Model Based Hypothesis Testing In Biomedicine
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Model Based Hypothesis Testing In Biomedicine
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Author : Rikard Johansson
language : en
Publisher: Linköping University Electronic Press
Release Date : 2017-10-03
Model Based Hypothesis Testing In Biomedicine written by Rikard Johansson and has been published by Linköping University Electronic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-03 with categories.
The utilization of mathematical tools within biology and medicine has traditionally been less widespread compared to other hard sciences, such as physics and chemistry. However, an increased need for tools such as data processing, bioinformatics, statistics, and mathematical modeling, have emerged due to advancements during the last decades. These advancements are partly due to the development of high-throughput experimental procedures and techniques, which produce ever increasing amounts of data. For all aspects of biology and medicine, these data reveal a high level of inter-connectivity between components, which operate on many levels of control, and with multiple feedbacks both between and within each level of control. However, the availability of these large-scale data is not synonymous to a detailed mechanistic understanding of the underlying system. Rather, a mechanistic understanding is gained first when we construct a hypothesis, and test its predictions experimentally. Identifying interesting predictions that are quantitative in nature, generally requires mathematical modeling. This, in turn, requires that the studied system can be formulated into a mathematical model, such as a series of ordinary differential equations, where different hypotheses can be expressed as precise mathematical expressions that influence the output of the model. Within specific sub-domains of biology, the utilization of mathematical models have had a long tradition, such as the modeling done on electrophysiology by Hodgkin and Huxley in the 1950s. However, it is only in recent years, with the arrival of the field known as systems biology that mathematical modeling has become more commonplace. The somewhat slow adaptation of mathematical modeling in biology is partly due to historical differences in training and terminology, as well as in a lack of awareness of showcases illustrating how modeling can make a difference, or even be required, for a correct analysis of the experimental data. In this work, I provide such showcases by demonstrating the universality and applicability of mathematical modeling and hypothesis testing in three disparate biological systems. In Paper II, we demonstrate how mathematical modeling is necessary for the correct interpretation and analysis of dominant negative inhibition data in insulin signaling in primary human adipocytes. In Paper III, we use modeling to determine transport rates across the nuclear membrane in yeast cells, and we show how this technique is superior to traditional curve-fitting methods. We also demonstrate the issue of population heterogeneity and the need to account for individual differences between cells and the population at large. In Paper IV, we use mathematical modeling to reject three hypotheses concerning the phenomenon of facilitation in pyramidal nerve cells in rats and mice. We also show how one surviving hypothesis can explain all data and adequately describe independent validation data. Finally, in Paper I, we develop a method for model selection and discrimination using parametric bootstrapping and the combination of several different empirical distributions of traditional statistical tests. We show how the empirical log-likelihood ratio test is the best combination of two tests and how this can be used, not only for model selection, but also for model discrimination. In conclusion, mathematical modeling is a valuable tool for analyzing data and testing biological hypotheses, regardless of the underlying biological system. Further development of modeling methods and applications are therefore important since these will in all likelihood play a crucial role in all future aspects of biology and medicine, especially in dealing with the burden of increasing amounts of data that is made available with new experimental techniques. Användandet av matematiska verktyg har inom biologi och medicin traditionellt sett varit mindre utbredd jämfört med andra ämnen inom naturvetenskapen, såsom fysik och kemi. Ett ökat behov av verktyg som databehandling, bioinformatik, statistik och matematisk modellering har trätt fram tack vare framsteg under de senaste decennierna. Dessa framsteg är delvis ett resultat av utvecklingen av storskaliga datainsamlingstekniker. Inom alla områden av biologi och medicin så har dessa data avslöjat en hög nivå av interkonnektivitet mellan komponenter, verksamma på många kontrollnivåer och med flera återkopplingar både mellan och inom varje nivå av kontroll. Tillgång till storskaliga data är emellertid inte synonymt med en detaljerad mekanistisk förståelse för det underliggande systemet. Snarare uppnås en mekanisk förståelse först när vi bygger en hypotes vars prediktioner vi kan testa experimentellt. Att identifiera intressanta prediktioner som är av kvantitativ natur, kräver generellt sett matematisk modellering. Detta kräver i sin tur att det studerade systemet kan formuleras till en matematisk modell, såsom en serie ordinära differentialekvationer, där olika