Integrated interactomes and pathways in precision medicine by Igor Jurisica, Toronto

Machine learning is the fastest growing field in computer science, and Health Informatics is amongst the greatest application challenges, providing benefits in improved medical diagnoses, disease analyses, and pharmaceutical development – towards future precision medicine.

Talk announcement: Friday, 12th May, 2017, 10:00, Seminaraum 137, Parterre, Inffeldgasse 16c

Integrated interactomes and pathways in precision medicine

by Igor Jurisica, University of Toronto and Princess Margaret Cancer Center Toronto

Abstract: Fathoming cancer and other complex disease development processes requires systematically integrating diverse types of information, including multiple high-throughput datasets and diverse annotations. This comprehensive and integrative analysis will lead to data-driven precision medicine, and in turn will help us to develop new hypotheses, and answer complex questions such as what factors cause disease; which patients are at high risk; will patients respond to a given treatment; how to rationally select a combination therapy to individual patient, etc.
Thousands of potentially important proteins remain poorly characterized. Computational biology methods, including machine learning, knowledge extraction, data mining and visualization, can help to fill this gap with accurate predictions, making disease modeling more comprehensive. Intertwining computational prediction and modeling with biological experiments will lead to more useful findings faster and more economically.

Short Bio: Igor Jurisica is Tier I Canada Research Chair in Integrative Cancer Informatics, Senior Scientist at Princess Margaret Cancer Centre, Professor at University of Toronto and Visiting Scientist at IBM CAS. He is also an Adjunct Professor at the School of Computing, Pathology and Molecular Medicine at Queen’s University, Computer Science at York University, scientist at the Institute of Neuroimmunology, Slovak Academy of Sciences and an Honorary Professor at Shanghai Jiao Tong University in China. Since 2015, he has also served as Chief Scientist at the Creative Destruction Lab, Rotman School of Management. Igor has published extensively on data mining, visualization and cancer informatics, including multiple papers in Science, Nature, Nature Medicine, Nature Methods, Journal of Clinical Oncology, and received over 9,960 citations since 2012. He has been included in Thomson Reuters 2016, 2015 & 2014 list of Highly Cited Researchers, and The World’s Most Influential Scientific Minds: 2015 & 2014 Reports.

Jurisica Lab, IBM Life Sciences Discovery Center:

Canada Tier I Research Chair:

On Nutrigenomics [1]:

[1] Nutrigenomics tries to define the causality or relationship between specific nutrients and specific nutrient regimes (diets) on human health. The underlying idea is in personalized nutrition based on the *omics background, which may help to foster personal dietrary recommendations. Ultimately, nutrigenomics will allow effective dietary-intervention strategies to recover normal homeostasis and to prevent diet-related diseases, see: Muller, M. & Kersten, S. 2003. Nutrigenomics: goals and strategies. Nature Reviews Genetics, 4, (4), 315-322.

What is machine learning?

Many services of our every day life rely meanwhile on machine learning. Machine learning is a very practical field and provides powerful technologies that allows machines (i.e. computers) to learn from prior data, to extract knowledge, to generalize and to make predictions – similar as we humans can do (see the MAKE intro). There is a very nice and highly recommendable info graphic available by the Royal Society [1]. This includes also an interactive quiz, which can be found here:

Royal Society Infographic “What is machine learning?”

This is part of a larger info campaign about machine learning from the Royal Society:

[1] The Royal Society was formed by a group of natural scientists influenced by Francis BACON (1561-1626).  The first ‘learned society’ meeting on 28 November 1660 followed a lecture at Gresham College by Christopher WREN. Joined by Robert BOYLE and John WILKINS and others, the group received royal approval by King Charles II (1630-1685) in 1663 and was known since as ‘The Royal Society of London for Improving Natural Knowledge’.

Today the Royal Society is a registered charity and the governing body of the Society is its Council and its members are elected by and from the Fellowship. Important to mention is that the Royal Society has an international character: “Science is an inherently international activity. The Society’s aim  is to reinforce the importance of science to build partnerships between nations, promote international relations and science’s role in culture and society”

Machine Learning Guide

The Machine Learing Guide by Tyler RENELLE (Tensor Flow, O-C-Devel) is highly recommendable to my students! This series aims to teach the high level fundamentals of machine learning with a focus on algorithms and some underlying mathematics, which is really great.




