EXPLORATION OF CAUSAL AND CORRELATIONAL MODELLING IN CANCER : GLIOBLASTOMA CASE STUDY

EXPLORATION OF CAUSAL AND CORRELATIONAL MODELLING IN CANCER : GLIOBLASTOMA CASE STUDY

Bibliographic Details
Main Author: Saengphueng, Sompop
Language:English
Published: Case Western Reserve University School of Graduate Studies / OhioLINK 2015
Subjects:
Biomedical Engineering
Biology
Systems Science
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=case1428074720
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-case14280747202021-08-03T06:29:47Z EXPLORATION OF CAUSAL AND CORRELATIONAL MODELLING IN CANCER : GLIOBLASTOMA CASE STUDY Saengphueng, Sompop Biomedical Engineering Biology Systems Science Physicians observe and study human diseases to gain insight into these complex biological systems and to develop appropriate therapies. Understanding the disease mechanism, which factors regulate or control others, may lead us to find the methods for stopping or preventing severity of it's symptoms or causes by using drugs or alternative treatments. The treatment of a disease requires a chance discovery that intake of a compound or prior vaccination affects the outcome. There are many examples of this: Jenner's discovery that milkmaids did not suffer the full effects of smallpox. The discovery that nitrogen mustards (chemical weapons) suppressed lymphoid cell proliferation led to the first successful treatment for lymphoma long before the causes of lymphoma were understood. There are several factors that may contribute to cause a disease. Determination of the causes is fundamental to fully describe a disease. Based on a statistical method from Shipley's book \emph{"Cause and Correlation in Biology"}, we fashion an approach to provide a description of the biological phenomenon's functions in a statistical way. To provide effective treatment is to make an observation that exposure to certain factors (such as chemical or radiation, etc.) affects a positive outcome in diseased individuals. Improving treatments may benefit from causal models that permit simulations that explore alternative treatment interventions. Determining how to provide a causal description of the phenomenon starting from statistical experiments presents a challenge because when we analyze all factors statistically and try to identify their relationship by judging from their correlation, it cannot tell us what are causes or what are effects. When we select the factors that are highly correlated to the output, i.e. in a regression model, some factors or regressors may not be concluded that they are the cause of output i.e. the rain is the cause of the mud, but the mud is not the cause of rain.The measurable criteria help to classify tumors into groups that help physicians predict tumor behavior. Among these are tumor size, metastatic status, and pathological considerations (cellular features) \citep{Sheehan2010}. Currently, there is a large effort to identify molecular markers that may help to refine these predictions, and more importantly, may guide targeted therapy \citep{Ellis2008,Clarke2004}. There are literally many tens of thousands of potential markers. As a means for measurement, more markers (genetic, epi-genetic, and biochemical measurements, as well as higher level cellular measurements of process and function) have provided measurements, but making sense of the measurements constitutes a major problem in biomedical science \citep{Mayeux2004,Dunckley2005,Kim2014,Roy2014,Zhu2014,Hasenauer2012}.For this reason, we will use the method proposed by Shipley to provide us a fundamental understanding of the brain cancer’s functioning \citep{Shipley2000,Shipley2009}. Shipley's method relies on the structural equation modeling (SEM) and the software TETRAD V to assist with the identification of the structure view of a system during a statistical experiment. This approach also provides useful information for differentiating healthy from unhealthy states in terms of causality. In an effort to begin to address this problem in a novel way, we have explored an approach to identify causality once we determine the most influential inputs. 2015-06-03 English text Case Western Reserve University School of Graduate Studies / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=case1428074720 http://rave.ohiolink.edu/etdc/view?acc_num=case1428074720 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Biomedical Engineering
Biology
Systems Science
spellingShingle Biomedical Engineering
Biology
Systems Science
Saengphueng, Sompop
EXPLORATION OF CAUSAL AND CORRELATIONAL MODELLING IN CANCER : GLIOBLASTOMA CASE STUDY
author Saengphueng, Sompop
author_facet Saengphueng, Sompop
author_sort Saengphueng, Sompop
title EXPLORATION OF CAUSAL AND CORRELATIONAL MODELLING IN CANCER : GLIOBLASTOMA CASE STUDY
title_short EXPLORATION OF CAUSAL AND CORRELATIONAL MODELLING IN CANCER : GLIOBLASTOMA CASE STUDY
title_full EXPLORATION OF CAUSAL AND CORRELATIONAL MODELLING IN CANCER : GLIOBLASTOMA CASE STUDY
title_fullStr EXPLORATION OF CAUSAL AND CORRELATIONAL MODELLING IN CANCER : GLIOBLASTOMA CASE STUDY
title_full_unstemmed EXPLORATION OF CAUSAL AND CORRELATIONAL MODELLING IN CANCER : GLIOBLASTOMA CASE STUDY
title_sort exploration of causal and correlational modelling in cancer : glioblastoma case study
publisher Case Western Reserve University School of Graduate Studies / OhioLINK
publishDate 2015
url http://rave.ohiolink.edu/etdc/view?acc_num=case1428074720
work_keys_str_mv AT saengphuengsompop explorationofcausalandcorrelationalmodellingincancerglioblastomacasestudy
_version_ 1719437841853841408

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