title: Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis.
abstract: BACKGROUND
  Neglected tropical diseases, especially those caused by helminths, constitute some of the most common infections of the world's poorest people. Development of techniques for automated, high-throughput drug screening against these diseases, especially in whole-organism settings, constitutes one of the great challenges of modern drug discovery.
  
  
  METHODS
  We present a method for enabling high-throughput phenotypic drug screening against diseases caused by helminths with a focus on schistosomiasis. The proposed method allows for a quantitative analysis of the systemic impact of a drug molecule on the pathogen as exhibited by the complex continuum of its phenotypic responses. This method consists of two key parts: first, biological image analysis is employed to automatically monitor and quantify shape-, appearance-, and motion-based phenotypes of the parasites. Next, we represent these phenotypes as time-series and show how to compare, cluster, and quantitatively reason about them using techniques of time-series analysis.
  
  
  RESULTS
  We present results on a number of algorithmic issues pertinent to the time-series representation of phenotypes. These include results on appropriate representation of phenotypic time-series, analysis of different time-series similarity measures for comparing phenotypic responses over time, and techniques for clustering such responses by similarity. Finally, we show how these algorithmic techniques can be used for quantifying the complex continuum of phenotypic responses of parasites. An important corollary is the ability of our method to recognize and rigorously group parasites based on the variability of their phenotypic response to different drugs.
  
  
  CONCLUSIONS
  The methods and results presented in this paper enable automatic and quantitative scoring of high-throughput phenotypic screens focused on helmintic diseases. Furthermore, these methods allow us to analyze and stratify parasites based on their phenotypic response to drugs. Together, these advancements represent a significant breakthrough for the process of drug discovery against schistosomiasis in particular and can be extended to other helmintic diseases which together afflict a large part of humankind.
authors.affiliation: Department of Computer Science, San Francisco State University, San Francisco, CA 94132, USA.
authors.forename: Hyokyeong
authors.initials: H
authors.lastname: Lee
authors.forename: Asher
authors.initials: A
authors.lastname: Moody-Davis
authors.forename: Utsab
authors.initials: U
authors.lastname: Saha
authors.forename: Brian M
authors.initials: BM
authors.lastname: Suzuki
authors.forename: Daniel
authors.initials: D
authors.lastname: Asarnow
authors.forename: Steven
authors.initials: S
authors.lastname: Chen
authors.forename: Michelle
authors.initials: M
authors.lastname: Arkin
authors.forename: Conor R
authors.initials: CR
authors.lastname: Caffrey
authors.forename: Rahul
authors.initials: R
authors.lastname: Singh
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D006801:Humans; D008148:Lovastatin / Q000627:therapeutic use; D010641:Phenotype; D011223:Praziquantel / Q000627:therapeutic use; D012552:Schistosomiasis / Q000188:drug therapy*; D012556:Schistosomicides / Q000627:therapeutic use
chemical_list: D012556:Schistosomicides; D011223:Praziquantel; D008148:Lovastatin