Added lecture.

lectures/liacs_ai/Makefile

+../../submodules/presentation/Makefile
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lectures/liacs_ai/NovaSeq6000.jpg

+../../submodules/presentation-pics/pics/NovaSeq6000.jpg
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lectures/liacs_ai/beamerthemelumc.sty

+../../submodules/presentation/beamerthemelumc.sty
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lectures/liacs_ai/liacs_ai.tex

+\documentclass[slidestop]{beamer}
+
+\author{Jeroen F.J. Laros}
+\title{Artificial Intelligence in Genetics}
+\providecommand{\mySubTitle}{Requirements and examples}
+\providecommand{\myConference}{ML/AI meeting LUMC - LIACS}
+\providecommand{\myDate}{27-06-2018}
+\providecommand{\myGroup}{Research Software Engineering}
+\providecommand{\myDepartment}{Department of Human Genetics}
+\providecommand{\myCenter}{Center for Human and Clinical Genetics}
+
+\usetheme{lumc}
+\usepackage{fancyvrb}
+
+\begin{document}
+
+% This disables the \pause command, handy in the editing phase.
+%\renewcommand{\pause}{}
+
+% Make the title slide.
+\makeTitleSlide{\includegraphics[height=2.7cm]{sanger_trace}}
+
+% First page of the presentation.
+\section{Introduction}
+\makeTableOfContents
+
+
+\section{Sequencing}
+\subsection{From DNA to sequences}
+\begin{pframe}
+  \begin{minipage}[t]{0.47\textwidth}\begin{figure}[]
+    \begin{center}
+      \includegraphics[width=\textwidth]{NovaSeq6000}
+    \end{center}
+    \caption{Illumina NovaSeq 6000.}
+  \end{figure}
+  \end{minipage}
+  \hfill
+  \begin{minipage}[t]{0.47\textwidth}
+    \begin{Verbatim}[fontsize=\tiny]
+      @K0187:120:GFLBXX:1:1101:2950:1191 1:N:0:CTTGAG
+      TTAGGAAATAATAAATTGTAGTTTTTTTTATGATTTGGTTGAATTGATT
+      +
+      AAAFFJJJJJJ-AJFFFJ-FF<F<JJJAFJFFFJAFJJ<-FFFA<FFAA
+      @K0187:120:GFLBXX:1:1101:7710:1209 1:N:0:CTTGAG
+      GTCCACTAGAACTTGTAGAGCTGGAACCAACTGATTTGCAAAAGCAAAA
+      +
+      AA-FFFJAAAF-FFAJJJFJJJ--AFFJJFFJJFAFA-FJJJJFJJJJF
+      @K0187:120:GFLBXX:1:1101:17269:1209 1:N:0:CTTGAG
+      GTACTTGATGAATATTTACTAAAGAATGGAAGGAAAAAAGAAAGAAGGA
+      +
+      AAFFFJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJ
+      @K0187:120:GFLBXX:1:1101:28067:1209 1:N:0:CTTGAG
+      TGGGTGTGTTAAGAATGACATTCAACGCAGTCTCTTTACCACCTCCCCA
+      +
+      AAA-FJFJFJFAJJJJFFJFAFJJJJFFJFAJJJJFFJJJAAJ7FFJJA
+      @K0187:120:GFLBXX:1:1101:1367:1226 1:N:0:CTTGAG
+      NTAGGTGTAAAAACATGATCATCGAAGAGACAAGAAATAGAACGTATTT
+      +
+      #AAFFFJFJJJJJJJJJJJJJJJJFJFJFAFJJJJJJJJJJJJJJJAFF
+      @K0187:120:GFLBXX:1:1101:8410:1226 1:N:0:CTTGAG
+      AGTGAGACCCTCTCTCTAAAAAGAAAAAAGAAAAAAAATTATGATGTTT
+      +
+      AAFAFFJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJ
+    \end{Verbatim}
+  \end{minipage}
+\end{pframe}
+
+
+\section{Background}
+\subsection{Current status and requirements}
+\begin{pframe}
+  We work a lot with \emph{sequences} (over an alphabet of four letters).
+  \begin{itemize}
+    \item Alignment / variant calling.
+    \item \textit{De novo} assembly / gene prediction.
+    \item $k$-mer profiling.
+  \end{itemize}
+  \bigskip
+
+  For most of these problems, there are efficient deterministic algorithms
+  available, with the exception of \textit{de novo} assembly.
+  \bigskip
+  \pause
+
+  We have a strong need for being able to \emph{explain} results.
+  \begin{itemize}
+    \item Techniques like decision trees.
+    \item Optimisation of procedures, which are in the end very clear.
+  \end{itemize}
+\end{pframe}
+
+
+\section{Farmacogenetics}
+\subsection{Personalised Medicine}
+\begin{pframe}
+  Predict drug response from patient genetics.
+  \begin{itemize}
+    \item The current gold standard $r^2 \approx 0.55$.
+  \end{itemize}
+  \bigskip
+
+  Over the years, we have collected quite some data.
+  \begin{itemize}
+    \item High quality genotypes from sequencing.
+    \item $550$ \textit{in vivo} metabolite measurements.
+  \end{itemize}
+  \bigskip
+  \pause
+
+  The approach, a neural network with:
+  \begin{itemize}
+    \item Binary vectors indicating presence/absence of genetic features (two
+      sets of inputs due to ploidy) as input.
+    \item Predicted metabolite measurement (scalar) as output.
+  \end{itemize}
+
+  \vfill
+  \permfoot{Guy Allard}
+\end{pframe}
+
+\begin{pframe}
+  \vspace{-0.5cm}
+  \begin{figure}[]
+    \begin{center}
+      \includegraphics[height=0.8\textheight]{pm_network}
+    \end{center}
+    \caption{Model architecture.}
+  \end{figure}
+\end{pframe}
+
+\begin{pframe}
+  Techniques and libraries:
+  \begin{itemize}
+    \item Model constructed and trained using TensorFlow.
+    \item Allele submodules share the same layers (same model).
+  \end{itemize}
+
