The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform

Matthew C Chambers - One of the best experts on this subject based on the ideXlab platform.

  • myrimatch highly accurate tandem mass spectral peptide identification by multivariate Hypergeometric analysis
    Journal of Proteome Research, 2007
    Co-Authors: David L Tabb, And Christopher G Fernando, Matthew C Chambers
    Abstract:

    Shotgun proteomics experiments are dependent upon database search engines to identify peptides from tandem mass spectra. Many of these algorithms score potential identifications by evaluating the number of fragment ions matched between each peptide sequence and an observed spectrum. These systems, however, generally do not distinguish between matching an intense peak and matching a minor peak. We have developed a statistical model to score peptide matches that is based upon the multivariate Hypergeometric Distribution. This scorer, part of the “MyriMatch” database search engine, places greater emphasis on matching intense peaks. The probability that the best match for each spectrum has occurred by random chance can be employed to separate correct matches from random ones. We evaluated this software on data sets from three different laboratories employing three different ion trap instruments. Employing a novel system for testing discrimination, we demonstrate that stratifying peaks into multiple intensity ...

John R Yates - One of the best experts on this subject based on the ideXlab platform.

  • a Hypergeometric probability model for protein identification and validation using tandem mass spectral data and protein sequence databases
    Analytical Chemistry, 2003
    Co-Authors: Rovshan G Sadygov, John R Yates
    Abstract:

    We present a new probability-based method for protein identification using tandem mass spectra and protein databases. The method employs a Hypergeometric Distribution to model frequencies of matches between fragment ions predicted for peptide sequences with a specific (M + H)+ value (at some mass tolerance) in a protein sequence database and an experimental tandem mass spectrum. The Hypergeometric Distribution constitutes null hypothesisall peptide matches to a tandem mass spectrum are random. It is used to generate a score characterizing the randomness of a database sequence match to an experimental tandem mass spectrum and to determine the level of significance of the null hypothesis. For each tandem mass spectrum and database search, a peptide is identified that has the least probability of being a random match to the spectrum and the corresponding level of significance of the null hypothesis is determined. To check the validity of the Hypergeometric model in describing fragment ion matches, we used χ2...

N Balakrishnan - One of the best experts on this subject based on the ideXlab platform.

  • a conway maxwell poisson type generalization of the negative Hypergeometric Distribution
    Communications in Statistics-theory and Methods, 2020
    Co-Authors: Sudip Roy, Ram C Tripathi, N Balakrishnan
    Abstract:

    Negative Hypergeometric Distribution arises as a waiting time Distribution when we sample without replacement from a finite population. It has applications in many areas such as inspection sampling...

  • a closed form approximation of moments of new generalization of negative binomial Distribution
    arXiv: Statistics Theory, 2019
    Co-Authors: Sudip Roy, Ram C Tripathi, N Balakrishnan
    Abstract:

    In this paper, we propose a closed form approximation to the mean and variance of a new generalization of negative binomial (NGNB) Distribution arising from the Extended COM-Poisson (ECOMP) Distribution developed by Chakraborty and Imoto (2016)(see [4]). The NGNB is a special case of the ECOMP Distribution and was named so by these authors. This Distribution is more flexible in terms of the dispersion index as compared to its ordinary counterparts. It approaches the COM-Poisson Distribution (Shmueli et al. 2005) [11] under suitable limiting conditions. The NGNB can also be obtained from the COM-Negative Hypergeometric Distribution (Roy et al. 2019)[10] as a limiting Distribution. In this paper, we present closed-form approximations for the mean and variance of the NGNB Distribution. These approximations can be viewed as the mean and variance of convolution of independent and identically distributed negative binomial populations. The proposed closed-form approximations of the mean and variance will be helpful in building the link function for the generalized negative binomial regression model based on the NGNB Distribution and other extended applications, hence resulting in enhanced applicability of this model.

  • a primer on statistical Distributions
    2003
    Co-Authors: N Balakrishnan, V B Nevzorov
    Abstract:

    Preface. Preliminaries. I. DISCRETE DistributionS. Discrete Uniform Distribution. Degenerate Distribution. Bernoulli Distribution. Binomial Distribution. Geometric Distribution. Negative Binomial Distribution. Hypergeometric Distribution. Poisson Distribution. Miscellanea. II. CONTINUOUS DistributionS. Uniform Distribution. Cauchy Distribution. Triangular Distribution. Power Distribution. Pareto Distribution. Beta Distribution. Arcsine Distribution. Exponential Distribution. Laplace Distribution. Gamma Distribution. Extreme Value Distributions. Logistic Distribution. Normal Distribution. Miscellanea. III. MULTIVARIATE DistributionS. Multinomial Distribution. Multivariate Normal Distribution. Dirichlet Distribution. Appendix - Pioneers in Distribution Theory. Bibliography. Author Index. Subject Index.

  • some approximations to the multivariate Hypergeometric Distribution with applications to hypothesis testing
    Computational Statistics & Data Analysis, 2000
    Co-Authors: Aaron Childs, N Balakrishnan
    Abstract:

    In this paper, we will examine some approximations to the multivariate Hypergeometric Distribution by continuous random variables. The continuous random variables will be chosen so as to have the same range of variation, means, variances and covariances as their discrete counterparts. We then show how these approximations can be used in testing hypotheses about the parameters of the multivariate Hypergeometric Distribution.

David L Tabb - One of the best experts on this subject based on the ideXlab platform.

  • myrimatch highly accurate tandem mass spectral peptide identification by multivariate Hypergeometric analysis
    Journal of Proteome Research, 2007
    Co-Authors: David L Tabb, And Christopher G Fernando, Matthew C Chambers
    Abstract:

    Shotgun proteomics experiments are dependent upon database search engines to identify peptides from tandem mass spectra. Many of these algorithms score potential identifications by evaluating the number of fragment ions matched between each peptide sequence and an observed spectrum. These systems, however, generally do not distinguish between matching an intense peak and matching a minor peak. We have developed a statistical model to score peptide matches that is based upon the multivariate Hypergeometric Distribution. This scorer, part of the “MyriMatch” database search engine, places greater emphasis on matching intense peaks. The probability that the best match for each spectrum has occurred by random chance can be employed to separate correct matches from random ones. We evaluated this software on data sets from three different laboratories employing three different ion trap instruments. Employing a novel system for testing discrimination, we demonstrate that stratifying peaks into multiple intensity ...

Mustapha Samih - One of the best experts on this subject based on the ideXlab platform.

  • A Novel Methodology-Based Joint Hypergeometric Distribution to Analyze the Security of Sharded Blockchains
    IEEE Access, 2020
    Co-Authors: Abdelatif Hafid, Abdelhakim Hafid, Mustapha Samih
    Abstract:

    Cryptocurrencies (e.g., Bitcoin and Ethereum), which promise to become the future of money transactions, are mainly implemented with blockchain technology. However, blockchain suffers from scalability issues. Sharding is the leading solution for blockchain scalability. Sharding splits the blockchain network into sub-chains called shards/committees. Each shard processes a sub-set of transactions, rather than the entire network processing all transactions. This raises security issues for sharding-based blockchain protocols. In this paper, we propose a novel methodology to analyze the security of these protocols (e.g., OmniLedger and RapidChain). In particular, this methodology estimates the failure probability of one sharding round taking into consideration the failure probabilities of all shards. To illustrate the effectiveness of the estimated failure probability, we conduct a numerical analysis of our methodology based on a huge number of trials. Finally, we compute confidence intervals to accurately estimate the failure probability and compare our methodology with existing approaches.