Prof. Ioannis Tsamardinos on [CDAC 2019] Cancer Development and…

Cancer is a complex disease involving several intertwined phenomena
and events, which collude to unleash the tumor cells inherent
programs to proliferate, live, and move; thus, it is the malfunction of
the biomolecular machinery responsible for the “checks and balances”,
normally governed by various complex feedback loops among a population
of various cell types. Breakdown of this machinery leads to
uncontrolled growth of a cell population being selected by
evolutionary pressure that ultimately costs the very survival of the
host.

Understanding the many intricacies of all these interactions at the
subcellular, cellular and tissue levels has greatly benefitted from
the ever-improving applications of algorithmic, statistical and
mathematical modeling tools. Moreover, during the past 15 years, new
measurement technology for gene expression and, more recently, “deep”
genome sequence data, have produced vast amount of data, waiting to
be analyzed to deliver new interpretations. The design of novel “wet”
experiments and appropriately matched algorithmic, statistical and
mathematical modeling tools are expected to become the key to
successful oncological science and practice.

The Workshop and School on Cancer Development and Complexity seeks to
convene researchers from various related disciplines to explore
multiple facets of the challenges posed by cancer a “disease of the
systems.” The workshop will provide opportunities for the researchers
to exchange new ideas and viewpoints, forge new collaborations and
train the next generation of young scientists.

Participants are encouraged to present their work in two sessions and
poster presentations that will be held during the workshop.

The program will provide an introduction to both cancer biology and
mathematical and statistical methods used in analyzing the datasets
currently being produced by several laboratories around the world.
Next the program will provide an opportunity to interact with world
renowned cancer and bioinformatics researchers and the chance for
attendees to present their current work.  Finally, all the attendees
will receive a certificate of completion of the School.

*** Speakers (confirmed)

* Alessandra Carbone, Laboratory of Computational and Quantitative
 Biology, Sorbonne Universite`, Paris, France
* Davide Cittaro, Ospedale San Raffaele, USA
* Sui Huang, Institute for Systems Biology, Seattle, USA
* Enzo Medico, IRCC Candiolo, Turin, Italy
* Laxmi Parida, IBM T.J. Watson Laboratory, Yorktown Heights, NY, USA
* Tamar Schlick, Courant Institute of Mathematical Sciences and
 Dept. of Chemistry, New York University, New York, NY, USA
* Ioannis Tsamardinos, Computer Science Department, University of
 Crete, Greece

*** School Directors

* Marco Antoniotti, BIMIB, Dipartimento di Informatica, Sistemistica e
 Comunicazione, Università degli Studi di Milano Bicocca, Milan,
 Italy.
* Bud Mishra, Courant Institute of Mathematical Sciences, and Tandon
 School of Engineering, NYU, New York, NY, USA.

*** Local Organization

* Francesco Camporini (Fondazione Alessandro Volta)
* Alex Graudenzi (CNR)
* Davide Maspero (BIMIB e Istituto Nazionale dei Tumori)
* Giancarlo Mauri (BIMIB)

*** Steering Committee

* Marco Antoniotti, BIMIB, Dipartimento di Informatica,
 Sistemistica e Comunicazione, Università degli Studi di Milano
 Bicocca, Milan, Italy.
* Riccardo Bellazzi, Università degli Studi di Pavia, Pavia, Italy.
* Charles Cantor, Agena Biosciences, Sequenom, Retrotope and Boston
 University, USA.
* Alex Graudenzi, Innovation & Integration in Molecular Medicine
 Laboratory (INLAB), Institute of Molecular Bioimaging and Physiology
 of the Italian National Research Council (IBFM-CNR), Italy.
* Bud Mishra, Courant Institute of Mathematical Sciences, and Tandon
 School of Engineering, NYU, New York, NY, USA.
* Giulio Pavesi, Università degli Studi di Milano, Milan, Italy.
* Giovanni Porta, Università degli Studi dell’Insubria, Varese, Italy.

*** Institutions

* BIMIB, Dipartimento di Informatica, Sistemistica e Comunicazione,
 Università degli Studi di Milano Bicocca, Milan, Italy
 (http://bimib.disco.unimib.it)
* Fondazione “Alessandro Volta”, Como, Italy
 (http://fondazionealessandrovolta.it)
* Lake Como School of Advanced Studies, Como, Italy
 (http://lakecomoschool.org)

*** Venue

Villa del Grumello (http://www.villadelgrumello.it)

*** Registration

Registration fees include participation to the school, social events,
coffee breaks and school material.