CD-MAKE machine learning and knowledge extraction

Cross Domain Conference for Machine Learning & Knowledge Extraction

Call for Papers – due to May, 15, 2017

Call for Papers due to May, 15, 2017

International IFIP Cross Domain Conference for Machine Learning & Knowledge Extraction CD-MAKE
in Reggio di Calabria (Italy) August 29 – September 1, 2017

CD stands for Cross-Domain and means the integration and appraisal of different fields and application domains (e.g. Health, Industry 4.0, etc.) to provide an atmosphere to foster different perspectives and opinions. The conference is dedicated to offer an international platform for novel ideas and a fresh look on the methodologies to put crazy ideas into Business for the benefit of the human. Serendipity is a desired effect, and shall cross-fertilize methodologies and transfer of algorithmic developments.

MAKE stands for MAchine Learning & Knowledge Extraction.

CD-MAKE is a joint effort of IFIP TC 5, IFIP WG 8.4, IFIP WG 8.9 and IFIP WG 12.9 and is held in conjunction with the International Conference on Availability, Reliability and Security (ARES).
Keynote Speakers are Neil D. LAWRENCE (Amazon) and Marta MILO (University of Sheffield).

IFIP is the International Federation for Information Processing and the leading multi-national, non-governmental, apolitical organization in Information & Communications Technologies and Computer Sciences, is recognized by the United Nations and was established in the year 1960 under the auspices of the UNESCO as an outcome of the first World Computer Congress held in Paris in 1959.

Papers are sought from the following seven topical areas (see image below). Papers which deal with fundamental questions and theoretical aspects in machine learning are very welcome.

❶ Data science (data fusion, preprocessing, data mapping, knowledge representation),
❷ Machine learning (both automatic ML and interactive ML with the human-in-the-loop),
❸ Graphs/network science (i.e. graph-based data mining),
❹ Topological data analysis (i.e. topology data mining),
❺ Time/entropy (i.e. entropy-based data mining),
❻ Data visualization (i.e. visual analytics), and last but not least
❼ Privacy, data protection, safety and security (i.e. privacy aware machine learning).

Proposals for Workshops, Special Sessions, Tutorials: April, 19, 2017
Submission Deadline: May, 15, 2017
Author Notification: June, 14, 2017
Camera Ready Deadline: July, 07, 2017


Machine Learning Podcast: Data Skeptic (recommendable)

Data Skeptic is a weekly podcast that is skeptical of and with data. They explain methods and algorithms that power our world in an accessible manner through short mini-episode discussions and longer interviews with experts in the field, see:


Call for Papers – Privacy Aware Machine Learning PAML due to April, 1, 2017

Privacy Aware Machine Learning (PAML)
for Health Data Science

Special Session on September, 1, 2017, organized by Andreas HOLZINGER, Peter KIESEBERG, Edgar WEIPPL and A Min TJOA in the context of the 12th International Conference on Availability, Reliability and Security (ARES and CD-ARES), Reggio di Calabria, Italy, August 29 – September, 2, 2017

Session Homepage

supported by the International Federation of Information Processing IFIP >  TC5 and WG 8.4 and WG 8.9

Keynote Talk by Neil D. LAWRENCE, University of Sheffield and Amazon

With the new European data protection and privacy regulations coming into effect with January, 1, 2018 issues having been nice to have so far are becoming a must have. Privacy aware machine learning will be one of the most important fields for the European research community and the IT business in particular. Most affected is the whole area of biology, medicine and health, partiuclarly driven by the fact that health sciences are becoming a more and more data intensive science.

This special session will bring together scientists with diverse background, interested in both the underlying theoretical principles as well as the application of such methods for practical use in the biomedical, life sciences and health care domain. The cross-domain integration and appraisal of different fields will provide an atmosphere to foster different perspectives and opinions; it will offer a platform for novel crazy ideas and a fresh look on the methodologies to put these ideas into business.

All paper will be peer-reviewed by three members of the international PAML-commitee. Paper acceptance rate of the last session was 35 %. Accepted papers will be published in a Springer Lecture Notes in Computer Science (LNCS) Volume and excellent contributions will be invited to be extented in a special issue of a journal (planned Springer MACH and/or BMC MIDM).

