PIDSIA Seminar by Marco Antoniotti (18th of November)
by Announcements of talks＠IDSIA
*Title:
Reconstructing Cancer Progression Models
*Abstract:
Cancer is a disease characterized by the accumulation of alterations to the genome, which selectively make cancer cells fitter to survive, proliferate and move. The understanding of progression and evolution models that underlie this processes, i.e., the characterization of sequences of alterations that lead to the emergence of the disease, is a topic attracting much attention. Of course, the problem of reconstructing such models is not new; in fact, several methods for inferring progression models (and phylogenies) from cross-sectional samples have been developed since the late 90s.
Recently, we have proposed a number of algorithms to reconstruct cancer clonal evolutionary models from a variety of cross-sectional data types, either "ensemble", "bulk" or "single cell". We perform our reconstructions using a variety of algorithms based on a “probability raising” score that guarantees statistical dependencies on the inferred precedence relations. Our methods are complementary to traditional phylogeny reconstructions ones.
Within this context, we have proven the correctness of our algorithms and characterized their performance. Our algorithms are collected in a R BioConductor package “TRanslational ONCOlogy” (TRONCO) that we have successfully used as part of our "Pipeline for Cancer Inference" (PiCnIc) to analyse Colorectal Cancer (CRC) data from TCGA. The newest addendum to TRONCO is the “Temporal oRder of Individual Tumors” (TRaIT) a new collection of algorithms that can be used for single-cell (and multi-region) progression analisys of cancers.
Web Sites: \* http://bimib.disco.unimib.it/ \* http://troncopackage.org
*Speakers:
Marco Antoniotti is an Associate Professor of Computer Science in the Department of Informatics, Systems and Communication of the Università Milano-Bicocca, Milan, Italy. Before that he was Senior Research Scientist of the NYU Courant Bioinformatics Group in New York and a Research Scientist at PARADES EEIG in Rome, Italy. From 1996 to 1997 post Doctoral fellow at University of California at Berkeley PATH Institute. He received his Ph.D in Computer Science from Courant Institute of Mathematical Sciences of New York University.
His main research topics are Bioinformatics and Computational (Systems) Biology, Simulation, Verification and Language Design Issues. More recently he has - almost - become a Cancer Researcher, and, with his group, he is studying Cancer Progressions, Cancer Evolution and Personalized Therapies; all by analyzing different data types and data sources, including Single-cells.
Marco Antoniotti is the author of several journal and conference papers and of several software projects; he has received support for his research from NSF, the Regione Lombardia (Italy), the Elixir european network, the European Commission under the H2020 program and the MCSA and COST actions and CRUK/AIRC/FA-AECC under the Accelerator Award scheme.
*Date:
Monday, 18th of November 2019, 12:00-13:00
*Location:
Manno, Galleria 1, 2nd floor, room G1-201
*Doodle registration:
Pizza (or alternative food) and drinks will be offered at the end of the talk. If you plan to attend the lunch, please register in a timely fashion (meaning at the latest at 9AM of the same day of the talk) at the following link so that we will have no shortage of food.
https://doodle.com/poll/g3bbf2pthrxq7nxp
If by any chance, after registering for a Pizza at the link above, you know you will not be able to attend the lunch, please cancel your registration asap.
5 years, 1 month
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PIDSIA Seminar by U. Michelucci and F. Venturini (14th of November)
by Announcements of talks＠IDSIA
*Title:
Neural Networks in Optical Sensors - a revolution in how sensors are built and used
*Abstract:
In this talk, we will discuss two different types of optical sensors based on luminescence: one is used for oxygen sensing type sensors, and the other is an «optical nose» for fingerprinting of substances.
The problem of these types of sensors is that the measured signal is influenced by many components (like mirrors, lasers, electronics). Unfortunately these dependencies cannot be modeled mathematically in a simple way. So typically, complicated and empirical mathematical models are used, which are then fine tuned for each sensor in what is called calibration. But do we need to model those effects? Or there is another way?
We will describe how the use neural networks can dramatically change how to build and use these sensors, without the need for any complicated mathematical model. Introducing neural networks in optical sensors typically does not require deep networks. However, there are several aspects that are very different from classical neural networks models which will be discussed here: one example overall is overfitting, that in this case requires completely different approaches to be dealt with.
We will bring at the talk a prototype of a portable, low-cost sensor that we are currently developing at TOELT to demonstrate how a low-cost sensor can be built and used.
*Speakers:
- Umberto Michelucci studied physics and mathematics and received its master from the University of Florence (Italy). He gained research experience at the George Washington University (USA) and the University of Augsburg (DE). He is an expert in numerical simulation, statistics, data science and machine learning. His is author of the books “Applied Deep Learning – A Case-Based Approach to Understanding Deep Neural Networks” and “Advanced Applied Deep Learning - Convolutional Neural Networks and Object Detection”. He is a lecturer at the University of Applied Sciences (Switzerland) on Deep Learning and Neural Networks Theory and Applications. He is a co-founder of TOELT LLC, a company aiming to promote research and education in AI and is a “Google Developer Expert” in Machine Learning. He is also a Google Developer Expert and gives lectures and training internationally together with Google on Machine Learning.
- Francesca Venturini received her Ph.D. in physics from the Technical University of Munich (Germany) in 2003. From 2003 to 2013 she gained practical experience in industrial research and development. In 2013 she joined the Institute of Applied Mathematics and Physics of the Zurich University of Applied Sciences (Switzerland), where she is a full professor of applied optics. Additional to teaching, she works in applied research in close contact with industrial partners. Her research interests are the field of optical sensing, with focus on luminescence spectroscopy and tunable-diode laser absorption spectroscopy. She is a co-founder of TOELT LLC, a company aiming to promote research and education in AI.
*Date:
Thursday, 14th of November 2019, 12:00-13:00
*Location:
Manno, Galleria 1, 2nd floor, room G1-201
*Doodle registration:
Pizza (or alternative food) and drinks will be offered at the end of the talk. If you plan to attend the lunch, please register in a timely fashion (meaning at the latest at 9AM of the same day of the talk) at the following link so that we will have no shortage of food.
https://doodle.com/poll/v48ze5eqaqr5ndqt <https://doodle.com/poll/v48ze5eqaqr5ndqt>
If by any chance, after registering for a Pizza at the link above, you know you will not be able to attend the lunch, please cancel your registration asap.
5 years, 1 month
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