NOVEMBER 21-22, 2019, BASEL
The first and only conference dedicated to Python in the pharma data science world.Learn More
“ Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. ” — Atul Butte
PyPharma will be a meeting, exchange and learning point for industry and academic scientific python users in pharma. At PyPharma, we will learn about the most interesting current challenges in the field and the new and exciting python tools and packages that can be used to tackle them.
PyPharma will be aimed at pharmaceutical python users of all levels working in data science, and it will welcome workshops, talks and posters covering all aspects of the pharmaceutical lifecycle and ecosystem where python tools are applied as well as data modalities.
PyPharma is fully run by volunteers in the pharmaceutical industry and academia and hosted by Roche and the University of Basel. Attendance will be based on invites, and it will be a single track, 2-day conference. Attendance will be purposely kept small to maximize interaction between the audience and the speakers.
|Time||Biozentrum Seminarraum 104||Kollegienhaus Mehrzweckraum||Missionsstrasse 64a Computerraum||Missionsstrasse 64a Seminarraum|
|09:00-10:30||Deep Learning for Natural Language Processing in Pharma and Biomedical Applications||Genomic Data Analysis with Pandas||Building pharmaceutical web applications with Dash|
|11:00-12:00||Deep Learning for Natural Language Processing in Pharma and Biomedical Applications||Genomic Data Analysis with Pandas||Building pharmaceutical web applications with Dash|
|13:00-14:30||Interpretability in Machine Learning for Computational Biology||Hands-on Bayesian: what, why, how?||Cheminformatics workshop from QSAR to DNN||Snakemake for reproducible analyses|
|15:00-17:00||Interpretability in Machine Learning for Computational Biology||Hands-on Bayesian: what, why, how?||Cheminformatics workshop from QSAR to DNN||Snakemake for reproducible analyses|
Day 1 will consist of workshops and will take place at the University of Basel. The room locations are:
Day 2 will consist of keynotes and talks and will take place at the Roche Viaduktstrasse amphitheater (close to the Basel SBB station). More details to follow.
María Rodríguez Martínez did her undergraduate studies in Physical Sciences at Universidad Complutense de Madrid. She then did her PhD in Theoretical Cosmology, at the Institut d’Astrophysique de Paris. Her PhD research focused on developing cosmological models of the early evolution of the universe with additional spatial dimensions. After completing her PhD, she moved to the Hebrew University in Jerusalem, where she focused on setting astrophysical bounds to theories that break Lorentz symmetry using the high-energy emissions from Gamma-Ray Bursts, extremely powerful explosions of gamma rays coming from outside our galaxy. In 2007, she transitioned into the field of Systems Biology as a postdoc at the Weizmann Institute of Science in Rehovot (Israel). Her research was devoted to the development of quantitative descriptions of biological networks and the complex interactions within. In 2009, she moved to Columbia University where she developed quantitative models to understand cancer gene dysregulation. María joined IBM as a Research Staff Member in November 2013. Her research at IBM focuses on integrating different high-throughput molecular datasets in order to build comprehensive molecular models of disease that can help clinicians to provide better diagnoses and suggest personalized therapies.
After obtaining a degree in bioinformatics from the computer science faculty at the University of Tübingen in 2009, Kai switched to Institute for Microbiology and Infection Medicine at the same university to obtain his PhD. During his PhD project, he co-developed the antiSMASH genome mining tool, initially released in 2010. Building on his software engineering knowledge obtained working on Open Source software projects since his undergrad days, Kai has focused on developing translational bioinformatics tools and databases. During his first post-doc at the Max Planck Institute for Biology of Ageing in Cologne, Germany, he worked on the doRiNA database of RNA interactions in post-transcriptional regulation. Returning to natural products research, Kai joined the Novo Nordisk Foundation Center for Biosustainability (NNFCFB) at the Technical University of Denmark, in Lyngby, Denmark. In addition to returning to antiSMASH development, he has driven the development of the antiSMASH database, the CRISPR/Cas sgRNA design tool CRISPy-web, and a number of smaller software projects. Kai is currently working as a Researcher at the NNFCFB, where he heads the bioinformatics group of the New Bioactive Compounds section ran by Sang Yup Lee and Tilmann Weber.
Michał Januszewski is a Staff Software Engineer at Google Research in Zürich, where he currently works on automated methods for high-throughput synaptic-resolution brain mapping. Prior to Google, Michał did research in the field of Computational Fluid Dynamics. He holds a PhD in Physics from University of Silesia in Katowice, Poland.
After getting a Ph.D. in Theoretical Chemistry in the group of Roald Hoffmann at Cornell University and doing a post-doctoral fellowship in Aachen Germany Greg moved to California where he worked at a couple of startups and started applying machine learning to life-sciences problems. In 2006 he moved to Basel to work at the Novartis Insitutes for BioMedical Research. Greg spent about five years in the computer aided drug design group and then shifted to research IT to head the group responsible for chemistry software and data systems. Eventually he was also responsible for an internal initiative to integrate internal and external biological and chemical data and make it available to researchers. He now splits his time between KNIME and T5 Informatics, a consulting company providing support and services related to the open-source cheminformatics toolkit RDKit. Greg has been using Python together with C++ to solve scientific problems for 20 years now.