hypoteser kan uttryckas som precisa matematiska uttryck som påverkar modellens output. Inom vissa delområden av biologin har utnyttjandet av matematiska modeller haft en lång tradition, såsom den modellering gjord inom elektrofysiologi av Hodgkin och Huxley på 1950?talet. Det är emellertid just på senare år, med ankomsten av fältet systembiologi, som matematisk modellering har blivit ett vanligt inslag. Den något långsamma adapteringen av matematisk modellering inom biologi är bl.a. grundad i historiska skillnader i träning och terminologi, samt brist på medvetenhet om exempel som illustrerar hur modellering kan göra skillnad och faktiskt ofta är ett krav för en korrekt analys av experimentella data. I detta arbete tillhandahåller jag sådana exempel och demonstrerar den matematiska modelleringens och hypotestestningens allmängiltighet och tillämpbarhet i tre olika biologiska system. I Arbete II visar vi hur matematisk modellering är nödvändig för en korrekt tolkning och analys av dominant-negativ-inhiberingsdata vid insulinsignalering i primära humana adipocyter. I Arbete III använder vi modellering för att bestämma transporthastigheter över cellkärnmembranet i jästceller, och vi visar hur denna teknik är överlägsen traditionella kurvpassningsmetoder. Vi demonstrerar också frågan om populationsheterogenitet och behovet av att ta hänsyn till individuella skillnader mellan celler och befolkningen som helhet. I Arbete IV använder vi matematisk modellering för att förkasta tre hypoteser om hur fenomenet facilitering uppstår i pyramidala nervceller hos råttor och möss. Vi visar också hur en överlevande hypotes kan beskriva all data, inklusive oberoende valideringsdata. Slutligen utvecklar vi i Arbete I en metod för modellselektion och modelldiskriminering med hjälp av parametrisk ”bootstrapping” samt kombinationen av olika empiriska fördelningar av traditionella statistiska tester. Vi visar hur det empiriska ”log-likelihood-ratio-testet” är den bästa kombinationen av två tester och hur testet är applicerbart, inte bara för modellselektion, utan också för modelldiskriminering. Sammanfattningsvis är matematisk modellering ett värdefullt verktyg för att analysera data och testa biologiska hypoteser, oavsett underliggande biologiskt system. Vidare utveckling av modelleringsmetoder och tillämpningar är därför viktigt eftersom dessa sannolikt kommer att spela en avgörande roll i framtiden för biologi och medicin, särskilt när det gäller att hantera belastningen från ökande datamängder som blir tillgänglig med nya experimentella tekniker.
Model Based Inference In The Life Sciences
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Author : David R. Anderson
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-12-22
Model Based Inference In The Life Sciences written by David R. Anderson and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-22 with Science categories.
This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.
Interpreting Biomedical Science
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Author : Ülo Maiväli
language : en
Publisher: Academic Press
Release Date : 2015-06-12
Interpreting Biomedical Science written by Ülo Maiväli and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-12 with Science categories.
Interpreting Biomedical Science: Experiment, Evidence, and Belief discusses what can go wrong in biological science, providing an unbiased view and cohesive understanding of scientific methods, statistics, data interpretation, and scientific ethics that are illustrated with practical examples and real-life applications. Casting a wide net, the reader is exposed to scientific problems and solutions through informed perspectives from history, philosophy, sociology, and the social psychology of science. The book shows the differences and similarities between disciplines and different eras and illustrates the concept that while sound methodology is necessary for the progress of science, we cannot succeed without a right culture of doing things. Features theoretical concepts accompanied by examples from biological literature Contains an introduction to various methods, with an emphasis on statistical hypothesis testing Presents a clear argument that ties the motivations and ethics of individual scientists to the success of their science Provides recommendations on how to safeguard against scientific misconduct, fraud, and retractions Arms young scientists with practical knowledge that they can use every day
Good Research Practice In Non Clinical Pharmacology And Biomedicine
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Author : Anton Bespalov
language : en
Publisher: Springer Nature
Release Date : 2020-01-01
Good Research Practice In Non Clinical Pharmacology And Biomedicine written by Anton Bespalov and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-01 with Cardiology categories.
This open access book, published under a CC BY 4.0 license in the Pubmed indexed book series Handbook of Experimental Pharmacology, provides up-to-date information on best practice to improve experimental design and quality of research in non-clinical pharmacology and biomedicine.
Empirical Likelihood Methods In Biomedicine And Health
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Author : Albert Vexler
language : en
Publisher: CRC Press
Release Date : 2018-09-03
Empirical Likelihood Methods In Biomedicine And Health written by Albert Vexler and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Mathematics categories.
