Research Triangle Park

About: Research Triangle Park is a nonprofit organization based out in Durham, North Carolina, United States. It is known for research contribution in the topics: Population & Receptor. The organization has 24961 authors who have published 35800 publications receiving 1684504 citations. The organization is also known as: RTP.

Statistical tests, P-values, confidence intervals, and power: a guide to misinterpretations

Abstract: Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so-and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.

Growth Stage –Based Phenotypic Analysis of Arabidopsis: A Model for High Throughput Functional Genomics in Plants

Abstract: With the completion of the Arabidopsis genome sequencing project, the next major challenge is the large-scale determination of gene function. As a model organism for agricultural biotechnology, Arabidopsis presents the opportunity to provide key insights into the way that gene function can affect commercial crop production. In an attempt to aid in the rapid discovery of gene function, we have established a high throughput phenotypic analysis process based on a series of defined growth stages that serve both as developmental landmarks and as triggers for the collection of morphological data. The data collection process has been divided into two complementary platforms to ensure the capture of detailed data describing Arabidopsis growth and development over the entire life of the plant. The first platform characterizes early seedling growth on vertical plates for a period of 2 weeks. The second platform consists of an extensive set of measurements from plants grown on soil for a period of approximately 2 months. When combined with parallel processes for metabolic and gene expression profiling, these platforms constitute a core technology in the high throughput determination of gene function. We present here analyses of the development of wild-type Columbia (Col-0) plants and selected mutants to illustrate a framework methodology that can be used to identify and interpret phenotypic differences in plants resulting from genetic variation and/or environmental stress.

QSAR Modeling: Where have you been? Where are you going to?

Abstract: Quantitative structure–activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR stu...