[institut] Reminder - SCL Seminar: Vladimir Gligorijevic, Thursday, 8 March, 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 Thursday, 8 March 2018 at 
14:00 in the library reading room “Dr. Dragan Popović" of the Institute 
of Physics Belgrade. The talk entitled

Deep Multi-network Embedding for Protein Function Prediction

will be given by Vladimir Gligorijević (Flatiron Institute, New York, 
USA). Abstract of the talk:

The prevalence of high-throughput experimental methods has resulted in 
an abundance of large-scale molecular and functional interaction 
networks.  The connectivity of these networks provides a rich source of 
information for inferring functional annotations for genes and proteins. 
An important challenge has been to develop methods for combining these 
heterogeneous networks to extract useful protein feature representations 
for function prediction. Most of the existing approaches for network 
integration use shallow models that cannot capture complex and 
highly-nonlinear network structures. We introduce deepNF, our novel 
deep-learning based network integration method for protein function 
prediction. deepNF consists of two steps: 1) creating a low-dimensional 
dense vector representation of proteins (i.e., embedding) using 
Multimodal Deep Autoencoders and 2) training a classifier on the 
resulting representation to predict protein functions. 

We apply deepNF on 6 different networks obtained from the STRING db to 
construct a compact low-dimensional representation containing high-level 
protein features. We will present an extensive performance analysis 
comparing our method with the state-of-the-art network integration 
methods for protein function prediction. In addition to 
cross-validation, the analysis also includes a temporal holdout 
validation evaluation similar to the measures in Critical Assessment of 
Functional Annotation (CAFA). Our results show that our method 
outperforms previous methods for both human and yeast STRING networks. 
Our method offers a great advantage of being able to capture non-linear 
information conveyed by large-scale biological networks, leading to 
improved network representations. Features learned by our method lead to 
substantial improvements in protein function prediction accuracy, which 
could enable novel protein function discoveries.

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