OCSANA: optimal combinations of interventions from network analysis

Vera-Licona, Paola; Bonnet, Eric; Barillot, Emmanuel; Zinovyev, Andreï

doi:10.1093/bioinformatics/btt195

Cited by 31 publications

(40 citation statements)

References 10 publications

(10 reference statements)

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“…The members of DMC performed such analyses to find synthetically interacting genes [75], to predict drug synergy [76], or to suggest complex intervention sets that open a possibility of drug repositioning [51,77].…”

Section: Discussionmentioning

confidence: 99%

“…Clinical applications of disease maps [44,51] are close to the role of a Clinical Decision Support System, with an emphasis on exploration and interpretation of medically relevant data. Big health data, collected in great amounts by healthcare providers and pharmaceutical companies, needs to be structured and interpreted through visualization.…”

Section: Health and Disease Data Interpretation Pipelinesmentioning

confidence: 99%

See 1 more Smart Citation

Community-driven roadmap for integrated disease maps

Ostaszewski¹,

Gebel²,

Kuperstein³

et al. 2018

Preprint

Self Cite

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The Disease Maps Project builds upon a network of scientific and clinical groups that exchange best practices, share information, and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation, or serve as a scaffold for the generation of predictive mathematical models.This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and web-tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations in order to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.

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Section: Discussionmentioning

confidence: 99%

Section: Health and Disease Data Interpretation Pipelinesmentioning

confidence: 99%

Community-driven roadmap for integrated disease maps

Ostaszewski¹,

Gebel²,

Kuperstein³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Paola Vera-Licona et al [41] tried to identify and prioritize optimal combinations of interventions that perturb the paths from source nodes to target nodes in signaling pathways. OCSANA nodes scoring is based on the lengths of the paths from the selected node to the targets, the type of the node effect on target nodes (activation/inhibition), side effects of the node, the number of paths in which the node is included in and the number of targets that node can connect to.…”

Section: Systems Biology Based Methodsmentioning

confidence: 99%

In-Silico Methodologies for Cancer Multidrug Optimization

Hasan¹,

Eldin

Khedr

et al. 2018

IJCT

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Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic were found out through experience. The molecular mechanisms underlying these drug combinations are often not clear. It is not easy to suggest new drug combinations. Computational approaches are proposed to reduce the search space for defining the most promising combinations and prioritizing their experimental evaluation. In this paper, we review methods, techniques and hypotheses developed for in silico methodologies for drug combination discovery in cancer and discuss the limitations and challenges of these methods.

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“…Thus high-throughput data can be integrated for scoring and selecting optimal synthetic lethal sets in each particular patient, exploiting the specific vulnerabilities. [56]. These and other topologybased methods for structural analysis of maps are used for retrieving intervention M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 11 combinations in cancer and predicting response to treatment (Table 2 and extensively reviewed in [57]).…”

Section: A C C E P T E D Accepted Manuscript Assuming Interaction Parmentioning

confidence: 99%