At the state-of-the-art in drug discovery, one of the key challenges is to develop high-throughput screening (HTS) techniques that can measure changes as a continuum of complex phenotypes induced in a target pathogen. Such measurements are crucial in developing therapeutics against diseases like schistosomiasis, trypanosomiasis, and leishmaniasis, which impact millions worldwide. These diseases are caused by parasites that can manifest a variety of phenotypes at any given point in time in response to drugs. Consequently, a single end-point measurement of 'live or death' (e.g., ED 50 value) commonly used for lead identification is over-simplistic. In our method to address this problem, the parasites are tracked during the entire course of (video) recorded observations and changes in their appearance-based and behavioral characteristics quantified using geometric, texturebased, color-based, and motion-based descriptors. Subsequently, within the on-line setting, machine learning techniques are used classify the exhibited phenotypes into well defined categories. Important advancements introduced as a consequence of the proposed approach include: (1) ability to assess the interactions between putative drugs and parasites in terms of multiple appearance and behavior-based phenotypes, (2) automatic classification and quantification of pathogen phenotypes. Experimental data from lead identification studies against the disease Schistosomiasis validate the proposed methodology.

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Abstract. Exchange type chromosome aberrations (ETCAs) are rearrangements of the genome that occur when chromosomes break and the resulting fragments rejoin with fragments from other chromosomes or from other regions within the same chromosome. ETCAs are commonly observed in cancer cells and in cells exposed to radiation. The frequency of these chromosome rearrangements is correlated with their spatial proximity, therefore it can be used to infer the three dimensional organization of the genome. Extracting statistical significance of spatial proximity from cancer and radiation data has remained somewhat elusive because of the sparsity of the data. We here propose a new approach to study the three dimensional organization of the genome using algebraic statistics. We test our method on a published data set of irradiated human blood lymphocyte cells. We provide a rigorous method for testing the overall organization of the genome, and in agreement with previous results we find a random relative positioning of chromosomes with the exception of the chromosome pairs {1,22} and {13,14} that have a significantly larger number of ETCAs than the rest of the chromosome pairs suggesting their spatial proximity. We conclude that algebraic methods can successfully be used to analyze genetic data and have potential applications to larger and more complex data sets.