Biomedical subjects

Gyan Bhanot

Publications and source records attributed to Gyan Bhanot.

7 recordsLinked to original sources

PubMed2006

Constructing near-perfect phylogenies with multiple homoplasy events.

MOTIVATION: We explore the problem of constructing near-perfect phylogenies on bi-allelic haplotypes, where the deviation from perfect phylogeny is entirely due to homoplasy events. We present polynomial-time algorithms for restricted versions of the problem. We show that these algorithms can be extended to genotype data, in which case the problem is called the near-perfect phylogeny haplotyping (NPPH) problem. We present a near-optimal algorithm for the H1-NPPH problem, which is to determine if a given set of genotypes admit a phylogeny with a single homoplasy event. The time-complexity of our algorithm for the H1-NPPH problem is O(m2(n + m)), where n is the number of genotypes and m is the number of SNP sites. This is a significant improvement over the earlier O(n4) algorithm. We also introduce generalized versions of the problem. The H(1, q)-NPPH problem is to determine if a given set of genotypes admit a phylogeny with q homoplasy events, so that all the homoplasy events occur in a single site. We present an O(m(q+1)(n + m)) algorithm for the H(1,q)-NPPH problem. RESULTS: We present results on simulated data, which demonstrate that the accuracy of our algorithm for the H1-NPPH problem is comparable to that of the existing methods, while being orders of magnitude faster. AVAILABILITY: The implementation of our algorithm for the H1-NPPH problem is available upon request.

Algorithms↗

PubMed2006

A robust meta-classification strategy for cancer detection from MS data.

We propose a novel method for phenotype identification involving a stringent noise analysis and filtering procedure followed by combining the results of several machine learning tools to produce a robust predictor. We illustrate our method on SELDI-TOF MS prostate cancer data (http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp). Our method identified 11 proteomic biomarkers and gave significantly improved predictions over previous analyses with these data. We were able to distinguish cancer from non-cancer cases with a sensitivity of 90.31% and a specificity of 98.81%. The proposed method can be generalized to multi-phenotype prediction and other types of data (e.g., microarray data).

Biomarkers, Tumor↗

PubMed2005

A robust meta-classification strategy for cancer diagnosis from gene expression data.

One of the major challenges in cancer diagnosis from microarray data is to develop robust classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose a meta-classification scheme which uses a robust multivariate gene selection procedure and integrates the results of several machine learning tools trained on raw and pattern data. We validate our method by applying it to distinguish diffuse large B-cell lymphoma (DLBCL) from follicular lymphoma (FL) on two independent datasets: the HuGeneFL Affmetrixy dataset of Shipp et al. (www. genome.wi.mit.du/MPR /lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our meta-classification technique achieves higher predictive accuracies than each of the individual classifiers trained on the same dataset and is robust against various data perturbations. We also find that combinations of p53 responsive genes (e.g., p53, PLK1 and CDK2) are highly predictive of the phenotype.

Algorithms↗

PubMed2005

Robust diagnosis of non-Hodgkin lymphoma phenotypes validated on gene expression data from different laboratories.

A major challenge in cancer diagnosis from microarray data is the need for robust, accurate, classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose such a classification scheme originally developed for phenotype identification from mass spectrometry data. The method uses a robust multivariate gene selection procedure and combines the results of several machine learning tools trained on raw and pattern data to produce an accurate meta-classifier. We illustrate and validate our method by applying it to gene expression datasets: the oligonucleotide HuGeneFL microarray dataset of Shipp et al. (www.genome.wi.mit.du/MPR/lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our pattern-based meta-classification technique achieves higher predictive accuracies than each of the individual classifiers , is robust against data perturbations and provides subsets of related predictive genes. Our techniques predict that combinations of some genes in the p53 pathway are highly predictive of phenotype. In particular, we find that in 80% of DLBCL cases the mRNA level of at least one of the three genes p53, PLK1 and CDK2 is elevated, while in 80% of FL cases, the mRNA level of at most one of them is elevated.

Biomarkers, Tumor↗

PubMed2003

The importance of thermodynamic equilibrium for high throughput gene expression arrays.

