Early Computational Statistics

Watnik, Mitchell

doi:10.1198/jcgs.2011.204b

Cited by 14 publications

(3 citation statements)

References 22 publications

(14 reference statements)

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“…The Monte Carlo method uses computational algorithms based on iterations of random sampling from a positively skewed probability distribution (i.e., the probability is inversely related to larger population-based inferences versus known validated patient registries) in order to acquire numerical results for the goals of sampling, optimization, and estimations. [34][35][36] In other words, our population confidence decreased as we increased our reliance on sequencing point estimates, resulting in positive skewness for the probability distribution. Using a minimum amount defined as the mean of RDEB-GS of 343 and a maximum population estimate of 12,562 of any RDEB, we conducted 10,000 repetitions resulting in a mean estimate of approximately 3850 patients in the US who may benefit from COL7A1mediated treatments in the US.…”

Section: Simulationmentioning

confidence: 99%

<p>From Clinical Phenotype to Genotypic Modelling: Incidence and Prevalence of Recessive Dystrophic Epidermolysis Bullosa (RDEB)</p>

Eichstadt¹,

Tang²,

Solis³

et al. 2019

CCID

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Background: Recessive dystrophic epidermolysis bullosa (RDEB) is an inherited genetic disorder characterized by recurrent and chronic open wounds with significant morbidity, impaired quality of life, and early mortality. RDEB patients demonstrate reduction or structural alteration type VII collagen (C7) owing to mutations in the gene COL7A1, the main component of anchoring fibrils (AF) necessary to maintain epidermal-dermal cohesion. While over 700 alterations in COL7A1 have been reported to cause dystrophic epidermolysis bullosa (DEB), which may be inherited in an autosomal dominant (DDEB) or autosomal recessive pattern (RDEB), the incidence and prevalence of RDEB is not well defined. To date, the widely estimated incidence (0.2-6.65 per million births) and prevalence (3.5-20.4per million people) of RDEB has been primarily characterized by limited analyses of clinical databases or registries. Methods: Using a genetic modelling approach, we use whole exome and genome sequencing data to estimate the allele frequency of pathogenic variants. Through the ClinVar and NCBI database of human genome variants and phenotypes, DEB Register, and analyzing premature COL7A1 termination variants we built a model to predict the pathogenicity of previously unclassified variants. We applied the model to publicly available sequences from the Exome Aggregation Consortium (ExAC) and Genome Aggregation Database (gnomAD) and identified variants which were classified as pathogenic for RDEB from which we estimate disease incidence and prevalence. Results: Genetic modelling applied to the whole exome and genome sequencing data resulted in the identification of predicted RDEB pathogenic alleles, from which our estimate of the incidence of RDEB is 95 per million live births, 30 times the 3.05 per million live birth incidence estimated by the National Epidermolysis Bullosa Registry (NEBR). Using a simulation approach, we estimate a mean of approximately 3,850 patients in the US who may benefit from COL7A1-mediated treatments in the US. Conclusion: We conclude that genetic allele frequency estimation may enhance the underdiagnosis of rare genetic diseases generally, and RDEB specifically, which may improve incidence and prevalence estimates of patients who may benefit from treatment.

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Section: Simulationmentioning

confidence: 99%

<p>From Clinical Phenotype to Genotypic Modelling: Incidence and Prevalence of Recessive Dystrophic Epidermolysis Bullosa (RDEB)</p>

Eichstadt¹,

Tang²,

Solis³

et al. 2019

CCID

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“…To be certain, mathematical computation was carried out with the help of machines long before the advent of the modern computer with devices as simple as the abacus and slide rule to more complex mechanical machines which could quite literally crank out the four basic operations of mathematics (explore for example the 'Analytic Engine' of Babbage 3 ). Watnik 4 provides a concise summary of the early history of computational statistics in which he relays an interesting story concerning the first use of the word 'computer' by the Journal of the American Statistical Association which he attributes to Porter. 5 In this work, Porter references a machine designed by Herman Hollerith for tabulating census statistics by using a sorted punch card system and, as Watnik notes, 4 despite a significant increase in the U.S. population the machine aided tabulation was completed in less than a year despite the previous census requiring 7 years to complete the task.…”

Section: A Brief History Of Computation Leading To the Era Of Electronic Computersmentioning

confidence: 99%

“…Watnik 4 provides a concise summary of the early history of computational statistics in which he relays an interesting story concerning the first use of the word 'computer' by the Journal of the American Statistical Association which he attributes to Porter. 5 In this work, Porter references a machine designed by Herman Hollerith for tabulating census statistics by using a sorted punch card system and, as Watnik notes, 4 despite a significant increase in the U.S. population the machine aided tabulation was completed in less than a year despite the previous census requiring 7 years to complete the task. Archaic computing machinery though primitive by today's standards first showed the potential for carrying out tedious mathematical computation on a large scale and helped pave the way for modern computational science.…”

Section: A Brief History Of Computation Leading To the Era Of Electronic Computersmentioning

confidence: 99%

Where do computational mathematics and computational statistics converge?

Duffy¹

2014

WIREs Computational Stats

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This overview presents a discussion on several topics of importance shared by the fields of computational mathematics and computational statistics. A brief history of the advancements to the fields of mathematics and statistics leading to the modern computational era is provided, along with a discussion of the intersection of topics which comprise the two subjects. The foundational aspects shared by both computational mathematics and computational statistics are explored with elementary discussions suitable to nonexperts and aspiring students of the computational sciences. Finally, a discussion of the role played by computational mathematics and computational statistics in a few selected application areas is explored. © 2014 Wiley Periodicals, Inc. How to cite this article: WIREs Comput Stat AN INTRODUCTION TO COMPUTATIONAL MATHEMATICS AND COMPUTATIONAL STATISTICSV arious fields that comprise the greater forum of computational science possess a certain core of methodologies necessary for producing numerically founded solutions. Since the advent of the first computers, the fields of computational mathematics and computational statistics have rapidly emerged as distinct disciplines making many important contributions to this area. While each subject possesses its own primary studies, computational mathematics and computational statistics share common ground owing to the close relationship of the underlying subjects. In this paper we will examine the areas and ways in which the disciplines of computational mathematics and computational statistics converge. The first step in this process will be to define what exactly computational * Correspondence to: aduffy@math.fsu.edu Citilabs, Tallahassee, FL, USA Conflict of interest: The author has declared no conflicts of interest for this article. mathematics and computational statistics are and how they relate to their more theoretical parent subjects.The subject of computational mathematics is primarily concerned with ways in which to efficiently compute numerical solutions to mathematical problems which may be difficult or impossible to solve otherwise. Computational mathematics can be viewed as the application of the theory of numerical analysis where algorithms must be developed to adhere to the theoretical solution properties of convergence and stability in a finite precision environment, and implemented in a manner which allows for feasible computation with limited computing resources. In computational mathematics, feasible computation may require the use of limited memory algorithms or parallel computation to reduce run times. In the modern many-core computing era parallelization is a vital aspect, and some introductory texts covering the tools of computational mathematics teach parallel computing alongside traditional material such as the text of Karniadakis and Kirby. 1 The practicing computational mathematician is typically well versed in high performance computing and parallel algorithms, as they are often in the business of producing high performance mathematic...

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