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.

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...

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...

Also labeled: Bioinformatics, Computer Science, Deep networks, Neural Embeddings, Stanford University

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...

Also labeled: Bioinformatics, Computer Science, Factorization, Latent Models, Network Science, Stanford University

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...

Also labeled: Factorized Models, Latent Models, Network Science, Neural Embeddings, Stanford University, Data Mining

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...

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.

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.

Also labeled: Bioinformatics, Computer Science, Network Science, Neural Embeddings, Stanford University

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.

Also labeled: Bioinformatics, Computer Science, Network Science, Stanford University, Neural Embeddings

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 ...

You are cordially invited to submit a paper to the upcoming 2017 ECML PKDD conference. ECML PKDD is the European Conference on Machine Learning and Knowledge Discovery. It is the largest European conference in these areas that has developed from the...

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...

Our paper on Gene prioritization by compressive data fusion and chaining has been published in PLoS Computational Biology. In the paper, we present Collage, a new data fusion approach to gene prioritization. Together with collaborators from Baylor...

Also labeled: Baylor College of Medicine, Bioinformatics, Computer Science, Data Fusion, Factorization, Factorized Models, Latent Models

Our invited talk at the Workshop on Matrix Computations for Biomedical Informatics at the 15th Conference on Artificial Intelligence in Medicine, AIME in Pavia, Italy, discussed the use of collective latent factor models for various predictive...

Our poster on Gene prioritization by compressive data fusion and chaining got best poster award at the Basel Computational Biology Conference ([BC]^2). The poster highlights our recent computational method that prioritizes genes by fusing ...

Together with Blaz Zupan we organize a tutorial on data fusion at the Basel Computational Biology Conference ([BC]^2). The tutorial is targeted at computational scientists, data mining researchers and molecular biologists interested in large-scale...

Also labeled: Bioinformatics, Data Fusion, Factorization, Factorized Models, Latent Models, Orange, Visualization, Computer Science

Our recent paper in Systems Biomedicine describes a new computational approach that predicts patient’s survival time from a collection of heterogeneous data sets. This is the full paper of our award winning entry at CAMDA meeting at ISMB 2014, Boston,...

Also labeled: Cancer Genomes Analysis, Computer Science, Data Fusion, Factorization, Factorized Models, Latent Models, Maths, Survival Regression, Bioinformatics

Our recent paper in Journal of Computational Biology introduces an interaction data imputation method called network-guided matrix completion (NG-MC). The core part of NG-MC is low-rank probabilistic matrix completion that incorporates prior knowledge...

Also labeled: Bioinformatics, Computer Science, Data Fusion, Factorization, Factorized Models, Latent Models, Matrix Completion, Network Science, Maths

We recently published a paper on a new data fusion method in IEEE Transactions on Pattern Analysis and Machine Intelligence. For most problems in science and engineering we can obtain data sets that describe the observed system from various ...

Also labeled: Bioinformatics, Data Fusion, Factorization, Factorized Models, Latent Models, Maths, Optimization

We got accepted a paper on Imputation of Quantitative Genetic Interactions in Epistatic MAPs by Interaction Propagation Matrix Completion to RECOMB 2014. Epistatic Miniarray Profile (E-MAP) is a popular large-scale gene interaction discovery...

Also labeled: Bioinformatics, Computer Science, Data Mining, Factorization, Maths, Data Fusion, Matrix Completion, Factorized Models, Latent Models