Differentially mutated subnetworks discovery

Hajkarim, Morteza Chalabi; Upfal, Eli; Vandin, Fabio

doi:10.1186/s13015-019-0146-7

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…To evaluate the confidence of association, we set the number of permutations as 100 to compute p-values and applied a threshold of p<0.01. A second approach used DAMOKLE ( 62 ) to identify differentially mutated subnetworks of protein-protein interacting complexes, as defined by the STRING database (https://string-db.org/) ( 60 ), and with a significance threshold of p<0.05. Gene essentiality scores from the DEPMAP and TCGA DEPMAP were then associated with the differentially mutated sub-networks.…”

Section: Methodsmentioning

confidence: 99%

TCGA_DEPMAP– Mapping Translational Dependencies and Synthetic Lethalities within The Cancer Genome Atlas

Shi

Verduzco

Petiwala

et al. 2022

Preprint

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The Cancer Genome Atlas (TCGA) has yielded unprecedented genetic and molecular characterization of the cancer genome, yet the functional consequences and patient-relevance of many putative cancer drivers remain undefined. TCGADEPMAP is the first hybrid map of TCGA patient dependencies that was built from 6,581 expression-based predictive models of gene essentiality across 791 cancer cell models within the Cancer Dependency Map (DEPMAP). TCGADEPMAP captured well-known cancer lineage and sub-lineage dependencies (e.g., ESR1, ERBB2, etc.), genetic drivers (e.g., KRAS, BRAF, etc.), and synthetic lethalities (e.g., STAG1/2, SMARCA2/4, etc.), demonstrating the robustness of TCGADEPMAP as a translational dependency map. TCGADEPMAP also unveiled novel synthetic lethalities that were experimentally confirmed using multiplexed CRISPR screening across multiple cancer cell lines. Other map "extensions" were built to capture unique aspects of patient-relevant tumor dependencies, including translating gene essentiality to drug response in patient-derived xenograft (PDX) models (i.e., PDXEDEPMAP) and predicting gene tolerability within normal tissues (GTEXDEPMAP). Collectively, this study demonstrates how translational dependency maps can be used to leverage the rapidly expanding catalog of patient genomic datasets to identify and prioritize novel therapeutic targets with the best therapeutic indices.

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Section: Methodsmentioning

confidence: 99%

TCGA_DEPMAP– Mapping Translational Dependencies and Synthetic Lethalities within The Cancer Genome Atlas

Shi

Verduzco

Petiwala

et al. 2022

Preprint

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“…Morteza Chalabi Hajkarim et al (2019) [42] aim at identifying differentially mutated subnetworks of a large genegene interaction network by defining a computational problem then showing that this problem is a NP-Hard problem. The proposed method identifies subnetworks infrastructures that have a statistically significant difference in their frequency of mutation in cases where aa reasonable generative model is used as a data source.…”

Section: Literature Reviewmentioning

confidence: 99%

Meaningful Infrastructures in Biological Networks: Related Studies, Algorithms and Evaluation Parameters

Ngobesing¹,

Atay²

2022

Preprint

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In network science and big data, the concept of finding meaningful infrastructures in networks has emerged as a method of finding groups of entities with similar properties within very complex systems. The whole concept is generally based on finding subnetworks which have more properties (links) amongst nodes belonging to the same cluster than nodes in other groups (A concept presented by Girvan and Newman, 2002). Today meaningful infrastructure identification is applied in all types of networks from computer networks, to social networks to biological networks. In this article we will look at how meaningful infrastructure identification is applied in biological networks. This concept is important in biological networks as it helps scientist discover patterns in proteins or drugs which helps in solving many medical mysteries. This article will encompass the different algorithms that are used for meaningful infrastructure identification in biological networks. These include Genetic Algorithm, Differential Evolution, Water Cycle Algorithm (WCA), Walktrap Algorithm, Connect Intensity Iteration Algorithm (CIIA), Firefly algorithms and Overlapping Multiple Label Propagation Algorithm. These al-gorithms are compared with using performance measurement parameters such as the Mod-ularity, Normalized Mutual Information, Functional Enrichment, Recall and Precision, Re-dundancy, Purity and Surprise, which we will also discuss here.

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“…For example, finding a sub-graph within a larger graph network is an NP-hard problem (Barillot et al, 2013 [78]). Finding differentially mutated subnetworks of a larger gene-gene interaction network is likewise an NPhard problem (Lu et al, 2016 [79]; Hajkarim et al, 2019 [80]). With chaotic attractors, the computational complexity and intractability is much greater.…”

Section: Boolean Networkmentioning

confidence: 99%

A Complex Systems Analysis of Cancer Networks

Uthamacumaran¹

2019

Preprint

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Cancers are complex, adaptive ecosystems. It remains the lead cause of disease-related, pediatric death in North America. The emerging field of complexity science has redefined cancer as a computational system with intractable, algorithmic complexity. Herein, a tumor and its heterogeneous phenotypes are discussed as dynamical systems having multiple, chaotic attractors. Machine learning, Network science and information theory are discussed as current tools for cancer network reconstruction. The fluid dynamics of cancer-cell fate transitions and chemical pattern formation are briefly reviewed. Deep Learning architectures, delay-embedding algorithms and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems. Cancer cell decision-making is investigated within the framework of complexity theory.

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Differentially mutated subnetworks discovery

Cited by 10 publications

References 40 publications

TCGA_DEPMAP– Mapping Translational Dependencies and Synthetic Lethalities within The Cancer Genome Atlas

TCGA_DEPMAP– Mapping Translational Dependencies and Synthetic Lethalities within The Cancer Genome Atlas

Meaningful Infrastructures in Biological Networks: Related Studies, Algorithms and Evaluation Parameters

A Complex Systems Analysis of Cancer Networks

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Differentially mutated subnetworks discovery

Cited by 10 publications

References 40 publications

TCGADEPMAP– Mapping Translational Dependencies and Synthetic Lethalities within The Cancer Genome Atlas

TCGADEPMAP– Mapping Translational Dependencies and Synthetic Lethalities within The Cancer Genome Atlas

Meaningful Infrastructures in Biological Networks: Related Studies, Algorithms and Evaluation Parameters

A Complex Systems Analysis of Cancer Networks

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Product

Resources

About

TCGA_DEPMAP– Mapping Translational Dependencies and Synthetic Lethalities within The Cancer Genome Atlas

TCGA_DEPMAP– Mapping Translational Dependencies and Synthetic Lethalities within The Cancer Genome Atlas