About

Douglas Ross is a Professor from Practice at the University of Washington School of Law, where he has taught since 2010. His career began with the Antitrust Division at the Department of Justice in Washington, D.C., before he entered private practice in Seattle with Davis Wright Tremaine LLP, focusing on antitrust and health care law. During his private practice, he served as chair of the litigation department and was recognized multiple times as Seattle’s Lawyer of the Year in antitrust law, health care law, and health care litigation. Ross has held leadership roles in several professional organizations, including serving as an officer of the American Bar Association’s Section of Antitrust Law, chair of its Health Industry Committee, and chair of the Washington State Bar Association’s Antitrust and Consumer Protection Section. His scholarly work frequently addresses issues in antitrust and health care, and he is a frequent speaker and writer on these topics. Ross's academic focus includes antitrust law, health law, and administrative law, and he has contributed to the field through book chapters, peer-reviewed journal articles, and professional publications.

Research topics

• Computer Science
• Chemistry
• Chromatography
• Data Mining
• Biochemistry
• Machine Learning
• Environmental chemistry
• Computational biology
• Microbiology
• Biological system
• Biology
• Genetics
• Pharmacology
• Organic chemistry

Selected publications

• Development and Application of a Multidimensional Database for the Detection of Quaternary Ammonium Compounds and Their Phase I Hepatic Metabolites in Humans

  Environmental Science & Technology · 2024 · 21 citations

  • Chemistry
  • Chromatography
  • Environmental chemistry

  ), and MS/MS spectra for 19 parent QACs and 81 QAC metabolites. Using this database, we confidently identified 13 parent QACs and 35 metabolites in de-identified human fecal samples. This is the first study to integrate in vitro metabolite biosynthesis with LC-IM-MS/MS for the simultaneous monitoring of parent QACs and their metabolites in humans.

  PublisherOA PDFDOI

• Determination of drugs and drug metabolites by ion mobility-mass spectrometry: A review

  Analytica Chimica Acta · 2021 · 60 citations

  1st authorCorresponding
  • Chemistry
  • Chromatography
  • Pharmacology
  DOI

• Breaking Down Structural Diversity for Comprehensive Prediction of Ion-Neutral Collision Cross Sections

  Analytical Chemistry · 2020 · 148 citations

  1st authorCorresponding
  • Data Mining
  • Computer Science
  • Chemistry

  Identification of unknowns is a bottleneck for large-scale untargeted analyses like metabolomics or drug metabolite identification. Ion mobility-mass spectrometry (IM-MS) provides rapid two-dimensional separation of ions based on their mobility through a neutral buffer gas. The mobility of an ion is related to its collision cross section (CCS) with the buffer gas, a physical property that is determined by the size and shape of the ion. This structural dependency makes CCS a promising characteristic for compound identification, but this utility is limited by the availability of high-quality reference CCS values. CCS prediction using machine learning (ML) has recently shown promise in the field, but accurate and broadly applicable models are still lacking. Here we present a novel ML approach that employs a comprehensive collection of CCS values covering a wide range of chemical space. Using this diverse database, we identified the structural characteristics, represented by molecular quantum numbers (MQNs), that contribute to variance in CCS and assessed the performance of a variety of ML algorithms in predicting CCS. We found that by breaking down the chemical structural diversity using unsupervised clustering based on the MQNs, specific and accurate prediction models for each cluster can be trained, which showed superior performance than a single model trained with all data. Using this approach, we have robustly trained and characterized a CCS prediction model with high accuracy on diverse chemical structures. An all-in-one web interface (https://CCSbase.net) was built for querying the CCS database and accessing the predictive model to support unknown compound identifications.

  DOI

• Dalbavancin exposure in vitro selects for dalbavancin-non-susceptible and vancomycin-intermediate strains of methicillin-resistant Staphylococcus aureus

  Clinical Microbiology and Infection · 2020 · 42 citations

  • Microbiology
  • Biology
  • Genetics
  PublisherOA PDFDOI

• LiPydomics: A Python Package for Comprehensive Prediction of Lipid Collision Cross Sections and Retention Times and Analysis of Ion Mobility-Mass Spectrometry-Based Lipidomics Data

  Analytical Chemistry · 2020 · 59 citations

  1st authorCorresponding
  • Computer Science
  • Chemistry
  • Chromatography

  strains that are resistant to various antimicrobials.

  DOI

Frequent coauthors

• Libin Xu

  University of Washington

  18 shared

• Xueyun Zheng

  Pacific Northwest National Laboratory

  11 shared

• Aivett Bilbao

  11 shared

• Santosh Lamichhane

  Åbo Akademi University

  8 shared

• Joon‐Yong Lee

  6 shared

• Alan K. Jarmusch

  6 shared

• Matthew M. Harkenrider

  Loyola University Medical Center

  6 shared

• Kayéromi Gomez

  Loyola University Chicago

  6 shared

Education

• B.A., Economics

  Tufts University

  1975

• Other

  Columbia University School of Law

  1978

Awards & honors

• Harlan Fiske Stone Scholar

Similar researchers at University of Washington

• Sarah Jaewon Leeh-151
• Jan Hansenh-130
• Linda Simonsenh-120
• Anna Waldh-119
• Jesse Bloomh-117