Marinka Zitnik

Fusing bits and DNA

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ACM XRDS: The Marvel Comic Book Universe

The Fall issue of ACM XRDS is here! In this issue we write about virtual reality. Among others, you can read about the virtual reality revolution and ways to bring virtual reality home. The issue also discusses how to use your own muscles to achieve realistic physical experience, how to manage cybersickness in virtual reality, and how to avoid danger with mine disaster simulations.

My department contributed a column on mining the Marvel comic book universe. Together with Lara Zupan we scraped Wikipedia to obtain information on the Marvel comics characters and then analyzed the structure of the Marvel multiverse network, where two characters were considered linked if they shared a skill set. Here, the analysis of complex networks allowed us to better understand how properties of fictitious networks emerge from non-trivial interactions between characters.

 

BMC Bioinformatics: Extracting Gene Regulatory Networks from Text

Our paper on Sieve-based relation extraction of gene regulatory networks from biological literature has been published in BMC Bioinformatics.

In the paper, we describe a network extraction algorithm, which is an improvement on our winning submission to BioNLP 2013. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. To enable extraction of distant relations we transform the data into skip-mention sequences. We then infer multiple models, each of which is able to extract a particular relationship type (e.g., inhibition, activation, binding). Further analysis following the challenge showed that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. The analysis also showed that our choice of transforming data into skip-mention sequences is appropriate for detecting relations between distant mentions.

 

PLoS CompBio: Gene Prioritization by Compressive Data Fusion

Our paper on Gene prioritization by compressive data fusion and chaining has been published in PLoS Computational Biology.

In the paper, we present Collage, a new data fusion approach to gene prioritization. Together with collaborators from Baylor College of Medicine, we tested Collage by prioritizing bacterial response genes in Dictyostelium as a novel model system for prokaryote-eukaryote interactions.

We started from four bacterial response genes and 14 different data sets ranging from gene expression to pathway and literature information. Collage proposed eight candidate genes that were tested in the wet laboratory. Mutations in all eight candidates reduced the ability of the amoebae to grow on Gram-negative bacteria. Furthermore, five out of the eight candidate genes were required for growth on Gram-negative bacteria but had no discernible effect on growth on Gram-positive bacteria. This is a remarkably accurate result since only about a hundred of the 12,000 Dictyostelium genes are estimated to be responsible for bacterial response.

 

Data Fusion Tutorial at the IEEE Engineering in Medicine and Biology

Together with Blaz Zupan we organize a tutorial on data fusion at the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

In the tutorial, we will explore latent factor models, a popular class of approaches that have in recent years seen many successful applications in integrative data analysis. We will describe the intuition behind matrix factorization and explain why latent factor models are suitable when collectively analyzing many heterogeneous data sets. To practice data fusion, we will construct visual data fusion workflows using Orange and its Data Fusion add-on.

This tutorial would not be possible without the great support by the Bioinformatics Laboratory at University of Ljubljana.

 

ACM XRDS: Understanding Cancer with Matrix Factorization

The Summer issue of ACM XRDS is here! In this issue we write about computational biology. Our features and interviews present different perspectives about some of the most recent advances of computational biology. You can read about personalized medicine and the use of genetic data to improve drug treatment, pharmacogenetics, machine learning techniques for mapping genetic differences to phenotypes in large-scale genome-wide association studies, computational approaches towards prediction of patient outcomes based on electronic health records, statistical techniques for drug discovery, etc. This issue also includes discussions on cutting-edge techniques, such as the analysis of single cell measurement data.

My department contributed a column on mining cancer data with matrix factorization, an established class of algorithms that proved useful in many bioinformatic studies. Diversity and abundance of data provided by the cancer projects like The International Cancer Genome Consortium challenge computer scientists of all kinds to develop innovative software, hardware, and analytic solutions for data analysis. We expect that with computationally and statistically stronger approaches, such as factorization models, we will be once able to reveal biological features that drive cancer development, define cancer types relevant for prognosis, and, ultimately, enable the development of new cancer therapies.

 

ISMB 2015: Gene Network Inference via Data Fusion

Our paper at ISMB 2015 addresses a challenging task of inferring gene networks by taking into consideration potentially many data sets. Importantly, these data sets might be nonidentically distributed and can follow any combination of exponential family distributions. To tackle this challenge we develop an efficient Markov network model that achieves fusion by reusing latent model parameters.

Empirical studies on cancer genome data sets show an advantage of joint inference over separate network inference and the merits of incorporating information about the underlying data distribution into inference.

