[institut] SCL Seminar: Vladimir Gligorijevic, Friday, 8 September, 14:00

[Marija Mitrovic Dankulov]

Dear colleagues,

You are cordially invited to the SCL seminar of the Center for the Study 
of Complex Systems, which will be held on Friday, 8 September 2017 at 
14:00 in the library reading room “Dr. Dragan Popović" of the Institute 
of Physics Belgrade. The talk entitled

Non-negative Matrix Factorization Methods for Analysis and Integration 
of Heterogeneous Network Data

will be given by Dr. Vladimir Gligorijević (Flatiron Institute, Njujork, 
SAD). Abstract of the talk:

Abstract

In many areas of science and technology data describing a process or a 
system of interest and its individual components can be obtained from 
different types of instruments, using different acquisition techniques 
and experimental setups. For example, in biology and biomedicine, rapid 
technological advances have led to the production of such heterogeneous 
and multimodal data, and enabled construction of complex networks with 
various types of interactions between diverse biological entities. 
Conventional network data analysis methods were shown to be limited in 
dealing with such multi-relational network data, as they are designed 
specifically for analysis of single-relational networks [1].

In this talk, I will introduce integrative methods that can collectively 
mine multiple types of biological networks and create more accurate 
integrative models capable of producing more holistic, systems-level 
biological insights. The proposed methods are based on Non-negative 
Matrix Factorization (NMF), a dimensionality reduction technique, that 
is further extended for learning and modelling dependencies between 
large-scale heterogeneous and multimodal network data.

In the first part of the talk, I will demonstrate the applicability of 
our methods on diverse biological problems including protein function 
prediction [2], alignment of multiple PPI networks from different 
species with the goal of discovering clusters of proteins that are 
evolutionarily and functionally conserved across all networks [3] and 
extraction of composite functional modules from multimodal biological 
networks [5]. In the second part of the talk, I will focus, in more 
details, on the challenges in personalized medicine. Here I will explain 
in detail our method for addressing the key challenges in cancer 
research: stratification of patients into groups with different clinical 
outcomes, cancer progression, prediction of driver genes whose mutations 
trigger the onset and development of cancers, and re-purposing of drugs 
treating particular cancer patient groups. I will outline some of the 
results obtained by applying our framework on ovarian cancer data [4].


[1] V. Gligorijević, N. Pržulj, Journal of the Royal Society 
Interface 12.112 (2015): 20150571.
[2] V. Gligorijević, V. Janjić, N. Pržulj, Bioinformatics 30.17 (2014): 
i594-i600.
[3] V. Gligorijević, N. Malod-Dognin, N. Pržulj, Bioinformatics 32.8 
(2015): 1195-1203.
[4] V. Gligorijević, N. Malod-Dognin, N. Pržulj, Proceedings of the 2016 
PSB
[5] V. Gligorijević, Y. Panagakis, S. Zafeiriou, arXiv preprint 
arXiv:1612.00750 (2016).

Best regards,
Marija Mitrovic Dankulov

-- 
Marija Mitrovic Dankulov
E-mail: mitrovic at ipb.ac.rs

Phone: +381 11 3713068
Fax: +381 11 3162190

Scientific Computing Laboratory
Institute of Physics Belgrade
Pregrevica 118, 11080 Belgrade, Serbia