|Deep Learning for Natural Language Processing in Pharma and Biomedical Applications||Diego Saldana||Roche|
|Genomic Data Analysis with Pandas||Maryan Zaheri and Carsten Magnus||University of Zurich (UZH)|
|Bayesian Inference with Python||Elizaveta Semenova||AstraZeneca|
|How to Upscale Hyper-parameter tuning with Scikit learn using DASK on the cluster||Marius Rene Garmhausen||Roche|
|Tutorial on interpretability in machine learning for Computational Biology||An-Phi Nguyen||IBM|
|Cheminformatics workshop from QSAR to DNN using RDKit||David Marcus||GlaxoSmithKline|
|Snakemake for Reproducible Analyses||Romain Feron and Amina Echchiki||University of Lausanne (UNIL)|
|Building pharmaceutical web applications with Dash||Rafal Chojnacki||Roche|
Diego Saldana is a Data Scientist at Roche Personalized Healthcare (PHC). He has developed models to perform various tasks and analyze diverse data sources. Currently his main applications of interest are in oncology and clinico-genomics.
Otto Fajardo works at Roche in the Biometrics department handling Real World and Clinical Data. He uses python in conjunction with relational databases to perform his job. He authors packages for data handling, most of them internal to Roche but also open source (pyreadstat, pyreadr). He has a background in Neuroscience (PhD) and Dentistry and has been using Python everyday for the last 10 years.
Carsten Magnus is a theoretical and computational biologist at the Institute of Medical Virology, University of Zurich. In his research he develops models and methods (mainly implemented in R and python) to tackle important questions on HIV and Influenza evolution. Check out his webpage webpage for further information.
Maryam Zaheri is a scientific assistant and computational biologist at the Institute of Medical virology, University of Zurich. She develops models and methods mainly in the field of metagenomics, drug resistant mutation detection and research related to HIV virus.
Geoffrey is a computational biologist/bioinformatician with the SIB Swiss Institute of Bioinformatics and the University of Basel. He generally works with biologists engaged in scientific computing at the high-performance computing center (sciCORE), and with the broader research community in data science applications and training.
Elizaveta Semenova is a Post-doctoral Researcher at AstraZeneca working in Bayesian Machine Learning. She has a PhD in Epidemiology/Biostatistics with experience in spatio-temporal modelling, data analysis, theoretical and applied mathematics. Elizaveta is interested in Bayesian statistics, machine learning and probabilistic modelling. She is also a technological innovation and technical education enthusiast.
Matteo is a Research Staff Member in Cognitive Health Care and Life Sciences at IBM Research Zürich. He's currently working on the development of multimodal deep learning models for drug discovery using chemical features and omic data. He also researches in multimodal learning techniques for the analysis of pediatric cancers in a H2020 EU project, iPC, with the aim of creating treatment models for patients. He received his degree in Mathematical Engineering from Politecnico di Milano in 2013. After getting his MSc he worked in a startup, Moxoff spa, as a software engineer and analyst for scientific computing. In 2019 he obtained his doctoral degree at the end of a joint PhD program between IBM Research and the Institute of Molecular Systems Biology, ETH Zürich, with a thesis on multimodal learning approaches for precision medicine.
An-phi is a doctoral student in the Cognitive Health Care and Life Sciences group (IBM Research Zurich) and in the Seminar for Statistics of ETH Zurich. His current research mainly focuses on interpretability for machine learning models with applications to computational biology. From time to time he also wonders about other topics in Machine Learning, Statistics, NLP and Computer Vision. He received his BSc degree in Mathematical Engineering from Politecnico di Milano and his MSc degree in Computational Science and Engineering from ETH Zurich. Before joining IBM Research, he worked in two startups as algorithm/software engineer (Insightness AG, TrueAI Ltd.).
I am a cheminformaticitan working at GlaxoSmithKline on various machine learning projects at data and computational sciences department, GSK R&D Stevenage site, United Kingdom. My background is Pharmacy, Medicinal Chemistry and a PhD in Computational Chemistry at the Hebrew University of Jerusalem, followed up by two postdocs, at the University of Cambridge and at EMBL-EBI. My main research interests are small-molecule libraries selection and project design for hit and lead generation in which I devleop tools for compound generation and Active Learning. I am also co-leading the small-molecule predictive modelling centre of excellence within GSK overlooking models progression and development for small-molecule predictions, from QSAR to DNN models, including commercial platfrom as well as in-house proprietry data driven tools.
Simon Dirmeier is a doctoral student in the Computational Biology Group at D-BSSE, ETH Zurich. His scientific interests revolve mainly around graphical and probabilistic modelling of genetic perturbation screens. Furthermore, he works on scalable methods for analysing large-scale imaging data.
Damian is a Senior Researcher at the Machine Learning and Computational Biology Lab at ETH Zurich. He develops and implements machine learning models to extract knowledge from patients’ clinical and omics data. His research interests lie in the analysis of complex networks, with a current focus on using electrophysiological recordings to model the connectivity of neuronal cultures.
This site is under construction.
This site is under construction.
This site is under construction.
Roche/Genentech is offering to cover travel and accommodation expenses for two conference attendees to support the diversity in the open source Python healthcare community. The eligible candidates have to be currently enrolled as students, or work for a non-profit organization.
Update: Two candidates have been selected and applicants have now been informed of the panel's decision.
Get in touch if you have questions or would like to learn more about PyPharma.
We have an active organizing community in slack with members of many companies and institutions in the pharmaceutical industry and academia. Contact us if you're interested in joining our community.
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