Marinka Zitnik

Fusing bits and DNA

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Stanford University

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 painstakingly...

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 Bioinformatics....

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: 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: Named a Rising Star in 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 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...

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