I am excited to announce that our tutorial onÂ Representation learning for network biology is accepted atÂ ISMB 2018. I will present the tutorial at ISMB 2018 conference in Chicago, IL. Stay tuned for more information and tutorial materials. Networks a...

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

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 acr...

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, Data Fusion

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, Data Fusion, Factorization, 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 Fusion, Data Mining, Factorization, 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, Bioinformatics, Computer Science, Factorization, Latent Models, Data Mining

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 Fusion, Data Mining, Factorization, Latent Models, Maths, Numerical Analysis

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...

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, 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, 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 knowledg...

Also labeled: Bioinformatics, Computer Science, Data Fusion, Factorization, 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, Data Fusion, Factorization, Latent Models, Machine Learning, Maths, Optimization

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...

I have presented at CAMDA 2014 an extension of our recent matrix factorization-based data fusion approach that couples data fusion with survival regression. CAMDA 2014 runs as a satellite meeting at ISMB 2014, Boston, MA, USA. Our presentation got...

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

Bioinformatics just published a special issue devoted to ISMB 2014 proceedings papers that will be presented next month at the world's premier conference on computational biology -- ISMB 2014 in Boston, MA, USA. Our paper, Gene network inference by...

We are presenting a poster about our recent data fusion methodology (ArXiv preprint) at RECOMB Conference. Thanks to Prof. Blaz Zupan for the storyline and Prof. Richard H. Kessin for valuable comments. xkcd.com served as an inspiration of poster...

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

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, Machine Learning, Data Fusion, Matrix Completion, Latent Models