Marinka Zitnik

Fusing bits and DNA

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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!

 

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.

   

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

Last month, I was 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!

 

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.

 

Bioinformatics: Jumping Across Contexts Using Compressive Fusion

Our paper on Jumping across biomedical contexts using compressive data fusion has just appeared in Bioinformatics. We will present the paper at ISMB 2016 in July 2016.

The rapid growth of diverse biological data allows us to consider interactions between a variety of objects, such as genes, chemicals, molecular signatures, diseases, pathways and environmental exposures. Often, any pair of objects—such as a gene and a disease—can be related in different ways, for example, directly via gene–disease associations or indirectly via functional annotations, chemicals and pathways. In this paper, we show that different ways of relating these objects carry different semantic meanings that are largely ignored by established computational methods.

We present an approach that operates on large-scale heterogeneous data collections and explicitly distinguishes between diverse data semantics. The approach detects size-k modules of objects that, taken together, appear most significant to another set of objects. The method builds on collective matrix factorization to derive different semantics, and it formulates the growing of the modules as a submodular optimization program.

In a systematic study on more than three hundred complex diseases, we show the effectiveness of the approach in associating genes with diseases and detecting disease modules.

 

ACM XRDS: Cultures of Computing

The Summer issue of ACM XRDS is here! The issue is centered around computing, culture, postcoloniality and questions of power. In it, many fascinating authors ask whether an Anglo-European culture of computing could be made more aware of its politics and what alternative cultures of computing could be realized. Our amazing issue editors, Ahmed Ansari (CMU) and Raghavendra Kandala (CMU), have tried to give the readers a slice of the incredible heterogeneity and plurality of critical scholarship and practice around the world.

The issue provides a brief introduction to decolonial computing and raises various issues around design and innovation in China, participation of Africans in the global HCI community, the life at the forefront of Indonesia's tech emancipation, and plans to develop hundreds of smart cities in India, revealing the complex politics of technological development and class.

Jennifer Jacobs (MIT) and I served as co-editors for the issue.

 
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