Journal article
Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis
BMC genomics, Vol.13(Suppl 1), pp.S4-S4
01/17/2012
DOI: 10.1186/1471-2164-13-S1-S4
PMCID: PMC3471343
PMID: 22369037
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.
Method
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.
Details
- Title: Subtitle
- Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis
- Creators
- Hyokyeong Lee - San Francisco State UniversityAsher Moody-Davis - San Francisco State UniversityUtsab Saha - San Francisco State UniversityBrian M Suzuki - University of California, San FranciscoDaniel Asarnow - San Francisco State UniversitySteven Chen - University of California, San FranciscoMichelle Arkin - University of California, San FranciscoConor R Caffrey - University of California, San FranciscoRahul Singh - San Francisco State University
- Resource Type
- Journal article
- Publication Details
- BMC genomics, Vol.13(Suppl 1), pp.S4-S4
- Publisher
- BioMed Central
- DOI
- 10.1186/1471-2164-13-S1-S4
- PMID
- 22369037
- PMCID
- PMC3471343
- ISSN
- 1751-6404
- eISSN
- 1471-2164
- Language
- English
- Date published
- 01/17/2012
- Academic Unit
- Computer Science
- Record Identifier
- 9984446270502771
Metrics
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