63 research outputs found
Large-scale analysis of disease pathways in the human interactome
Discovering disease pathways, which can be defined as sets of proteins
associated with a given disease, is an important problem that has the potential
to provide clinically actionable insights for disease diagnosis, prognosis, and
treatment. Computational methods aid the discovery by relying on
protein-protein interaction (PPI) networks. They start with a few known
disease-associated proteins and aim to find the rest of the pathway by
exploring the PPI network around the known disease proteins. However, the
success of such methods has been limited, and failure cases have not been well
understood. Here we study the PPI network structure of 519 disease pathways. We
find that 90% of pathways do not correspond to single well-connected components
in the PPI network. Instead, proteins associated with a single disease tend to
form many separate connected components/regions in the network. We then
evaluate state-of-the-art disease pathway discovery methods and show that their
performance is especially poor on diseases with disconnected pathways. Thus, we
conclude that network connectivity structure alone may not be sufficient for
disease pathway discovery. However, we show that higher-order network
structures, such as small subgraphs of the pathway, provide a promising
direction for the development of new methods
Survival regression by data fusion
Any knowledge discovery could in principal benefit from the fusion of directly or even indirectly related data sources. In this paper we explore whether data fusion by simultaneous matrix factorization could be adapted for survival regression. We propose a new method that jointly infers latent data factors from a number of heterogeneous data sets and estimates regression coefficients of a survival model. We have applied the method to CAMDA 2014 large- scale Cancer Genomes Challenge and modeled survival time as a function of gene, protein and miRNA expression data, and data on methylated and mutated regions. We find that both joint inference of data factors and regression coefficients and data fusion procedure are crucial for performance. Our approach is substantially more accurate than the baseline Aalen’s additive model. Latent factors inferred by our approach could be mined further; for CAMDA challenge, we found that the most informative factors are related to known cancer processes
Survival regression by data fusion
Any knowledge discovery could in principal benefit from the fusion of directly or even indirectly related data sources. In this paper we explore whether data fusion by simultaneous matrix factorization could be adapted for survival regression. We propose a new method that jointly infers latent data factors from a number of heterogeneous data sets and estimates regression coefficients of a survival model. We have applied the method to CAMDA 2014 large- scale Cancer Genomes Challenge and modeled survival time as a function of gene, protein and miRNA expression data, and data on methylated and mutated regions. We find that both joint inference of data factors and regression coefficients and data fusion procedure are crucial for performance. Our approach is substantially more accurate than the baseline Aalen’s additive model. Latent factors inferred by our approach could be mined further; for CAMDA challenge, we found that the most informative factors are related to known cancer processes
Gene network inference by fusing data from diverse distributions
Markov networks are undirected graphical models that are widely used to infer relations between genes from experimental data. Their state-of-the-art inference procedures assume the data arise from a Gaussian distribution. High-throughput omics data, such as that from next generation sequencing, often violates this assumption. Furthermore, when collected data arise from multiple related but otherwise nonidentical distributions, their underlying networks are likely to have common features. New principled statistical approaches are needed that can deal with different data distributions and jointly consider collections of datasets. We present FuseNet, a Markov network formulation that infers networks from a collection of nonidentically distributed datasets. Our approach is computationally efficient and general: given any number of distributions from an exponential family, FuseNet represents model parameters through shared latent factors that define neighborhoods of network nodes. In a simulation study, we demonstrate good predictive performance of FuseNet in comparison to several popular graphical models. We show its effectiveness in an application to breast cancer RNA-sequencing and somatic mutation data, a novel application of graphical models. Fusion of datasets offers substantial gains relative to inference of separate networks for each dataset. Our results demonstrate that network inference methods for non-Gaussian data can help in accurate modeling of the data generated by emergent high-throughput technologies
Gene network inference by fusing data from diverse distributions
Markov networks are undirected graphical models that are widely used to infer relations between genes from experimental data. Their state-of-the-art inference procedures assume the data arise from a Gaussian distribution. High-throughput omics data, such as that from next generation sequencing, often violates this assumption. Furthermore, when collected data arise from multiple related but otherwise nonidentical distributions, their underlying networks are likely to have common features. New principled statistical approaches are needed that can deal with different data distributions and jointly consider collections of datasets. We present FuseNet, a Markov network formulation that infers networks from a collection of nonidentically distributed datasets. Our approach is computationally efficient and general: given any number of distributions from an exponential family, FuseNet represents model parameters through shared latent factors that define neighborhoods of network nodes. In a simulation study, we demonstrate good predictive performance of FuseNet in comparison to several popular graphical models. We show its effectiveness in an application to breast cancer RNA-sequencing and somatic mutation data, a novel application of graphical models. Fusion of datasets offers substantial gains relative to inference of separate networks for each dataset. Our results demonstrate that network inference methods for non-Gaussian data can help in accurate modeling of the data generated by emergent high-throughput technologies
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