Marinka Zitnik

Fusing bits and DNA

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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.

More in Karolinska Institutet News and in Science for Life Laboratory (SciLifeLab) News.

 

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'm 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.

   

Invited Talk on Boosting Biomedical Discovery Through Network Data Analytics

I'm giving an invited talk on speeding-up scientific discovery in biomedicine through computational network analytics at the International Conference for Big Data and AI in Medicine.

 

Jozef Stefan Golden Emblem Prize

I am honored to receive Jozef Stefan Golden Emblem for winning PhD dissertation in the fields of natural sciences, medicine and biotechnology. The prize is awarded by Jozef Stefan Institute.

I look forward to making further progress on machine learning, data mining, and statistical methods research to better understand complex biomedical data systems!

For my Slovenian friends, I wrote a short non-technical column for Jozef Stefan Institute News (in Slovene) on the topic of this work.

 

Submit to AIME 2017 Workshop on Advanced Healthcare Analytics

You are cordially invited to submit a paper to the Workshop on Advanced Predictive Models in Healthcare that will take place during the AIME 2017 conference. This workshop will focus on topics related to advanced predictive models, capable of providing actionable and timely insights about health outcomes.

 

Submit to ECML PKDD 2017

You are cordially invited to submit a paper to the upcoming 2017 ECML PKDD conference.

ECML PKDD is the European Conference on Machine Learning and Knowledge Discovery. It is the largest European conference in these areas that has developed from the European Conference on Machine Learning (ECML) and the European Symposium on Principles of Knowledge Discovery and Data Mining (PKDD).

You are especially invited to consider submitting a paper to the ECML PKDD Demo Track which I am co-chairing this year.

 

ACM XRDS: The Infinite Mixtures of Food Products

The Fall issue of ACM XRDS is here! In this issue of XRDS, we take a closer look at the marriage of physics and computer science through quantum computing. Quantum computing is a model of computation that breaks with the tradition of digital computers surround us. The issue covers recent advances in the field of quantum computing, such as computer simulation, complexity theory, simulated annealing and machine learning, as well as an in-depth profile of David Deutsch who pioneered the field of quantum computation.

My department contributed a column on the infinite mixture models applied to the problem of clustering food products. Infinite mixture models are useful because they do not impose any a priori bound on the number of clusters in the data. This is in contrast with finite mixture models, which assume a finite and fixed number of clusters that have to be specified before the analysis is started. The column describes infinite mixture models through a generative story and then uses Gibbs sampling to cluster the food facts. It can be seen that the number of clusters detected by the model varies as we feed in more food products. As expected, the model discovers more clusters as more food products arrive. Additionally, results show that detected food clusters have distinct nutritional profiles revealing interesting nutrition patterns.

 

ISMB 2016: Connecting Gene-Disease Contexts

We presented our recent approach for disease module detection at the ISMB 2016Slides are available. The method is capable of making inference over heterogeneous data collections in new interesting ways! One of them, an approach we call jumping across data contexts, connects entities, such as genes and diseases, through semantically distinct chains, which are estimated by a collective latent variable model.

 
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