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\citation{Ritchie14b}
\citation{Smedley16}
\citation{Schubach17}
\citation{Chawla2002}
\citation{Kuncheva14}
\citation{Schubach17}
\citation{Chawla2002}
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\citation{Smedley16}
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces \fontsize  {11}{13.2}\selectfont  (a): Distribution across $100$ different choices of {\em  hyperSMURF}parameters $(n,f,m)$ of the AUPRC values obtained by cross-validation on the training data; (b) Comparison of precision-recall curves obtained by {\em  hyperSMURF}~with default parameters (red curve) and with the selected ``best'' parameters (cyan curve).}}{2}{figure.1}}
\newlabel{fig}{{1}{2}{\fontsize {11}{13.2}\selectfont (a): Distribution across $100$ different choices of \Hy parameters $(n,f,m)$ of the AUPRC values obtained by cross-validation on the training data; (b) Comparison of precision-recall curves obtained by \Hy ~with default parameters (red curve) and with the selected ``best'' parameters (cyan curve)}{figure.1}{}}
\citation{Saito15}
\citation{Kircher2014}
\citation{Zhou2015}
\citation{Ionita-Laza:2016}
\citation{Schubach17}
\citation{Zhou2015}
\bibcite{Chawla2002}{1}
\bibcite{Ionita-Laza:2016}{2}
\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces \fontsize  {11}{13.2}\selectfont  Comparison of {\em  hyperSMURF}~results obtained respectively with default parameters ($n=100, f=2, m=3$) and with the best parameters obtained by internal cross-validation on the training data ($n=300, f=1, m=10$).}}{3}{table.1}}
\newlabel{table}{{1}{3}{\fontsize {11}{13.2}\selectfont Comparison of \Hy ~results obtained respectively with default parameters ($n=100, f=2, m=3$) and with the best parameters obtained by internal cross-validation on the training data ($n=300, f=1, m=10$)}{table.1}{}}
\bibcite{Kircher2014}{3}
\bibcite{Kuncheva14}{4}
\bibcite{Ritchie14b}{5}
\bibcite{Saito15}{6}
\bibcite{Schubach17}{7}
\bibcite{Smedley16}{8}
\bibcite{Zhou2015}{9}
