Marinka Zitnik

Fusing bits and DNA

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Stanford University

Article: Submit to ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds

Many scientific fields study data with an underlying graph or manifold structure—such as protein networks, sensor networks, and biomedical knowledge graphs. The need for new optimization methods and neural network architectures that can accommodate...

Article: Stanford News Story on How Species Evolve Ways to Backup Life's Machinery

Spotlight on our study of versatile and robust molecular machinery and evolution of protein interactomes in Stanford Engineering News.

Article: Proceedings of the National Academy of Sciences: Evolution of Molecular Networks

Our paper on evolution of resilience in protein interactomes is published in Proceedings of the National Academy of Sciences (PNAS). Using protein-protein interaction data that have only recently become available, we composed and analyzed interacto...

Article: ICLR 2019 Workshop: Representation Learning on Graphs and Manifolds

I am very excited to be co-organizing an ICLR 2019 workshop on Representation Learning on Graphs and Manifolds. We will be having an amazing lineup of invited speakers on a variety of methods and problems in this area! Also, stay tuned for the...

Article: Guest Lecture on Graph Convolutional Networks

I have had the opportunity to give a lecture on Graph Convolutional Networks in the CS224W class (Analysis of Networks: Mining and Learning with Graphs) at Stanford. Here are slides and video of the lecture.

Article: Evolution of Protein Interactomes across the Tree of Life

The interactome network of protein-protein interactions captures the structure of molecular machinery and gives rise to a bewildering degree of life complexity. We composed and analyzed interactome networks from 1,840 species across the tree of life,...

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 painstaking...

Article: Named a Rising Star in Biomedicine

I am honored to be named a Rising Star in Biomedicine by The Broad Institute of Harvard and MIT! I am thrilled to present my research at the Next Generation in Biomedicine Symposium at the Broad.

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: BioSNAP Datasets: Stanford Biomedical Network Dataset Collection

We are announcing a repository of biomedical network datasets, BioSNAP Datasets: Stanford Biomedical Network Dataset Collection! BioSNAP aims to bring biological and medical datasets closer to computer scientists who develop new exciting algorithms....

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 Bioinformati...

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 o...

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 a...

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: Named a Rising Star in Electrical Engineering and Computer Science (EECS)

I am both honored and excited to be named a Rising Star in Electrical Engineering and Computer Science by MIT!

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 analyti...

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...

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