Delivery number one

intro.aux

@@ -1,36 +1,34 @@
 \relax 
 \abx@aux@sortscheme{nyt}
-\abx@aux@cite{biomarker1}
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-\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {1}State of today}{1}}
-\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {1.1}Biomarkers}{1}}
+\abx@aux@cite{biomarker1}
 \abx@aux@cite{beyondpsa}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{2}}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {2}Biomarkers}{2}}
 \abx@aux@cite{cancerfacts}
 \abx@aux@cite{ngs1}
 \abx@aux@cite{personalized}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {3}Prostate specific antigen}{3}}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {4}Next-generation sequencing}{3}}
 \abx@aux@cite{psa-age}
 \abx@aux@cite{pca3-size}
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-\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {1.3}Next-generation sequencing}{3}}
-\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {2}Next generation of biomarkers}{3}}
-\abx@aux@cite{networkmodels}
 \abx@aux@cite{bayesiannetworks}
-\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {3}Network}{4}}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {5}Next generation of biomarkers}{4}}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {6}Network}{4}}
 \abx@aux@cite{siri}
 \abx@aux@cite{iri}
 \abx@aux@cite{gm}
 \abx@aux@cite{str}
 \abx@aux@cite{iridb}
 \abx@aux@cite{gmdb}
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-\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {5}Cytoscape}{5}}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {7}Clustering}{5}}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {8}Cytoscape}{5}}
 \abx@aux@cite{biopython}
 \abx@aux@cite{cytoscape-osgi}
 \abx@aux@cite{modularization}

intro.bbl

@@ -366,25 +366,6 @@
       \field{volume}{2}
       \field{year}{2010}
     \endentry
-    \entry{networkmodels}{article}{}
-      \name{labelname}{2}{}{%
-        {{hash=25504e4a8fa9f7fbfbc81a03b943d827}{Pe'er}{P\bibinitperiod}{Dana}{D\bibinitperiod}{}{}{}{}}%
-        {{hash=d6f13ab6c14862674be5e69417fb8185}{Hacohen}{H\bibinitperiod}{Nir}{N\bibinitperiod}{}{}{}{}}%
-      }
-      \name{author}{2}{}{%
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-        {{hash=d6f13ab6c14862674be5e69417fb8185}{Hacohen}{H\bibinitperiod}{Nir}{N\bibinitperiod}{}{}{}{}}%
-      }
-      \strng{namehash}{407ae9e1a151afb407c4e7b6d7c3c370}
-      \strng{fullhash}{407ae9e1a151afb407c4e7b6d7c3c370}
-      \field{sortinit}{P}
-      \field{labeltitle}{Principles and Strategies for Developing Network Models in Cancer}
-      \field{journaltitle}{CellPress}
-      \field{month}{03}
-      \field{title}{Principles and Strategies for Developing Network Models in Cancer}
-      \field{volume}{144}
-      \field{year}{2011}
-    \endentry
     \entry{beyondpsa}{article}{}
       \name{labelname}{4}{}{%
         {{hash=f7b3439256ee92b6627e3236c722fa79}{Prensner}{P\bibinitperiod}{John\bibnamedelima R.}{J\bibinitperiod\bibinitdelim R\bibinitperiod}{}{}{}{}}%

intro.bcf

@@ -1974,20 +1974,19 @@
     <bcf:citekey order="7">psa-age</bcf:citekey>
     <bcf:citekey order="8">pca3-size</bcf:citekey>
     <bcf:citekey order="9">beyondpsa</bcf:citekey>
-    <bcf:citekey order="10">networkmodels</bcf:citekey>
-    <bcf:citekey order="11">bayesiannetworks</bcf:citekey>
-    <bcf:citekey order="12">siri</bcf:citekey>
-    <bcf:citekey order="13">iri</bcf:citekey>
-    <bcf:citekey order="14">gm</bcf:citekey>
-    <bcf:citekey order="15">str</bcf:citekey>
-    <bcf:citekey order="16">iridb</bcf:citekey>
-    <bcf:citekey order="17">gmdb</bcf:citekey>
-    <bcf:citekey order="18">biopython</bcf:citekey>
-    <bcf:citekey order="19">cytoscape-osgi</bcf:citekey>
-    <bcf:citekey order="20">modularization</bcf:citekey>
-    <bcf:citekey order="21">designpattern</bcf:citekey>
-    <bcf:citekey order="22">cleancode</bcf:citekey>
-    <bcf:citekey order="23">galaxy</bcf:citekey>
+    <bcf:citekey order="10">bayesiannetworks</bcf:citekey>
+    <bcf:citekey order="11">siri</bcf:citekey>
+    <bcf:citekey order="12">iri</bcf:citekey>
+    <bcf:citekey order="13">gm</bcf:citekey>
+    <bcf:citekey order="14">str</bcf:citekey>
+    <bcf:citekey order="15">iridb</bcf:citekey>
+    <bcf:citekey order="16">gmdb</bcf:citekey>
+    <bcf:citekey order="17">biopython</bcf:citekey>
+    <bcf:citekey order="18">cytoscape-osgi</bcf:citekey>
+    <bcf:citekey order="19">modularization</bcf:citekey>
+    <bcf:citekey order="20">designpattern</bcf:citekey>
+    <bcf:citekey order="21">cleancode</bcf:citekey>
+    <bcf:citekey order="22">galaxy</bcf:citekey>
   </bcf:section>
   <bcf:sortlist section="0" type="entry" label="nyt">
   <bcf:sorting>