+  \begin{figure}[]
+    \begin{center}
+      \includegraphics[width=\textwidth]{allele}
+    \end{center}
+    \caption{Visualising contribution of features.}
+  \end{figure}
+  \vspace{-0.5cm}
+
+  Results:
+  \begin{itemize}
+    \item $10$-fold cross validation, $r^2 \approx 0.7$.
+    \item Allele submodule ($p_{a1}$) can be interrogated and the effects of
+      genetic features explored.
+  \end{itemize}
+
+  \vfill
+  \permfoot{Guy Allard}
+\end{pframe}
+
+
+%\section{Single cell sequencing}
+%\subsection{???}
+%\begin{pframe}
+%  Slides here.
+%\end{pframe}
+
+
+\section{Clinical Genetics}
+% Use case 2: Variant classification for the purposes of clinical diagnostics
+% in the clinic is currently a tedious manual task. Clinicians have to go
+% through lists of dozens of variants, and manually curate them. This task can
+% take up to several hours per patient. It would greatly speed up diagnosis if
+% the clinical could have an automated ranking, which would select the best
+% candidate hit first. As variants are automatically annotated with dozens of
+% annotation fields, we reasoned we could very well use some machine learnings
+% methods for this purpose. Our first attempt was to use a decision tree, or
+% ensemble of decision trees, as this is a white-box model. A confounding
+% factor here is that the vast majority of variants (millions) are
+% unclassified, with just a few thousand classified variants. This is currently
+% an ongoing project in cooperation with the ICT department.
+\subsection{Variant classification}
+\begin{pframe}
+  From the tens of thousands of genetic variants, we are only interested in the
+  one that causes a disease.
+  \begin{itemize}
+    \item Filtering strategies narrow it down to a few dozens of variants.
+    \item Manual curation takes a few hours per patient.
+  \end{itemize}
+  \bigskip
+
+  Can this be done automatically?
+  \bigskip
+  \pause
+
+  Challenges:
+  \begin{itemize}
+    \item Make the selection explainable (decision trees).
+    \item Large number of unclassified variants.
+  \end{itemize}
+
+  \vfill
+  \permfoot{Sander Bollen}
+\end{pframe}
+
+% Use case 1: Classifying reads to the lab protocol they were sequenced with.
+% Within the department of clinical genetics, several lab protocols are used
+% for the generation of Next-Generation Sequencing. Two different capture kits
+% are used (WES and IDP), as well as two different sequencers (Hiseq4000 and
+% NextSeq500). Downstream analysis can be slightly different for each protocol
+% used, which requires run metadata to be correct. Unfortunately, this is not
+% always the case, and a way of validating the input metadata is required. We
+% have used machine learning to accomplish this task. Briefly, k-mers were
+% counted for each NGS read, after which an SVM was trained on the dataset of
+% several hundred thousand reads. Nextseq reads separate well from all other
+% classes, but both hiseq4000 classes were more difficult to separate. This
+% method may also be useful for other purposes where separating reads into
+% classes is required, e.g. separating virus from host reads.
+\subsection{Detecting lab protocol from raw data}
+\begin{pframe}
+  Differences in preprocessing (lab) steps:
+  \begin{itemize}
+    \item Capture kit (selection of part of the genome).
+    \item Different sequencers.
+  \end{itemize}
+  \bigskip
+
+  Validation of this metadata is needed because downstream analysis depends on
+  it.
+  \bigskip
+  \pause
+
+  Approach:
+  \begin{itemize}
+    \item $k$-mer counts of every read.
+    \item Training on over $100,\!000$ examples:
+    \begin{itemize}
+      \item SVM.
+      \item Random forest.
+    \end{itemize}
+  \end{itemize}
+
+  \vfill
+  \permfoot{Sander Bollen}
+\end{pframe}
+
+\begin{pframe}
+  Distinguishing sequencing platform works very well.
+
+  \begin{figure}[]
+    \begin{center}
+      \includegraphics[width=0.5\textwidth]{svm_error}
+      \hfill
+      \includegraphics[width=0.5\textwidth]{random_forest_error}
+    \end{center}
+    \caption{Overall error (left: SVM, right: Random forest).}
+  \end{figure}
+
+  Potential applications in contamination and \emph{infection} detection.
+
+  \vfill
+  \permfoot{Sander Bollen}
+\end{pframe}
+
+
+% Make the acknowledgements slide.
+\makeAcknowledgementsSlide{
+  \begin{tabular}{l}
+    Guy Allard\\
+    %Peter van 't Hof\\
+    Sander Bollen\\
+    Leon Mei\\
+  \end{tabular}
+}
+
+\end{document}

lectures/liacs_ai/logos

+../../submodules/presentation/logos
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lectures/liacs_ai/sanger_trace.jpg

+../../submodules/presentation-pics/pics/sanger_trace.jpg
\ No newline at end of file