main.tex

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%% Custom commands

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%% Start Document
\begin{document}

\input{./tex/Cover.tex}
\thispagestyle{empty}

\begin{flushright}
%% insert inscription (page 2)
\end{flushright}

%% Epigraph
\chapter*{}
\pagenumbering{gobble}% Remove page numbers (and reset to 1)
\epigraph{\quotes{\emph{No one know nothing,\\everyone know something,\\but something is nothing to someone,\\while\\something is important to everybody}}}{Daudi, Manyara}
%\epigraph{\quotes{\emph{Software is like sex:\\it's better when it's free}}{Linus Torvalds}}

%% Abstract
\pagenumbering{gobble}% Remove page numbers (and reset to 1)
\input{./tex/Abstract.tex}

\tableofcontents
\newpage

%% Introduction

\pagenumbering{arabic}% Arabic page numbers (and reset to 1)
\input{./tex/Introduction.tex}

%% Chapter 1 - DNetPRO algorithm

\input{./tex/Chapter1/chapter1.tex}
% Introduction to feature selection problem and theoretical background. Focus on biological Big Data and problems related.
% Description of the method and efficiency a biological toy model.
% Description of the algorithm implementation with focus on performances of the new implementation.
% Application of the DNetPRO on the Synapse dataset (mRNA, miRNA, RPPA) of Yuan et al. with discussion about obtained performances compared with the most common ML methods and signature characterization
% Application of the DNetPRO on the Cytokine dataset with data description and obtained results
% Application of the DNetPRO on the Bovini dataset with data description and obtained results with biological interpretation


%% Chapter 2 - Neural Network and Byron

\input{./tex/Chapter2/chapter2.tex}
% Description of the modern deep neural networks with potential applications
% Neural Network laboratory developed in pure numpy. Study of the neural network functionality and testing of the code against tensorflow
% rFBP library to optimize the Julia code with reference to the Scorer library and application to COMPARE data (Daniele Thesis)
% Neural Network library for parallel computing developed in C++ with python wrap. Description of the algorithms and strategies chosen in the library development.
% Introduction on the image classification and detection with Yolo architecture. Implementation in Byron with description of performances against darknet (original implementation). Focus on performances (time, memory, cpu).
% Introduction on Super Resolution problem with focus on state-of-art neural network architecture. Description of the Byron implementation and application on NMR data with the most common measurements. Super-resolution allows better detection!
% Introduction on Image Segmentation problem and application of Unet (Byron implementation) on femur images (TODO)


%% Chapter 3 - CHIMeRA project

\input{./tex/Chapter3/chapter3.tex}
% Many public datasets available. Description of the database used in chimera
% Description of the web scraping techinques used to obtain the "no-public" datasets. Reference to the github project
% What is CHIMeRA project and which potentiality it has. Description of the database created and of the query implemented to obtain the results (TODO: better query)
% Some query examples like leukemia subnetwork and PRNP subnetwork. Description of the information extracted by these subnetworks


%% Chapter 4 - cardiological data

% \input{./tex/Chapter4/AppitechData.tex}
% % Description of the pulsoximmetry and of the cardiological data analyzed

% \input{./tex/Chapter4/CardioFeature.tex}
% % Description of the features extracted on the cardiological dataset

% \input{./tex/Chapter4/AgePrediction.tex}
% % Pipeline of the feature selection with age prediction obtained on cardio data

%% Conclusion

\input{./tex/Conclusions.tex}

%% Acknowledgment

\pagenumbering{gobble}% Remove page numbers (and reset to 1)
\input{./tex/Acknowledgment.tex}

%% Appendix

\input{./tex/Appendix/appendix.tex}
% reference to the studies about performances of the developed algorithms.

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%\clearpage

%\input{./tex/Relazione.tex}
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\clearpage


\bibliographystyle{abbrv}
\bibliography{biblio}



\end{document}