Registration: 350.00 until March 1st, 2019; 400.00 EUR afterward and
on site. (*)
Daily, on site registration: 100 EUR (social dinner extra)

Registration site will open soon.

(*) Participants affiliated with the four Universities supporting the
Lake Como Schools (Milano Statale, Pavia, Milano Bicocca and Insubria)
qualify for a 320.00 EUR registration fee.

All fees include VAT.

*** Accommodations

A list of local hotels will be provided on the web site.

*** Web sites

http://cdac2019.lakecomoschool.org
http://bimib.disco.unimib.it

HUNT Calculator on Norwegian Broadcasting media

Source with the corresponding video

CAUSALPATH on the Eur13 digital magazine

Our project CAUSALPATH now features on the current issue of Eur13! Read about the objectives and the progresses of our activities in the online magazine:

Online magazine

Jump to page 17 if you are impatient to read about CAUSALPATH

Two new scientific works To be presented at The…

Prof Tsamardinos will present two new scientific works at The 2017 ACM SIGKDD Workshop on Causal Discovery, Halifax, Nova Scotia, Canada, Aug 14, 2017 – Aug 14, 2017:
Tsagris M., Borboudakis G., Lagani V. and Tsamardinos I. (2017). Constraint-based Causal Discovery with Mixed Data. 2017 ACM SIGKDD Workshop on Causal Discovery.
And
Tsirlis K., Lagani V., Triantafillou S., Tsamardinos I.(2017 ). On Scoring Maximal Ancestral Graphs with the Max-Min Hill Climbing Algorithm. 2017 ACM SIGKDD Workshop on Causal Discovery.

Prof. Tsamardinos’s invited talk at the 2nd international congress…

Prof. Tsamardinos’s invited talk at the 2nd international congress
“Evolution and Cancer
Prof. Tsamardinos gave an invited talk at the 2nd international congress “ Evolution and Cancer” in Thessaloniki on the June 30th.

Link:
https://www.youtube.com/watch?v=EVtMeZixRbg#t=5h14m12s

Dr. Tsagris invited talk at the 2017 Annual Meeting…

Dr. Tsagris gave an invited talk at the 2017 Annual Meeting of the Statistical Society of Canada (University of Manitoba, Winnipeg, June 11-14).
The title of the talk is “A Dirichlet Regression Model for Compositional Data with Zeros”

Abastract:
Compositional data are met in many different fields, such as economics, archaeometry, ecology, geology and political sciences. Regression where the dependent variable is a composition is usually carried out via a log-ratio transformation of the composition or via the Dirichlet distribution. However, when there are zero values in the data these two ways are not readily applicable. Suggestions for this problem exist, but most of them rely on substituting the zero values. In this paper we adjust the Dirichlet distribution when covariates are present, in order to allow for zero values to be present in the data, without modifying any values. To do so, we modify the log-likelihood of the Dirichlet distribution to account for zero values. Examples and simulation studies exhibit the performance of the zero adjusted Dirichlet regression.

New publication on Scientific Report!

Dr. Maria Markaki and Prof Tsamardinos participated in a new paper published on Scientific Report. In this work an advanced machine-learning pipeline, namely Just Add Data Bio (JADBio) is used for deriving MatureP, a classifier able to predict secreted proteins from information from their mature regions. Check it online:
link to paper

Prof. Tsamardinos invited talk at the 2017 ACM SIGKDD…

On the 14th of August 2017 Prof. Tsamardinos will give an invited talk at the 2017 ACM SIGKDD Workshop on Causal Discovery. Title: “Advances in Causal-Based Feature Selection”
The talk will illustrate the past and explore the future of feature selection algorithms based on Causal Discovery theory.

Link to the conference

Abstract:
Feature selection (a.k.a. variable selection) is a common task in data analytics, where the goal is to identify a minimal-size, optimally predictive feature subset. Theoretical results connect the solutions of the problem with the causal mechanism that generated the data, often represented by a Bayesian Network, a Maximal Ancestral Graph, or a Semi-Markov Causal Network. In such frameworks, the selected features are not only predictive of a target outcome of interest but also have a causal interpretation. Such results have given rise to a class of algorithms inspired by causal modeling of the data distribution. In the talk, we examine prototypical causally-inspired feature selection algorithms, advances that allow the algorithms to scale to high-dimensional problems, be applicable to a plethora of different types of data, identify multiple statistically-equivalent solutions, and scale to Big Data.