Eleanor A. Howe

Dr. Howe has spent nearly two decades helping teams expand and unlock the possibilities in their data. She engages on the analysis front, rapidly deploying and exploiting both software and statistical techniques to help domain experts understand the patterns and signals in their data. She drops into new areas of biology, builds an understanding of the scientific complexities in the data, and works closely with domain experts to ensure that her analyses are relevant and actionable.
Howe can function as a senior analyst, and also brings the perspective to inform more strategic choices.  When the question is “what shall we do,” rather than “who shall do it,” a technical assessment by Howe provides high value with a very well bounded engagement.
Eleanor is a computational biologist and data scientist with broad experience in using genomics data to inform drug discovery, target identification, target validation, and model selection. She has a background in drug-discovery projects in oncology, cardiovascular disease and rare diseases.

Past work includes:

  • RNA-Seq analysis, both bulk and single-cell, including QC/Normalization, differential expression and pathway analysis. Used for purposes of compound mechanism-of-action studies, discovery biology, biomarker discovery.
  • Functional assay or dropout screen (e.g. Project Achilles) data analysis.
  • Data visualization, including building interactive tools for exploring large datasets or the results of a complex analysis.
  • Use of survival modeling to asses the impact of clinical data and genetic variants on severity of rare disease.

Technical skills:

  • Programming: experience in R (8 year), Java (7 years)
  • Machine learning: clustering, classification, cross-validation
  • Statistics: survival modeling, feature selection, enrichment, marker detection
  • Cloud computing: experience running analyses in EC2/S3, using AWS api


  • Reproducible research: systematic use of self-documenting Rmarkdown to produce analysis reports that can be re-created later.
  • Leadership: extensive mentoring of students of all levels (high school through PhD level), plus management of projects composed of peer contributors and consultants.
  • Informatics: excellent knowledge of informatics, including evaluation of data storage needs for lab experiments and an understanding of how information systems are experienced by users of all levels.