THÈSE DE DOCTORAT de l’Université de recherche Paris Sciences et Lettres PSL Research University Préparée à MINES ParisTech Personalized drug side effect prediction Prédiction personalisée des effets secondaires indésirables de médicaments École doctorale no432 SCIENCES DES METIERS DE L’INGENIEUR Spécialité BIO-INFORMATIQUE COMPOSITION DU JURY : M Bertram M´’ULLERr-MYHSOK MPI für Psychiatrie, Président Mme Florence d’ALCHÉ-BUC Tèlécom ParisTech, Rapporteur Soutenue par Víctor BELLÓN M Jean-Loup FAULON le 24 mai 2017 INRA, Rapporteur M Pierre NEUVIALl Dirigée par Véronique Stoven CNRS, Membre du jury Chloé-Agathe Azencott Mme Véronique STOVEN Mines ParisTech, Membre du jury Mme Chloé AZENCOTT Mines ParisTech, Membre du jury Acknowledgements De res a poc, i sempre amb vent de cara, quin llarg camí d’angoixa i de silencis. Miquel Marti i Pol Ara Mateix Però amb tot, malgrat tot, operem i avancem, pacífics,potser pusil.lànimes, però mai resignats i sempre tossuts, i obrim cada dia -importuns, enfadosos, burxons- clivelles de llum en aqueixa presó on, al cap i a la fi, respirem; Joan Oliver (Pere Quart) Versos elementals als catalans de 1969 I would like to thank to my two advisors, Veronique and Chloé, who have allowed me to have the freedom of driving this thesis towards answering the questions that I found interesting while guiding me and advising me. I would also like to thank the colleagues from my group for the scientific discussion and help. Thanks, Yunlong, Benoit, Peter, Beyrem, Marine, Nelle, Elsa, Nino, Jean-Louis, Svetlana, Alice, Xiwei, Olivier, Azadeh, Judith, Joseph, Hector, Thomas and Jean-Phillipe. I would like to thank the Marie Curie Initial Train- ing Network “Machine Learning for Personalized Medicine” for the funding, and all the people that were involved. The ITN have given me the opportunity to be part of an ambitious project, with highly talented people, and assists to i ii great talks and meetings during the last three years. I would like to thank to the people at the MPI für Psychiatrie in Munich, specially to the group of Professor Muller-Myshok for welcoming and helping me during my internship there. Also, thanks to the people in Roche, specially to Raul Rodriguez Este- ban who welcomed me to the group and allowed me to work in an interesting project for three months. I would like to thank to my friends in Paris: Pau, Andrea, Álvaro and Agata for sharing these years with me. I could not forget about the Castellers de Paris that have allowed me to do things I did not know I could. We have travelled and lived fantastic experiences together, and the most important, you have become my family here. I would like to specially thank Ester, who convinced me to try it for the first time. Thanks, also, to my friends back home. They have been a big support when I have needed one: Cris, Alex, Xavi, Didac, Alberto, Israel, Sergio and all the others I might forget, thanks. Finally, I would like to thank my family. To my mother, who spend many hours, when I was a kid, making up simple mathematical problems for me to solve. To my brother, to whom I ought too much, he faced many problems that he didn’t have to, and thanks to that I could continue studding for many years. For finishing, thanks to my uncles and cousins, who have always been an important support. List of Figures 1.1 RBF kernel value in a 1-dimensional space applied to x’=0 and −5 < x < 5 with different values for the scaling factor σ2. 18 1.2 Comparison of a Linear SVR and an SVR using an RBF kernel. Points in color are the selected Support Vectors by the SVR with RBF kernel. Noise is added to some of the points. Both functions show a bandwidth of size = 0:1.................... 21 1.3 Scheme of a neuronal network with three layers. The first layer corresponds to the input data and the last layer corresponds to the output layer. The middle layers of a neural network are called hidden layers. 25 1.4 Scheme of a perceptron unit.The perceptron receives the input from several variables and applies a non-linear function f that can be learned from data, and has a single output f(x1; x2; : : : ; xn). 25 1.5 Scheme of the multitask approach in [21]. The tasks share all the input and hidden layers of the network, and each one of them has its own output node. 27 2.1 Distribution of subpopulations in the Dream 8 Challenge on Toxico- genetics. The different subpopulations are: Han Chinese in Beijing China (CHB), Japanese in Tokyo, Japan (JPT), Luhya in Webuye, Kenya (LWK), Yoruban in Ibadan, Nigeria (YRI), Utah residents with European ancestry (CEU), British from England and Scotland (GBR), Tuscan in Italy (TSI), Mexican ancestry in Los Angeles California (MXL) and Colombian in Medellin, Colombia (CLM). 