A methodology for customizing clinical tests for esophageal cancer based on patient preferences.

BACKGROUND: Clinical tests for diagnosis of any disease may be expensive, uncomfortable, time consuming and can have side effects e.g. barium swallow test for esophageal cancer. Although we can predict non-existence of esophageal cancer with near 100% certainty just using demographics, lifestyle, medical history information, and a few basic clinical tests but our objective is to devise a general methodology for customizing tests with user preferences to avoid expensive or uncomfortable tests. METHOD: We propose to use classifiers trained from electronic medical records (EMR) for selection of tests. The key idea is to design classifiers with 100% false normal rates, possibly at the cost of higher false abnormal. We find kernel logistic regression to be most suitable for the task. We propose an algorithm for finding the best probability threshold for kernel LR, based on test set accuracy tuning with help of a validation data set. Using the proposed algorithm, we describe schemes for selecting tests, which appear as features in the automatic classification algorithm, using preferences on costs and discomfort of the users i.e the proposed method is able to detect almost all true patients in the population even with user preferred clinical tests. RESULT: We test our methodology with EMRs collected for more than 3000 patients, as a part of project carried out by a reputed hospital in Mumbai, India. We found that kernel SVM and kernel LR with a polynomial kernel of degree 3, yields an accuracy of 99.18% and sensitivity 100% using only demographic, lifestyle, patient history, and basic clinical tests. We demonstrate our test selection algorithm using two case studies, one using cost of clinical tests, and other using "discomfort" values for clinical tests. We compute the test sets corresponding to the lowest false abnormals for each criterion described above, using exhaustive enumeration of 12 and 15 clinical tests respectively. The sets turn out to be different, substantiating our claim that one can customize test sets based on user preferences.

Comparison of protein structures by growing neighborhood alignments.

BACKGROUND: Design of protein structure comparison algorithm is an important research issue, having far reaching implications. In this article, we describe a protein structure comparison scheme, which is capable of detecting correct alignments even in difficult cases, e.g. non-topological similarities. The proposed method computes protein structure alignments by comparing, small substructures, called neighborhoods. Two different types of neighborhoods, sequence and structure, are defined, and two algorithms arising out of the scheme are detailed. A new method for computing equivalences having non-topological similarities from pairwise similarity score is described. A novel and fast technique for comparing sequence neighborhoods is also developed. RESULTS: The experimental results show that the current programs show better performance on Fischer and Novotny's benchmark datasets, than state of the art programs, e.g. DALI, CE and SSM. Our programs were also found to calculate correct alignments for proteins with huge amount of indels and internal repeats. Finally, the sequence neighborhood based program was used in extensive fold and non-topological similarity detection experiments. The accuracy of the fold detection experiments with the new measure of similarity was found to be similar or better than that of the standard algorithm CE. CONCLUSION: A new scheme, resulting in two algorithms, have been developed, implemented and tested. The programs developed are accessible at http://mllab.csa.iisc.ernet.in/mp2/runprog.html.

Projections for fast protein structure retrieval.

BACKGROUND: In recent times, there has been an exponential rise in the number of protein structures in databases e.g. PDB. So, design of fast algorithms capable of querying such databases is becoming an increasingly important research issue. This paper reports an algorithm, motivated from spectral graph matching techniques, for retrieving protein structures similar to a query structure from a large protein structure database. Each protein structure is specified by the 3D coordinates of residues of the protein. The algorithm is based on a novel characterization of the residues, called projections, leading to a similarity measure between the residues of the two proteins. This measure is exploited to efficiently compute the optimal equivalences. RESULTS: Experimental results show that, the current algorithm outperforms the state of the art on benchmark datasets in terms of speed without losing accuracy. Search results on SCOP 95% nonredundant database, for fold similarity with 5 proteins from different SCOP classes show that the current method performs competitively with the standard algorithm CE. The algorithm is also capable of detecting non-topological similarities between two proteins which is not possible with most of the state of the art tools like Dali.