distinct dichotomy of proximal versus distal colorectum. Gut 61, 847–854 (2012).

  35. Waldron, L., Simpson, P., Parmigiani, G. & Huttenhower, C. Report on emerging technologies for translational bioinformatics: a symposium on gene expression profiling for archival tissues. BMC Cancer 12, 124 (2012).

  36. Waldron, L. et al. Expression profiling of archival tumors for long-term health studies. Clin. Cancer Res. 18, 6136–6146 (2012).

  37. Waldron, L., Coller, H. A. & Huttenhower, C. Integrative approaches for microarray data analysis. Methods Mol. Biol. 802, 157–182 (2012).

  38. Singh, N. et al. The murine caecal microRNA signature depends on the presence of the endogenous microbiota. Int. J. Biol. Sci. 8, 171–186 (2012).

  39. Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814 (2012).

  40. Segata, N. et al. Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples. Genome Biol. 13, R42 (2012).

  41. Waldron, L. et al. Optimized application of penalized regression methods to diverse genomic data. Bioinformatics 27, 3399–3406 (2011).

  42. Shi, W. et al. MicroRNA-301 mediates proliferation and invasion in human breast cancer. Cancer Res. 71, 2926–2937 (2011).

  43. Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).

  44. Reis, P. P. et al. A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence. BMC Cancer 11, 437 (2011).

  45. Reis, P. P. et al. mRNA transcript quantification in archival samples using multiplexed, color-coded probes. BMC Biotechnol. 11, 46 (2011).

  46. Eppert, K. et al. Stem cell gene expression programs influence clinical outcome in human leukemia. Nat. Med. 17, 1086–1093 (2011).

  47. Hui, A. B. Y. et al. Comprehensive MicroRNA profiling for head and neck squamous cell carcinomas. Clin. Cancer Res. 16, 1129–1139 (2010).

  48. Goswami, R. S. et al. Optimization and analysis of a quantitative real-time PCR-based technique to determine microRNA expression in formalin-fixed paraffin-embedded samples. BMC Biotechnol. 10, 47 (2010).

  49. Wilkins, O., Waldron, L., Nahal, H., Provart, N. J. & Campbell, M. M. Genotype and time of day shape the Populus drought response. Plant J. 60, 703–715 (2009).

  50. Hui, A. B. Y. et al. Robust global micro-RNA profiling with formalin-fixed paraffin-embedded breast cancer tissues. Lab. Invest. 89, 597–606 (2009).