Research topics covered by this special session include but are not limited to the following topics:

– Production of Open Data Sets
– Synthetic data sets for learning algorithm testing
– Privacy preserving machine learning, data mining and knowledge discovery
– Data leak detection
– Data citation
– Differential privacy
– Anonymization and pseudonymization
– Securing expert-in-the-loop machine learning systems
– Evaluation and benchmarking

This picture was taken by our local host, Francesco Buccafurri on January, 3, 2017: from the conference venue you have a direct view to the Aetna volcano:

Picture taken by Francesco Buccafurri on January, 3, 2017

Picture taken by Francesco Buccafurri on January, 3, 2017

machine learning for health informatics

LNAI 9605 Machine Learning for Health Informatics available

14.12.2016 LNAI 9605 just appeared

Machine Learning for Health Informatics Lecture Notes in Artificial Intelligence LNAI 9605

Holzinger, Andreas (ed.) 2016. Machine Learning for Health Informatics: State-of-the-Art and Future Challenges. Cham: Springer International Publishing, doi:10.1007/978-3-319-50478-0

[book homepage]

Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization.

Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence.

This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.

NIPS 2016

NIPS 2016 is over

A crazy 5700-people event is over: NIPS 2016 in Barcelona. Registration on Sunday, 4th December, on Monday, 5th traditionally the tutorials were presented concluded by the first keynote talk given by Yann LeCun (now director at Facebook AI research) and completed by the official opening and the first poster presentation.  On Tuesday, Dec 6th, after starting with a keynote by Drew Purves (Google Deep Mind), parallel tracks on clustering and graphical models took place concluded by a keynote given by Saket Nevlakha (The Salk Institute) and completed by parallel tracks on deep learning and machine learning theory and poster sessions and demonstrations. Wednesday was openend by a keynote from Kyle Cranmer (New York University), the award talk “matrix completion has no spurious local min” and dominated by parallel tracks on algorithms and applications, concluded by a keynote by Marc Raibert (Boston Dynamics) who presented advances in latest robotic learning, and parallel tracks on deep learning and optimization, completed by the poster sessions with cool demonstrations. The Thursday was opened by a keynote fromm Irina Rish (IBM) and Susan Holmes (Stanford), followed by parallel tracks on interpretable models and cognitive neuroscience, concluded by various symposia until 21:30! Friday and Saturday were the whole day workshops – the sunday was reserverd for recreation on the sand beach of Barcelona 🙂

NIPS is definitely the most exciting conference with amazing variety on topics and themes revolving in machine learning with all sorts of theory and applications.


Machine Learning with Fun

Google Research hosts a number of very interesting so-called A.I. experiments. There you can play with machine learning algorithms in a very amusing way. A recent example is QUICK, DRAW *). This is an online guessing game that challenges humans to hand sketch (called doodles) a given object. The game uses a  neural network to learn from the input data

which is part of the A.I. Experiments platform:

and here the explanatory video:

Have fun and enjoy!

Here you see more than 100.000 hedgehog drawings made by humans on the internet:

*) not to be confused with QuickDraw [1], which is a sketch-based drawing tool facilitating to draw precise geometry diagrams,  and can automatically recognize sketched diagrams containing components such as line segments and circles, infer geometric constraints relating recognized components, and use this information to “beautify” the sketched diagram. This “Beautification” is based on an algorithm that iteratively computes various sub-components of the components using an extensible set of deductive rules.

[1] Cheema, S., Gulwani, S. & Laviola, J. QuickDraw: improving drawing experience for geometric diagrams. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2012. ACM, 1037-1064. doi: 10.1145/2207676.2208550


Obama on humans-in-the-loop

How artificial intelligence will affect jobs

In an discussion with Barack OBAMA [1] on how artificial intelligence will affect jobs, he emphasized how important human-in-the-loop machine learning will become in the future. Trust, transparency and explainabiltity will be THE driving factors of future AI solutions! The discussion interview was led by the Wired [2] Editor Scott DADICH, and MIT Media Lab [3] Director Joi ITO. I recommend my students to watch the full video. Barack Obama demonstrates a  good understanding of the field and indicates indirectly the importance of our research in the the human-in-the-loop approach [4], despite all progress towards fully automatic approaches and autonomous systems.

More information see:

[1] Barack Obama was the 44th President of the United States of America and was in office from January, 20, 2009 to January, 20, 2017. He was born August, 4, 1961 in Honolulu (Hawaii)

[2] Wired is a monthly tech magazine which reports since 1993 on how emerging technologies may affect culture, politics, economics. Very interesting to note is that Wired is known for coning the popular terms “long tail” and “crowdsourcing”.

[3] The MIT Media Lab is an interdisciplinary research lab at the Massachusetts Institute of Technology in Cambridge (MA), which is part of the Boston metropolitan area in the north, just across the Charles River – not far way from the Harvard Campus.

[4] Holzinger, A., Plass, M., Holzinger, K., Crisan, G.C., Pintea, C.-M. & Palade, V. 2017. A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop. arXiv:1708.01104



Portfolio Items