intro.blg

@@ -1,17 +1,17 @@
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intro.log

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intro.tex

@@ -10,12 +10,12 @@
 \begin{document}
 \maketitle
 \tableofcontents{}
-\part{Introduction}
+\newpage
+\section{Introduction}
 To this day we still struggle with cancer. Even with all our modern equipment and knowledge we have still not been able
 to tame this horrible disease. My thesis is about making a tool, named Ranklust, which gives cancer researchers an
 easier way of identifying network biomarkers in cancer. 
-\section{State of today}
-\subsection{Biomarkers}
+\section{Biomarkers}
 Biomarkers are at the centre of this thesis. They are what Ranklust should be able to detect and rank in order to
 identify network biomarkers, and not just single molecules of them. A biomarker is a "biological measure of a biological
 state" \cite{biomarker1}. It can be represented by the levels of a specific protein in our blood, a specific gene, or a
@@ -63,7 +63,7 @@ \subsection{Biomarkers}
     \item Modifiable with treatment
     \item Consistent across gender and ethnic groups
 \end{itemize}
-\subsection{Prostate specific antigen}
+\section{Prostate specific antigen}
 An example of a single molecule biomarker is the prostate specific antigen (PSA). This is a protein produced by the 
 prostate gland in male humans. The identification of cancer with PSA is simple, the higher the level of PSA, measured in
 ng/mL (nanograms per milliliter), the higher is the chance of the patient having prostate cancer \cite{cancerfacts}.
@@ -83,7 +83,7 @@ \subsection{Prostate specific antigen}
 of not taking any action against it at all. So there is need for a new biomarker, or at least a better way to diagnose
 and predict the right treatment.
 
-\subsection{Next-generation sequencing}
+\section{Next-generation sequencing}
 Today, rapid analyzing of genes and proteins are made available through Next-Generation Sequencing (NGS)\cite{ngs1}.
 % Find a better source for this!
 This opens up the possibility of looking at a bigger picture when trying to diagnose cancer patients. Through acquiring
@@ -107,20 +107,20 @@ \section{Next generation of biomarkers}
 But all of this is based on single genes or proteins. What if we looked at whole networks as biomarkers?
 
 \section{Network}
-Shit goes dooown \cite{networkmodels}.
-\begin{itemize}
-    \item Cells contain a vast array of molecular structures that come together to form complex, dynamic, and plastic
-        networks
-    \item For example, where in the molecular network of a tumor should we perturb with drug to reduce tumor
-        proliferation or metastasis
-    \item mTOR having poor results in clinical trials and even having subsequently increased cell proliferation
-    \item For example, if protein A induces expression of protein B, then we expect to see high leveles of protein B
-        whenever levels or specific molecular states of its activator A are high. The reverse of this logic is that
-        statistical correlation between protein states indicates a potential interaction between them
-    \item Although nonlinear relations frequently occur in biology, linear regression models are more robust, and thus
-        they often give better results, even when the underlying model is nonlinear
-    \item Networks are not fixed: The role of context and dynamics
-\end{itemize}
+%Shit goes dooown \cite{networkmodels}.
+%\begin{itemize}
+%    \item Cells contain a vast array of molecular structures that come together to form complex, dynamic, and plastic
+%        networks
+%    \item For example, where in the molecular network of a tumor should we perturb with drug to reduce tumor
+%        proliferation or metastasis
+%    \item mTOR having poor results in clinical trials and even having subsequently increased cell proliferation
+%    \item For example, if protein A induces expression of protein B, then we expect to see high leveles of protein B
+%        whenever levels or specific molecular states of its activator A are high. The reverse of this logic is that
+%        statistical correlation between protein states indicates a potential interaction between them
+%    \item Although nonlinear relations frequently occur in biology, linear regression models are more robust, and thus
+%        they often give better results, even when the underlying model is nonlinear
+%    \item Networks are not fixed: The role of context and dynamics
+%\end{itemize}
 Viewing the cell as a network of proteins and genes presents us with several assumptions to make. Firstly, there is no
 physical connection between the proteins and genes, which represents the nodes in the network. So a way of defining
 edges between nodes has to be established. It exists several ways of doing this, but there has yet to be discovered a

intro.toc

@@ -2,20 +2,18 @@
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 \select@language {UKenglish}
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-\contentsline {part}{I\hspace {1em}Introduction}{1}
+\contentsline {section}{\numberline {1}Introduction}{2}
 \defcounter {refsection}{0}\relax 
-\contentsline {section}{\numberline {1}State of today}{1}
+\contentsline {section}{\numberline {2}Biomarkers}{2}
 \defcounter {refsection}{0}\relax 
-\contentsline {subsection}{\numberline {1.1}Biomarkers}{1}
+\contentsline {section}{\numberline {3}Prostate specific antigen}{3}
 \defcounter {refsection}{0}\relax 
-\contentsline {subsection}{\numberline {1.2}Prostate specific antigen}{3}
+\contentsline {section}{\numberline {4}Next-generation sequencing}{3}
 \defcounter {refsection}{0}\relax 
-\contentsline {subsection}{\numberline {1.3}Next-generation sequencing}{3}
+\contentsline {section}{\numberline {5}Next generation of biomarkers}{4}
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-\contentsline {section}{\numberline {2}Next generation of biomarkers}{3}
+\contentsline {section}{\numberline {6}Network}{4}
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+\contentsline {section}{\numberline {7}Clustering}{5}
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-\contentsline {section}{\numberline {4}Clustering}{5}
-\defcounter {refsection}{0}\relax 
-\contentsline {section}{\numberline {5}Cytoscape}{5}
+\contentsline {section}{\numberline {8}Cytoscape}{5}