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 acr...

Also labeled: Computer Science, Data Fusion, Factorized Models, Machine Learning, Stanford University

Our paper on Jumping across biomedical contexts using compressive data fusion has just appeared in Bioinformatics. We will present the paper at ISMB 2016 in July 2016. The rapid growth of diverse biological data allows us to consider interactions...

Our paper on integrative analysis of multiple RNA-binding proteins has just appeared in Bioinformatics. RNA binding proteins (RBPs) are important for many cellular processes, including post-transcriptional control of gene expression, splicing,...

Also labeled: Data Fusion, Factorized Models

Our paper on Collective Pairwise Classification for Multi-Way Analysis has been published in the Proceedings of the 21st Pacific Symposium on Biocomputing. We will present the work at the PSB conference in January 2016. In the paper, we develop a...

Our paper on Sieve-based relation extraction of gene regulatory networks from biological literature has been published in BMC Bioinformatics. In the paper, we describe a network extraction algorithm, which is an improvement on our winning submission...

Also labeled: Information Retrieval, Computer Science

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, Computer Science, Data Fusion, Factorization, Factorized Models, Latent Models, Machine Learning

Together with Blaz Zupan we organize a tutorial on data fusion at the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). In the tutorial, we will explore latent factor models, a popular class of approaches that...

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

The Summer issue of ACM XRDS is here! In this issue we write about computational biology. Our features and interviews present different perspectives about some of the most recent advances of computational biology. You can read about personalized...

Also labeled: Association for Computing Machinery, Computer Science, Factorization, Factorized Models, Latent Models, Data Mining

Our paper at ISMB 2015 addresses a challenging task of inferring gene networks by taking into consideration potentially many data sets. Importantly, these data sets might be nonidentically distributed and can follow any combination of exponential...

Our poster at ISMB 2015 is concerned with data set selection and sensitivity estimation in collective factor models. Molecular biology data is rich in volume as well as heterogeneity. We can view individual data sets as relations between objects of...

Also labeled: Computer Science, Data Fusion, Data Mining, Factorization, Factorized Models, Latent Models, Maths, Numerical Analysis

My talk at the Summer School on Computational Topology in Ljubljana, Slovenia was about coupling compressive data fusion methods with algebraic topology, in particular persistent homology. There, I discussed how the latent data space obtained by fusi...

Our paper on Gene network inference by fusing data from diverse distributions has been published in Bioinformatics. We will present it at ISMB 2015 in Dublin. In the paper we describe FuseNet, a Markov network formulation that infers networks from a...

Also labeled: Cancer Genomes Analysis, Data Fusion, Latent Models, Probabilistic Numerics, Network Science, Maths

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...

Also labeled: Baylor College of Medicine, Data Fusion, Factorization, Latent Models, Machine Learning

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: Data Fusion, Factorization, Factorized Models, Latent Models, Machine Learning, 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, Machine Learning, Maths, Survival Regression

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 knowledg...

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

The Winter 2014 issue of ACM XRDS is here! This issue is on health informatics, which has received considerable attention both in research and general public in recent years. You can read about the opportunities of social media in health and...

Also labeled: Association for Computing Machinery, Computer Science, Network Science, Stanford University

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: Data Fusion, Factorization, Factorized Models, Latent Models, Machine Learning, Maths, Optimization

I am visiting the Department of Computer Science at Stanford University, CA, USA in Summer and Fall 2014. During my stay we will study the interplay between network analysis, data integration and biology. There are many exciting challenges one ca...

I have presented our recent approach for epistasis-based gene network inference at ISMB 2014. We propose a factorized model of interactions that is used for scoring of different types of gene-gene relationships, such as epistasis, parallelism and...