The slides of the talk are available.

 

ISMB 2015: Integrate Everything but the Kitchen Sink

Our poster at ISMB 2015 is concerned with data set selection and sensitivity estimation in collective factor models.

Molecular biology data is rich in volume as well as heterogeneity. We can view individual data sets as relations between objects of different types, for example, function annotations describe relationships between genes and functions. We represent a large data compendium with a multiscale and multiplex relation graph. Recently, latent factor models were developed to fuse such representations and collectively infer accurate prediction models (Zitnik & Zupan, IEEE TPAMI 2015). Here, we are interested in how changes in one relation (data set) affect the latent model of another relation in the context of a given collective latent factor model. For example, in a user-movie recommendation system, how would a change of casting affect user's movie preferences? In bioinformatics, how would a change in gene expression data influence prediction of gene-disease associations?

We address this challenge by developing an approach to estimate dependence between any two relations within a single run of inference algorithm. Forensic derives from the theory of Frechet derivation and matrix conditioning and can be used with any collective matrix factorization.

See our poster for more details.

 

Compressive Data Fusion and Persistent Homology

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My talk at the Summer School on Computational Topology in Ljubljana, Slovenia was about coupling compressive data fusion methods with algebraic topology, in particular persistent homology. There, I discussed how the latent data space obtained by fusion of heterogeneous biological data sets can be explored with topological methods.

In a case study from molecular biology, which included nearly two dozen data sets, we studied persistence (lifetime) of various topological features, e.g. connected components, loops, voids, tunnels, etc. We showed that significant topological features, i.e. features with long lifetime, also carry biologically relevant information. For example, gene modules with significant topology were enriched for cellular functions and biological processes, and, similarly, persistent drug modules captured the structural similarity between drugs.

The slides of the talk are available.

Last Updated on Thursday, 18 February 2016 07:47
 

Invited Talk on Learning Latent Factor Models by Data Fusion

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Our invited talk at the Workshop on Matrix Computations for Biomedical Informatics at the 15th Conference on Artificial Intelligence in Medicine, AIME in Pavia, Italy, discussed the use of collective latent factor models for various predictive modeling tasks in biomedicine, such as gene prioritization, gene function prediction, network inference and discovery of disease-disease associations.

In the talk given together with Blaz Zupan, we highlighted our recent developments of data fusion approaches via latent factor models.

The slides of the talk are available at Prezi.

Last Updated on Friday, 21 August 2015 16:05
 

Bioinformatics: Gene Network Inference by Fusing Diverse Distributions

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Our paper on Gene network inference by fusing data from diverse distributions has been published in Bioinformatics. We will present it at ISMB 2015 in Dublin.

In the paper we describe FuseNet, a Markov network formulation that infers networks from a collection of potentially nonidentically distributed datasets.

Markov networks are undirected graphical models that are widely used to infer relations between genes from experimental data. Their state-of-the-art inference procedures assume the data arise from a Gaussian distribution. High-throughput omics data, such as that from next generation sequencing, often violates this assumption. Furthermore, when collected data arise from multiple related but otherwise nonidentical distributions their underlying networks are likely to have common features. New principled statistical approaches are needed that can deal with different data distributions and jointly consider collections of datasets.

FuseNet is computationally efficient and general: given any number of distributions from an exponential family, FuseNet represents model parameters through shared latent factors that define neighborhoods of network nodes. In a simulation study, we demonstrate good predictive performance of FuseNet in comparison to several popular graphical models. We show its effectiveness in an application to breast cancer RNA-sequencing and somatic mutation data, a novel application of graphical models. Fusion of datasets offers substantial gains relative to inference of separate networks for each dataset. We also demonstrate that network inference methods for non-Gaussian data can help in accurate modeling of the data generated by emergent high-throughput technologies.

Last Updated on Friday, 21 August 2015 16:06
 

Poster Award at the Basel Computational Biology Conference

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Our poster on Gene prioritization by compressive data fusion and chaining got best poster award at the Basel Computational Biology Conference ([BC]^2).

The poster highlights our recent computational method that prioritizes genes by fusing heterogeneous data. An appealing property of our approach is its ability to consider data that might be provided in totally different input spaces, which is achieved through chaining of the latent data representation. We report on a very successful application in hunting genes responsible for bacterial resistance in Dictyostelium, where our predictions were validated in the wet lab.

Last Updated on Sunday, 14 June 2015 10:05
 


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