Marinka Zitnik

Fusing bits and DNA

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Machine Learning

Article: Submit to Frontiers in Genetics: Single-Cell Data Analytics

I am thrilled about an opportunity to co-edit a research topic on single-cell data analytics, resources, challenges and perspectives for Frontiers in Genetics! With this research topic, we aim to provide a broad coverage of single-cell data analytic...

Article: 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...

Article: 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...

Article: 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.

Article: 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.

Article: Invited Talk on Uncovering Cellular Functions Through Multi-Layer Tissue Networks

I'm giving an invited talk on discovering gene functions through multi-layer tissue networks at the Network Medicine meeting at NetSci 2017. Check out the slides.

Article: 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 ...

Article: 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...

Article: ISMB 2016: Connecting Gene-Disease Contexts

We presented our recent approach for disease module detection at the ISMB 2016. Slides 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 ac...

Article: 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...

Article: Invited Talk on Learning Latent Factor Models by Data Fusion

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...

Article: Poster Award at the Basel Computational Biology Conference

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 ...

Article: Data Fusion Tutorial at the Basel Computational Biology Conference

Together with Blaz Zupan we organize a tutorial on data fusion at the Basel Computational Biology Conference ([BC]^2). The tutorial is targeted at computational scientists, data mining researchers and molecular biologists interested in large-scale...

Article: Syst Biomed: Survival Regression by Data Fusion

Our recent paper in Systems Biomedicine describes a new computational approach that predicts patient’s survival time from a collection of heterogeneous data sets. This is the full paper of our award winning entry at CAMDA meeting at ISMB 2014, Boston,...

Article: J Comp Biol: Network-Guided Matrix Completion

Our recent paper in Journal of Computational Biology introduces an interaction data imputation method called network-guided matrix completion (NG-MC). The core part of NG-MC is low-rank probabilistic matrix completion that incorporates prior knowledge...

Article: IEEE TPAMI: Data Fusion by Matrix Factorization

We recently published a paper on a new data fusion method in IEEE Transactions on Pattern Analysis and Machine Intelligence. For most problems in science and engineering we can obtain data sets that describe the observed system from various ...

Article: @RECOMB 2014, Pittsburgh, PA (Part I)

We got accepted a paper on Imputation of Quantitative Genetic Interactions in Epistatic MAPs by Interaction Propagation Matrix Completion to RECOMB 2014. Epistatic Miniarray Profile (E-MAP) is a popular large-scale gene interaction discovery...

Article: @Baylor College of Medicine, Department of Molecular and Human Genetics

Between December 2013 and August 2014 I am visiting the Department of Molecular and Human Genetics at Baylor College of Medicine, Houston, TX, USA. During my stay we will do research on computational methods for data fusion and their applications in...

Article: Press Coverage of Our Recent Study About Connections Between Human Diseases

BioTechniques, The International Journal of Life Science Methods highlighted our recent paper on Discovering disease-disease associations by fusing systems-level molecular data, which was published by Nature's Scientific Reports. In the paper we ...

Article: Discovering Disease-Disease Associations by Fusing Molecular Data

Nature's Scientific Reports has published our latest paper on data fusion, Discovering disease-disease associations by fusing systems-level molecular data, in which we combine various sources of biological information to discover human disease-disease...

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