We present an analysis of physical chemical constraints on the accuracy of DNA micro-arrays under equilibrium and nonequilibrium conditions. At the beginning of the article we describe an algorithm for choosing a probe set with high specificity for targeted genes under equilibrium conditions. The algorithm as well as existing methods is used to select probes from the full Saccharomyces cerevisiae genome, and these probe sets, along with a randomly selected set, are used to simulate array experiments and identify sources of error. Inasmuch as specificity and sensitivity are maximum at thermodynamic equilibrium, we are particularly interested in the factors that affect the approach to equilibrium. These are analyzed later in the article, where we develop and apply a rapidly executable method to simulate the kinetics of hybridization on a solid phase support. Although the difference between solution phase and solid phase hybridization is of little consequence for specificity and sensitivity when equilibrium is achieved, the kinetics of hybridization has a pronounced effect on both. We first use the model to estimate the effects of diffusion, crosshybridization, relaxation time, and target concentration on the hybridization kinetics, and then investigate the effects of the most important kinetic parameters on specificity. We find even when using probe sets that have high specificity at equilibrium that substantial crosshybridization is present under nonequilibrium conditions. Although those complexes that differ from perfect complementarity by more than a single base do not contribute to sources of error at equilibrium, they slow the approach to equilibrium dramatically and confound interpretation of the data when they dissociate on a time scale comparable to the time of the experiment. For the best probe set, our simulation shows that steady-state behavior is obtained in a relaxation time of approximately 12-15 h for experimental target concentrations approximately (10(-13) - 10(-14))M, but the time is greater for lower target concentrations in the range (10(-15)-10(-16))M. The result points to an asymmetry in the accuracy with which up- and downregulated genes are identified.

Algorithms↗

PubMed2003

Dynamical analysis of a degenerate primary and secondary humoral immune response.

Lymphocyte receptor response to antigen is degenerate. Each receptor can have a high affinity to more than one antigen. The optimal level of degeneracy was previously modeled using different methods; all showing that the degeneracy level should be inversely proportional to the probability that an antigen belongs to the self repertoire. Here we develop a new formalism, reproducing the results of previous models, which enables us to study the relation between receptor degeneracy and the pathogen-immune cell interaction dynamics, in primary and secondary response. We begin by developing a general formalism and reproducing the results obtained by Nemazee: (1) that an optimal immune system will have a capacity which is inversely proportional to the fraction of self-antigens and (2) that the number of self-reactive cells that the body destroys is tuned by this capacity optimization to be 63%. We then use our extended framework to relate the minimal number of B cell precursor required to mount an immune response to the naive B cell production rate. Finally, we analyze the dynamics of the interaction between the immune system and a pathogen and show that memory cells may be used as the first line of defense, while newly created cells are used later to refine the immune response.

Animals↗

PubMedDate not supplied

Results from modeling of B-Cell receptors binding to antigen.

In the late 80's, Dintzis et al. conducted an experiment which showed that T-Cell independent activation of B-cells needs high-valence antigen and happens only in a narrow range of antigen concentration. These experiments were believed to be explained by the "immunon" theory that requires that a minimum number of receptors need to be cross-linked to activate a cell. However, the immunon theory does not take into account receptor dynamics and cannot explain the lack of immune response at high antigen concentration or low antigen valence. We propose instead a simple, new mechanism for the T-Cell independent activation of B-Cell, which includes receptor endocytosis. Our model focuses on the fact that for the majority of antigens where the B-Cell is activated with T-Cell help, the kinetic parameters for binding, unbinding and endocytosis must be tuned so that there is an equilibrium between the number of receptors bound on the surface of the B-Cell and the number of antigen-bound receptors endocytosed. This equilibrium mechanism is probably generic and will also occur even when the B-Cell is activated by antigen without T-Cell help. By computer modeling, we show that if we accept this hypothesis of the requirement for equilibrium between the two mechanisms of binding and endocytosis, then we can explain both the valence cutoff and the low and high zone tolerance seen in the Dintzis experiment.

Adaptation, Physiological↗