Marinka Zitnik

Fusing bits and DNA

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Upcoming Talks

- 08/04/2018: Tech Summit SYNC 2018 (Computer History Museum, Mountain View, CA)

- 08/22/2018: AI in Medicine: Inclusion & Equity (AiMIE) Symposium 2018 (Stanford University)

- 09/04/2018: National Guest Scholar at Stanford CERC (Stanford University)

- 09/19/2018: EMBL-EBI Workshop on Machine Learning in Drug Discovery and Precision Medicine (EBI, Hinxton, UK)


 

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 seeing more biomedical network data considered in machine learning and data science research.

 

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, and show that it improves the performances of downstream analyses, including prediction of gene functions, interpretation of noisy Hi-C contact maps, and fine-grained identification of species.

   

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 attend the tutorial because it was sold out, check out our tutorial website. All materials, including slides, network tools, examples, and code bases are available for download from the tutorial website.

In this tutorial, we cover the key conceptual foundations of representation learning, from approaches relying on network propagation to very recent advancements in deep representation learning for networks. In addition to a broad high-level overview, we spend a considerable amount of time describing the algorithmic and implementation aspects of recent advancements in deep representation learning and discussing many biomedical applications.

 

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 machine learning to biology and medicine and its potential for advancing biomedicine given the vast amounts of heterogeneous data being generated today.

In this review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. We also discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

 

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 algorithm to identify modules which are most promising for further analysis. Our method allows for more efficient evaluation of hypotheses brought forward by the analysis of complex networks and thus speeding-up scientific discovery process in experimental network sciences.

 

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 to 69 take at least five prescription drugs to treat chronic conditions, a figure that jumps to nearly 46 percent for those between 70 and 79.

However, a major consequence of drug combinations for a patient is a much higher risk of side effects. These side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. These side effects are extremely difficult to identify manually because there are combinatorically many ways in which a given combination of drugs clinically manifests and each combination is valid in only a certain subset of patients. It is also practically impossible to test all possible pairs of drugs and observe side effects in relatively small clinical testing.

In our latest research published in Bioinformatics, we develop an approach for computational screening of drug combinations. The approach predicts what side effects a patient might experience when taking multiple drugs simultaneously.

Technically, this work defines a novel approach that blends deep learning for graphs with network science to achieve benefits from each. See the paper and project website for details!

 

Rising Star in EECS

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

 

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 studies.

We encourage contributions in the form of original research articles, short communications, reviews, and perspectives, addressing the major needs and challenges in the single-cell data analytics including (but not limited to): statistical models, algorithms, and software packages to analyze single-cell data; visualization tools for interpreting single-cell data; methods to relate single-cell data with disease classification and prognosis; methods and tools to discover spatial/temporal organization of tissues at a single-cell level; models of cell-cell communication; scalable mathematical and computer-science approaches for analysis of mega-scale single-cell data; methods for combining mixed platform data, noise filtering, and robust normalization.

You are cordially invited to submit your research to the Frontiers in Genetics' single-cell data analytics research topic.

 

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 are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. This tutorial investigates key advancements in representation learning for networks over the last few years, with an emphasis on fundamentally new opportunities in network biology enabled by these advancements.

Tutorial website: http://snap.stanford.edu/deepnetbio-ismb.

 
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