Marinka Zitnik

Fusing bits and DNA

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Tutorial on Representation Learning for Network Biology

I am excited to announce that our tutorial on Representation learning for network biology is accepted at ISMB 2018. I will present the tutorial at ISMB 2018 conference in Chicago, IL. Stay tuned for more information and tutorial materials.

Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. This tutorial investigates key advancements in representation learning for networks over the last few years, with an emphasis on fundamentally new opportunities in network biology enabled by these advancements.

Tutorial website: http://snap.stanford.edu/deepnetbio-ismb.

 

Graph Convolutional Networks for Computational Pharmacology

Our paper on graph convolutional networks for modeling polypharmacy side effects has been accepted to ISMB conference. Stay tuned for the final version published in Bioinformatics journal.

We describe a general graph convolutional neural network approach for multirelational link prediction in heterogeneous graphs. In computational pharmacology, this approach creates, for the first time, an opportunity to use large molecular, pharmacological, and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal studies.

Project website: http://snap.stanford.edu/decagon.

 

JMM 2018: Invited Talk on Prioritization of Network Communities

I am giving a talk on prioritization of network communities, a framework that enables speeding-up scientific discovery process in experimental network sciences.

It is very exciting to be able to present this challenging and important problem at the Joint Mathematics Meetings conference, in the session on Theory, Practice, and Applications of Graph Clustering.

 

PSB 2018: Disease Pathways in the Human Interactome

I am giving a talk on large-scale analysis of disease pathways in the human interactome at PSB.

Check out my slides, poster and the paper if interested or want to learn more about disease pathway prediction, learning using biological data, and network biology.

 

Scalable Matrix Tri-Factorization

In our new paper on accelerating matrix tri-factorization we show how to learn factorized representations that scale well on multi-processor and multi-GPU architectures.

The new approach speeds up computations by more than two orders of magnitude without any loss in accuracy and is especially suitable for large-scale biomedical data analytics.

 

ECML PKDD Proceedings Online

The third volume of ECML PKDD 2017 proceedings is online, describing state-of-the-art machine learning and data mining systems presented at European conference on machine learning.

I had a great experience co-chairing the demo track.

 

Guest Lecture on Biological Network Analysis

I am giving a guest lecture on biological network analysis in the CS224W Network Analysis course at Stanford.

The lecture introduces biological networks and their analysis to the CS and engineering students. It describes statistical enrichment tests and several important prediction problems in biology, such as disease pathway detection and gene function prediction. It also explains some of the most successful methods for solving these problems.

Slides and class notes.

 

Nature Communications: Mapping Biological Functions of NUDIX Enzymes

Our new study published in Nature Communications explores the NUDIX hydrolases in human cells and provides attractive opportunities for expanding the use of this enzyme family as biomarkers and potential novel drug targets. The NUDIX enzymes are involved in several cellular processes, yet their biological role has remained largely unclear.

In a collaborative study with Karolinska Institutet, Helleday Laboratory, Science for Life Laboratory (SciLifeLab)Uppsala University, Stockholm University, and the Human Protein Atlas we have generated comprehensive data on the individual structural, biochemical and biological properties of 18 human NUDIX proteins, as well as how they relate to and interact with each other.

I am especially happy to see how my machine learning and computational biology methods can help discover new biology! We used my recent methods for data fusion and gene network inference to generate predictions, which we then validated in the wet laboratory. Using these novel algorithms, we integrated all data and created a comprehensive NUDIX enzyme profile map. This map reveals novel insights into substrate selectivity and biological functions of NUDIX hydrolases and poses a platform for expanding the use of NUDIX as biomarkers and potential novel cancer drug targets.

Karolinska Institutet NewsScience for Life Laboratory (SciLifeLab) News, and by Phys.org News wrote about this project.

 

PSB 2018: Large-Scale Analysis of Disease Pathways in the Human Interactome

Our paper on large-scale analysis of disease pathways in the human interactome will appear at Pacific Symposium on Biocomputing.

Discovering disease pathways, which can be defined as sets of proteins associated with a given disease, is an important problem that has the potential to provide clinically actionable insights for disease diagnosis, prognosis, and treatment. Computational methods aid the discovery by relying on protein-protein interaction (PPI) networks. They start with a few known disease-associated proteins and aim to find the rest of the pathway by exploring the PPI network around the known disease proteins.

However, the success of such methods has been limited, and failure cases have not been well understood. In the paper we study the PPI network structure of disease pathways. We find that pathways do not correspond to single well-connected components in the PPI network. These results counter one of the most frequently used assumptions in network medicine, which posits that disease pathways are likely to correspond to highly interconnected groups of proteins. Instead, we show that proteins associated with a single disease tend to form many separate connected components/regions in the network.

Furthermore, we show that state-of-the-art disease pathway discovery methods perform especially poorly on diseases with disconnected pathways. These results suggest that integration of disconnected regions of disease proteins into a broader disease pathway will be crucial for a holistic understanding of disease mechanisms.

In addition to new insights into the PPI network connectivity of disease proteins, our analysis leads to important implications for future disease protein discovery that can be summarized as:

  • We move away from modeling disease pathways as highly interlinked regions in the PPI network to modeling them as loosely interlinked and multi-regional objects with two or more regions distributed throughout the PPI network.
  • Higher-order connectivity structure provides a promising direction for disease pathway discovery.

Project website: http://snap.stanford.edu/pathways.

 

ISMB/ECCB 2017: Feature Learning in Multi-layer Tissue Networks

I am giving a talk on feature learning in multi-layer tissue networks and tissue-specific protein function prediction at ISMB/ECCB.

Check out the slides, the poster and the recorded talk.

 

Understanding Protein Functions in Different Biological Contexts

Our paper on predicting multicellular function through multi-layer tissue networks is published in Bioinformatics and is included in the proceedings of ISMB/ECCB 2017, a premier conference in bioinformatics and computational biology.

Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet surprisingly little is known about protein functions in different biological contexts, and prediction of tissue-specific function remains a critical challenge in biomedicine.

Our approach OhmNet represents a network-based platform that shifts protein function prediction from flat networks to multiscale models able to predict a range of phenotypes spanning cellular systems.

OhmNet predicts tissue-specific protein functions by representing tissue organization with a rich multiscale tissue hierarchy and by modeling proteins through neural embedding-based representation of a multi-layer network. For the first time, we can systematically pinpoint tissue-specific functions of proteins across more than 100 human tissues. OhmNet accurately predicts protein functions, and also generates actionable hypotheses about protein actions specific to a given biological context.

Project website: http://snap.stanford.edu/ohmnet.

 
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