Marinka Zitnik

Fusing bits and DNA

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Article: BioSNAP Datasets: Stanford Biomedical Network Dataset Collection

We are announcing a new dataset collection, BioSNAP Datasets! BioSNAP Datasets contains many large biomedical networks that are ready-to-use for method development, algorithm evaluation, benchmarking, and network science analysis. We look forward to...

Also labeled: Computer Science

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

I had a wonderful time working on my new survey paper about machine learning for integrating data in biology and medicine. This paper is especially suitable for computational researchers who are curious about recent developments and applications of...

Article: Nature Communications: Prioritizing Network Communities

Community detection allows one to decompose a network into its building blocks. While communities can be identified with a variety of methods, their relative importance cannot be easily derived. In this Nature Communications paper, we introduce an...

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: Rising Star in EECS

I am both honored and excited to be selected as MIT EECS Rising Star!

Also labeled: Computer Science

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: JMM 2018: Invited Talk on Prioritization of Network Communities

I am giving a talk on prioritization of network communities, a framework that enables speeding-up scientific discovery process in experimental network sciences. It is very exciting to be able to present this challenging and important problem at...

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: 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: 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.

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