Marinka Zitnik

Fusing bits and DNA

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Bioinformatics

Article: Biomedical Entity Recognition with Deep Multi-Task Learning

We propose a deep multi-task learning approach for biomedical named entity recognition, which is a fundamental task in the mining of biomedical text data. The new approach saves human efforts and frees biomedical experts from the need to painstakingly...

Article: Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

My review of machine learning for biomedical data integration is now available online in Information Fusion. This paper is intended for computer scientists and biomedical researchers who are curious about recent developments and applications of...

Article: Nature Communications: General Method to Denoise Biological Networks

Technical noise in experiments is unavoidable, but it introduces inaccuracies into the biological networks we infer from the data. In this Nature Communications paper, we introduce a diffusion-based method for denoising undirected, weighted networks,...

Article: ISMB 2018: Polypharmacy Side Effects

We presented our work on predicting polypharmacy side effects at ISMB/ECCB in Chicago, IL, USA. Here are the slides. This work has been highlighted in Stanford News, covered by several other news outlets, and is the most read paper in Bioinformatics....

Article: Tutorial on Deep Learning for Network Biology at ISMB

We just presented a tutorial on Deep Learning for Network Biology at ISMB 2018 in Chicago, IL, USA. If you are interested in these topics and would like to learn more about graph neural networks and/or their biomedical applications but could not...

Article: New Survey Paper: Machine Learning for Integrating Data in Biology and Medicine

My new survey paper on machine learning for integrating data in biology and medicine is now online. In this review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of...

Article: Bioinformatics: What side effects to expect if taking multiple drugs?

Many patients take multiple drugs at the same time to treat complex diseases, such as heart failure, or co-occurring diseases, such as diabetes and epilepsy. The use of combinations of drugs is a common practice. In fact, 25 percent of people ages 65...

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: 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 ...

Article: 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...

Article: 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...

Article: 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 ...

Article: 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...

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: 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...

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: 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.

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 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 ...

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...

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