Empirical Likelihood Methods in Biomedicine and Health provides a compendium of nonparametric likelihood statistical techniques in the perspective of health research applications. It includes detailed descriptions of the theoretical underpinnings of recently developed empirical likelihood-based methods. The emphasis throughout is on the application of the methods to the health sciences, with worked examples using real data. Provides a systematic overview of novel empirical likelihood techniques. Presents a good balance of theory, methods, and applications. Features detailed worked examples to illustrate the application of the methods. Includes R code for implementation. The book material is attractive and easily understandable to scientists who are new to the research area and may attract statisticians interested in learning more about advanced nonparametric topics including various modern empirical likelihood methods. The book can be used by graduate students majoring in biostatistics, or in a related field, particularly for those who are interested in nonparametric methods with direct applications in Biomedicine.
Natural And Artificial Computation For Biomedicine And Neuroscience
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Author : José Manuel Ferrández Vicente
language : en
Publisher: Springer
Release Date : 2017-06-10
Natural And Artificial Computation For Biomedicine And Neuroscience written by José Manuel Ferrández Vicente and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-10 with Computers categories.
The two volumes LNCS 10337 and 10338 constitute the proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, held in Corunna, Spain, in June 2017. The total of 102 full papers was carefully reviewed and selected from 194 submissions during two rounds of reviewing and improvement. The papers are organized in two volumes, one on natural and artificial computation for biomedicine and neuroscience, addressing topics such as theoretical neural computation; models; natural computing in bioinformatics; physiological computing in affective smart environments; emotions; as well as signal processing and machine learning applied to biomedical and neuroscience applications. The second volume deals with biomedical applications, based on natural and artificial computing and addresses topics such as biomedical applications; mobile brain computer interaction; human robot interaction; deep learning; machine learning applied to big data analysis; computational intelligence in data coding and transmission; and applications.
Cognitive Informatics In Health And Biomedicine
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Author : Vimla L. Patel
language : en
Publisher: Springer
Release Date : 2017-05-31
Cognitive Informatics In Health And Biomedicine written by Vimla L. Patel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-31 with Medical categories.
As health care is moving toward a team effort with patients as partners, this book provides guidance on the optimized use of health information and supporting technologies, and how people think and make decisions that affect their health and wellbeing. It focuses on investigations of how general public understand health information, assess risky behaviors, make healthcare decisions, and how they use health information technologies. e-health technologies have opened up new horizons for promoting increased self-reliance in patients. Although information technologies are now in widespread use, there is often a disparity between the scientific and technological knowledge underlying health care practices and the cultural beliefs, mental models, and cognitive representations of illness and disease. Misconceptions based on inaccurate perceptions and mental models, and flawed prior beliefs could lead to miscommunication as well as to erroneous decisions about individuals’ own health or the health of their family members. Cognitive Informatics in Health and Biomedicine: Understanding and Modeling Health Behaviors presents state of the art research in cognitive informatics for assessing the impact of patient behaviour. It is designed to assist all involved at the intersection of the health care institution and the patient and covers contributions from recognized researchers and leaders in the field.
Image Analysis And Modeling In Ophthalmology
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Author : Eddie Y. K. Ng
language : en
Publisher: CRC Press
Release Date : 2014-02-11
Image Analysis And Modeling In Ophthalmology written by Eddie Y. K. Ng and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02-11 with Medical categories.
Digital fundus images can effectively diagnose glaucoma and diabetes retinopathy, while infrared imaging can show changes in the vascular tissues. Likening the eye to the conventional camera, Image Analysis and Modeling in Ophthalmology explores the application of advanced image processing in ocular imaging. This book considers how images can be us
The Use Of Restricted Significance Tests In Clinical Trials
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Author : David Salsburg
language : en
Publisher: Springer Science & Business Media
Release Date : 1992-08-06
The Use Of Restricted Significance Tests In Clinical Trials written by David Salsburg and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992-08-06 with Mathematics categories.
This thought-provoking book discusses the use of statistics in randomized clinical trials. Its aim is two-fold: firstly, it presents a clear account of the design and analysis of experiments in this setting which stresses the foundational issues involved. Secondly, the book seeks to develop the specific tools of analysis which can be derived from Neyman's model of restricted tests. The book is based on the author's many years of experience of clinical trials. Throughout, examples are used from a variety of types of study. As a result, all statisticians and research scientists who work on clinical trials will find this presentation clear and accessible, and very relevant to their own research interests.
Some Hypothesis Testing Results For Two Way Linear Models In Clinical Trials
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Author : Bin Cheng
language : en
Publisher:
Release Date : 2004
Some Hypothesis Testing Results For Two Way Linear Models In Clinical Trials written by Bin Cheng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.