Biomedical subjects

Probal Chaudhuri

Publications and source records attributed to Probal Chaudhuri.

6 recordsLinked to original sources

PubMed2006

Multiscale classification using nearest neighbor density estimates.

Density estimates based on k-nearest neighbors have useful applications in nonparametric discriminant analysis. In classification problems, optimal values of k are usually estimated by minimizing the cross-validated misclassification rates. However, these cross-validation techniques allow only one value of k for each population density estimate, while in a classification problem, the optimum value of k for a class may also depend on its competing population densities. Further, it is computationally difficult to minimize the cross-validated error rate when there are several competing populations. Moreover, in addition to depending on the entire training data set, a good choice of k should also depend on the specific observation to be classified. Therefore, instead of using a single value of k for each population density estimate, it is more useful in practice to consider the results for multiple values of k to arrive at the final decision. This paper presents one such approach along with a graphical device, which gives more information about classification results for various choices of k and the related statistical uncertainties present there. The utility of this proposed methodology has been illustrated using some benchmark data sets.

Algorithms↗

PubMed2005

Identification of polymorphic motifs using probabilistic search algorithms.

The problem of identifying motifs comprising nucleotides at a set of polymorphic DNA sites, not necessarily contiguous, arises in many human genetic problems. However, when the sites are not contiguous, no efficient algorithm exists for polymorphic motif identification. A search based on complete enumeration is computationally inefficient. We have developed probabilistic search algorithms to discover motifs of known or unknown lengths. We have developed statistical tests of significance for assessing a motif discovery, and a statistical criterion for simultaneously estimating motif length and discovering it. We have tested these algorithms on various synthetic data sets and have shown that they are very efficient, in the sense that the "true" motifs can be detected in the vast majority of replications and in a small number of iterations. Additionally, we have applied them to some real data sets and have shown that they are able to identify known motifs. In certain applications, it is pertinent to find motifs that contain contrasting nucleotides at the sites included in the motif (e.g., motifs identified in case-control association studies). For this, we have suggested appropriate modifications. Using simulations, we have discovered that the success rate of identification of the correct motif is high in case-control studies except when relative risks are small. Our analyses of evolutionary data sets resulted in the identification of some motifs that appear to have important implications on human evolutionary inference. These algorithms can easily be implemented to discover motifs from multilocus genotype data by simple numerical recoding of genotypes.

Africa↗

PubMed2005

On visualization and aggregation of nearest neighbor classifiers.

Nearest neighbor classification is one of the simplest and most popular methods for statistical pattern recognition. A major issue in k-nearest neighbor classification is how to find an optimal value of the neighborhood parameter k. In practice, this value is generally estimated by the method of cross-validation. However, the ideal value of k in a classification problem not only depends on the entire data set, but also on the specific observation to be classified. Instead of using any single value of k, this paper studies results for a finite sequence of classifiers indexed by k. Along with the usual posterior probability estimates, a new measure, called the Bayesian measure of strength, is proposed and investigated in this paper as a measure of evidence for different classes. The results of these classifiers and their corresponding estimated misclassification probabilities are visually displayed using shaded strips. These plots provide an effective visualization of the evidence in favor of different classes when a given data point is to be classified. We also propose a simple weighted averaging technique that aggregates the results of different nearest neighbor classifiers to arrive at the final decision. Based on the analysis of several benchmark data sets, the proposed method is found to be better than using a single value of k.

Algorithms↗

PubMed2004

Thresholding in edge detection: a statistical approach.

Many edge detectors are available in image processing literature where the choices of input parameters are to be made by the user. Most of the time, such choices are made on an ad-hoc basis. In this article, an edge detector is proposed where thresholding is performed using statistical principles. Local standardization of thresholds for each individual pixel (local thresholding), which depends upon the statistical variability of the gradient vector at that pixel, is done. Such a standardized statistic based on the gradient vector at each pixel is used to determine the eligibility of the pixel to be an edge pixel. The results obtained from the proposed method are found to be comparable to those from many well-known edge detectors. However, the values of the input parameters providing the appreciable results in the proposed detector are found to be more stable than other edge detectors and possess statistical interpretation.

Algorithms↗

PubMed2003

Words in DNA sequences: some case studies based on their frequency statistics.

One of the critical requirements of data analysis involving large DNA sequences is an effective statistical summarization of those sequences. In this article DNA sequences have been analyzed based on word frequencies. Our analysis focuses on the detection of structural signature of a genome reflected in word frequencies and identification of phylogenetic relationships among different species reflected in the variation of word distributions in their DNA sequences. We have carried out a statistical study of the complete genome of baker's yeast, of various ribosomal RNA sequences from different prokaryotic and eukaryotic organisms and of the full genomes of some bacteriophages. Our exploratory analysis amply demonstrates the usefulness of DNA word frequencies in reducing the dimensionality of large sequences while retaining some of the structural information there that can have biological significance. Some conceptual issues that arise in course of our investigation have been addressed. A few interesting problems related to the statistics of DNA words have been pointed out with some indication of their possible solutions. The work has been partially motivated by the fact that sequence alignment and homology techniques that are quite popular for comparing and analyzing relatively smaller DNA sequences of nearly equal sizes are not applicable to data consisting of large sequences with widely varying sizes, which may contain segments with unknown or no biological functions, and consequently their comparison through functional homology is either impossible or extremely difficult.

Bacteriophages↗

PubMed2002

SWORDS: a statistical tool for analysing large DNA sequences.

In this article, we present some simple yet effective statistical techniques for analysing and comparing large DNA sequences. These techniques are based on frequency distributions of DNA words in a large sequence, and have been packaged into a software called SWORDS. Using sequences available in public domain databases housed in the Internet, we demonstrate how SWORDS can be conveniently used by molecular biologists and geneticists to unmask biologically important features hidden in large sequences and assess their statistical significance.

Animals↗