40 iii iv List of Figures 2.2 2D representation of o-phenanthroline. The non-annotated vertices correspond to carbon atoms and hydrogen atoms are not shown. 42 2.3 Tanimoto kernel matrix between all chemicals using ECFP with circular substructures of length up to 9. 47 2.4 Cross-validated CI for predicting the toxicity of a new untested cell line using different kernels. CI is calculated independently for every chemical and then the mean CI across all chemicals is reported. Cell lines kernels are displayed along the vertical axis and chemical kernels along the horizontal axis. 48 2.5 Cross-validated RMSE for predicting a new cell line toxicity using different kernels. Cell lines kernels are presented along the vertical axis and chemical kernels along the horizontal axis. 49 2.6 For the model with best RMSE, predictions of new cell lines toxicity values (vertical axis) as a function of the measured value (horizontal axis). The MinMax kernel was used for cell lines, and a MinMax kernel with substructures of length 9 for the chemicals . 50 2.7 Cross validated PC for predicting a new chemical compound toxic- ity using different kernels. Cell lines kernels are in the vertical axis and chemical kernels in the horizontal. 51 2.8 Cross validated PC for predicting a new chemical compound toxic- ity using different kernels. Cell lines kernels are in the vertical axis and chemical kernels in the horizontal. 51 2.9 Cross validated CI for predicting a new cell line and new chemicals toxicity using different kernels. Cell lines kernels are in the vertical axis and chemical kernels in the horizontal. 52 List of Figures v 2.10 Cross validated CI for predicting a new cell line and new chemicals toxicity using different kernels. Cell lines kernels are in the vertical axis and chemical kernels in the horizontal. 52 3.1 Performance of our methods on the leaderboard of the DREAM challenge. Only SNPs data were used to learn the models. The plot shows the correlation of the predictions of the model with re- spect to the real response level (vertical axis) as a function of the number of MI SNPs used (horizontal axis). Methods that build a single model for all treatments are labelled ’together’, and those corresponding to one model per treatment (performance averaged over the 6 treatments) are labelled ’treatment’. Methods including MI selected SNPs are labelled ’MI’ and those including biologi- cally selected SNPs are labelled ’Bio’. The models that do not include MI selected features have been plotted as an horizontal line to make comparisons easier. Those methods labelled with ’Mean’ correspond to predicting the mean response of the training data. 62 vi List of Figures 3.2 Performance of our methods on the leaderboard of the DREAM challenge. Clinical data and SNPs were both used to learn the models. The plot shows the correlation of the predictions of the model with respect to the real response level (vertical axis) as a function of the number of MI SNPs used (horizontal axis). Methods that build a single model for all treatments are labelled ’together’, and those corresponding to one model per treatment (performance averaged over the 6 treatments) are labelled ’treatment’. Methods including MI selected SNPs are labelled ’MI’ and those including biologically selected SNPs are labelled ’Bio’. The models that do not include MI selected features have been plotted as an horizontal line to make comparisons easier. 63 3.3 Performance of our methods on a 10-fold cross-validation over the training data. Only SNPs data were used to learn the models. The plot shows the correlation of the predictions of the model with respect to the real response level (vertical axis) as a function of the number of MI SNPs used (horizontal axis). Methods that build a single model for all treatments are labelled ’together’, and those corresponding to one model per treatment (performance averaged over the 6 treatments) are labelled ’treatment’. Methods including MI selected SNPs are labelled ’MI’ and those including biologically selected SNPs are labelled ’Bio’. Those models that do not include MI selected features have been plotted as an horizontal axis to make comparisons easier. 64 List of Figures vii 3.4 Results obtained by our methods on a 10-fold cross-validation over the training data. Clinical data and SNPs were both used to learn the models. The plot shows the correlation of the predictions of the model with respect to the real response level (vertical axis) as a function of the number of MI SNPs used (horizontal axis). Methods that build a single model for all treatments are labelled ’together’, and those corresponding to one model per treatment (performance averaged over the 6 treatments) are labelled ’treatment’.