Software engineering for scientific big data analysis

Grüning, Björn; Lampa, Samuel; Vaudel, Marc; Blankenberg, Daniel

doi:10.1093/gigascience/giz054

“…Ultimately, we wrapped each of these components in a command line interface (CLI) such that the results presented in each section of this work can be generated with a corresponding command following the guidelines described by Grüning et al (2019). The scripts for generating the figures in this manuscript are not included in the main pathway_forte, but rather in their own repository within Jupyter notebooks at .…”

Section: Methodsmentioning

confidence: 99%

The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

Mubeen

¹

,

Hoyt

²

,

Gemünd

³

et al. 2019

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Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.

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“…palletsprojects.com). Drug2ways allows users to use the algorithm on a variety of standard network formats (e.g., GraphML, Node-Link, and EdgeList) and is powered by a CLI, following the standard proposed by [38]. The CLI offers all the case scenarios for proposing drug candidates that are presented in the results section.…”

Section: Software and Implementationmentioning

confidence: 99%

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

Rivas

¹

,

Mubeen

²

,

Bernat

³

et al. 2020

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Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.

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“…The package leverages state-of-the-art Python packages such as NetworkX for network analysis (Hagberg et al ., 2008), MPI for parallelization ( https://mpi4py.readthedocs.io/ ), and click for exposing the command line interface (CLI) ( https://click.palletsprojects.com ). Drug2ways allows users to use the algorithm on a variety of standard network formats (e.g., GraphML, Node-Link, and EdgeList) and is powered by a CLI, following the standard proposed by Grüning et al . (2019).…”

Section: Software and Implementationmentioning

confidence: 99%

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

Rivas

¹

,

Mubeen

²

,

Guim-Bernat

³

et al. 2020

Preprint

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Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.

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Software engineering for scientific big data analysis

Cited by 28 publications

References 24 publications

The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

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