Selected proceedings of Machine Learning in Systems Biology: MLSB 2016
Citation:
BMC Bioinformatics
2016
17(Suppl 16):437
Volume 17 Supplement 16
Proceedings of the 10th International Workshop on Machine Learning in Systems Biology (MLSB 2016)
Research
Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
Den Haag, The Netherlands3-4 September 2016
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DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks
Functional genomic and epigenomic research relies fundamentally on sequencing based methods like ChIP-seq for the detection of DNA-protein interactions. These techniques return large, high dimensional data set...
Citation: BMC Bioinformatics 2016 17(Suppl 16):447 -
ChARM: Discovery of combinatorial chromatin modification patterns in hepatitis B virus X-transformed mouse liver cancer using association rule mining
Various chromatin modifications, identified in large-scale epigenomic analyses, are associated with distinct phenotypes of different cells and disease phases. To improve our understanding of these variations, ...
Citation: BMC Bioinformatics 2016 17(Suppl 16):452 -
Spectral consensus strategy for accurate reconstruction of large biological networks
The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to lar...
Citation: BMC Bioinformatics 2016 17(Suppl 16):493 -
On the inconsistency of ℓ 1-penalised sparse precision matrix estimation
Various ℓ 1-penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation and learning of undirected network structure from data....
Citation: BMC Bioinformatics 2016 17(Suppl 16):448 -
DegreeCox – a network-based regularization method for survival analysis
Modeling survival oncological data has become a major challenge as the increase in the amount of molecular information nowadays available means that the number of features greatly exceeds the number of observa...
Citation: BMC Bioinformatics 2016 17(Suppl 16):449 -
Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation
Identifying molecular signatures of disease phenotypes is studied using two mainstream approaches: (i) Predictive modeling methods such as linear classification and regression algorithms are used to find signa...
Citation: BMC Bioinformatics 2016 17(Suppl 16):0
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