Marinka Zitnik

Fusing bits and DNA

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Marinka Zitnik

Named a Rising Star in Electrical Engineering and Computer Science (EECS)

I am both honored and excited to be named a Rising Star in Electrical Engineering and Computer Science by MIT!


@Imperial College London, Department of Computing (Part II)

Recent days at Department of Computing, Imperial College London, were pleasant (though intense) and our efforts in data fusion produced some very good results.

Below are images taken at Imperial and nearby Chrome Web Lab, located in Science Museum. More about Google Chrome Web Lab experiment.


@Imperial College London, Department of Computing (Part I)

I have just arrived to London, United Kingdom, where I will stay until the end of November this year. I will be working at Imperial College London, Department of Computing, Computational Network Biology Research Group led by Prof. Dr.Natasa Przulj. For this great opportunity I need to thank to my supervisor Prof. Dr. Blaz Zupan, Head of Bioinformatics Laboratory at UofLj.

My work here will be about network integration for disease classification, specifically inferring prediction models from heterogenous data sources through matrix factorization. More about it in the next days. For now you can check the Interactive map of the Diseasome (Below is an image showing a part of diseasome. Interested reader is referred to Barabasi's paper The Human Disease Network.) linked from the NYTimes article Redefining Disease, Genes and All.

I am very much looking forward to it :)


To Embed or Not: Network Embedding as a Paradigm in Computational Biology

Current technology is producing high throughput biomedical data at an ever-growing rate. A common approach to interpreting such data is through network-based analyses. Since biological networks are notoriously complex and hard to decipher, a growing body of work applies graph embedding techniques to simplify, visualize, and facilitate the analysis of the resulting networks.

In this review, we survey traditional and new approaches for graph embedding and compare their application to fundamental problems in network biology with using the networks directly. We consider a broad variety of applications including protein network alignment, community detection, and protein function prediction. We find that in all of these domains both types of approaches are of value and their performance depends on the evaluation measures being used and the goal of the project. In particular, network embedding methods outshine direct methods according to some of those measures and are, thus, an essential tool in bioinformatics research.

This is joint work with colleagues from Stanford University, University of Toronto, Vector Institute, and Tel Aviv University.


Submit to ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds

Many scientific fields study data with an underlying graph or manifold structure such as protein networks, sensor networks, and biomedical knowledge graphs. The need for new optimization methods and neural network architectures that can accommodate these relational and non-Euclidean structures is becoming increasingly important.

We are organizing a workshop on Representation Learning on Graphs and Manifolds at the ICLR 2019. We encourage submissions to the workshop on topics related to graph and manifold representation learning.


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