Biomedical subjects

Srinivasan Parthasarathy

Publications and source records attributed to Srinivasan Parthasarathy.

3 recordsLinked to original sources

PubMed2006

A model-based approach for mining membrane protein crystallization trials.

MOTIVATION: Membrane proteins are known to play crucial roles in various cellular functions. Information about their function can be derived from their structure, but knowledge of these proteins is limited, as their structures are difficult to obtain. Crystallization has proved to be an essential step in the determination of macromolecular structure. Unfortunately, the bottleneck is that the crystallization process is quite complex and extremely sensitive to experimental conditions, the selection of which is largely a matter of trial and error. Even under the best conditions, it can take a large amount of time, from weeks to years, to obtain diffraction-quality crystals. Other issues include the time and cost involved in taking multiple trials and the presence of very few positive samples in a wide and largely undetermined parameter space. Therefore, any help in directing scientists' attention to the hot spots in the conceptual crystallization space would lead to increased efficiency in crystallization trials. RESULTS: This work is an application case study on mining membrane protein crystallization trials to predict novel conditions that have a high likelihood of leading to crystallization. We use suitable supervised learning algorithms to model the data-space and predict a novel set of crystallization conditions. Our preliminary wet laboratory results are very encouraging and we believe this work shows great promise. We conclude with a view of the crystallization space that is based on our results, which should prove useful for future studies in this area.

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PubMed2005

Automated decision tree classification of corneal shape.

PURPOSE: The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. METHODS: The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz-McDonnell index, Schwiegerling's Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. RESULTS: Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. CONCLUSION: Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification problems.

Cornea↗

PubMed2004

Parallel and distributed methods for incremental frequent itemset mining.

Traditional methods for data mining typically make the assumption that the data is centralized, memory-resident, and static. This assumption is no longer tenable. Such methods waste computational and input/output (I/O) resources when data is dynamic, and they impose excessive communication overhead when data is distributed. Efficient implementation of incremental data mining methods is, thus, becoming crucial for ensuring system scalability and facilitating knowledge discovery when data is dynamic and distributed. In this paper, we address this issue in the context of the important task of frequent itemset mining. We first present an efficient algorithm which dynamically maintains the required information even in the presence of data updates without examining the entire dataset. We then show how to parallelize this incremental algorithm. We also propose a distributed asynchronous algorithm, which imposes minimal communication overhead for mining distributed dynamic datasets. Our distributed approach is capable of generating local models (in which each site has a summary of its own database) as well as the global model of frequent itemsets (in which all sites have a summary of the entire database). This ability permits our approach not only to generate frequent itemsets, but also to generate high-contrast frequent itemsets, which allows one to examine how the data is skewed over different sites.

Algorithms↗