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...

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...

Our new study published in Nature Communications explores the NUDIX hydrolases in human cells and provides attractive opportunities for expanding the use of this enzyme family as biomarkers and potential novel drug targets. The NUDIX enzymes are ...

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 ...

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...

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

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: Bioinformatics, 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 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, 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: Bioinformatics, Computer Science, Data Mining, Factorization, Factorized Models, Latent Models, Orange, Visualization

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: Bioinformatics, Computer Science, 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 f...

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 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: Bioinformatics, Cancer Genomes Analysis, 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, Bioinformatics, 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: Bioinformatics, 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, Factorization, Factorized Models, Latent Models, Machine Learning, 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, Factorization, Factorized Models, Latent Models, Machine Learning, 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, Factorization, Factorized Models, Latent Models, Machine Learning, Maths, Optimization