Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): a web server for mechanism enrichment

Domingo‐Fernándéz, Daniel; Kodamullil, Alpha Tom; Iyappan, Anandhi; Naz, Mufassra; Emon, Mohammad Asif; Raschka, Tamara; Karki, Reagon; Springstubbe, Stephan; Ebeling, Christian; Hofmann‐Apitius, Martin

doi:10.1093/bioinformatics/btx399

Cited by 40 publications

(47 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, BEL has been successfully used as a semantic and modeling framework for multi-scale and multi-modal knowledge in order to investigate the aetiology of complex neurodegenerative diseases as shown by Domingo-Fernández et al . with the NeuroMMSig Mechanism Enrichment Server [31]. While the list of published BEL-specific algorithms is currently short (e.g., Reverse Causal Reasoning [32], Network Perturbation Amplitude [33], etc.…”

Section: /18mentioning

confidence: 99%

BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language

Hoyt

Domingo‐Fernándéz

Hofmann-Apitius

2018

Preprint

Self Cite

View full text Add to dashboard Cite

The rapid accumulation of knowledge in the field of systems and networks biology during recent years requires complex, but user-friendly and accessible web applications that allow from visualization to complex algorithmic analysis. While several web applications exist with various focuses on creation, revision, curation, storage, integration, collaboration, exploration, visualization, and analysis, many of these services remain disjoint and have yet to be packaged into a cohesive environment.Here, we present BEL Commons; an integrative knowledge discovery environment for networks encoded in the Biological Expression Language (BEL). Users can upload files in BEL to be parsed, validated, compiled, and stored with fine-granular permissions. After, users can summarize, explore, and optionally shared their networks with the scientific community. We have implemented a query builder wizard to help users find the relevant portions of increasingly large and complex networks and a visualization interface that allows them to explore their resulting networks. Finally, we have included a dedicated analytical service for performing data-driven analysis of knowledge networks to support hypothesis generation.This web application can be freely accessed at https://bel-commons.scai.fraunhofer.de .

show abstract

Section: /18mentioning

confidence: 99%

BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language

Hoyt

Domingo‐Fernándéz

Hofmann-Apitius

2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The lower-right box represents the comparative mechanism enrichment that was used to generate comorbidity insights (lower-left box) after literature-based methods proved insufficient. [23] as well as the addition of Parkinson's disease due to its previously published relevance [24].…”

Section: Figurementioning

confidence: 99%

“…The resulting 784 terms representing mechanisms were curated in order to normalize entities, remove irrelevant entries, and group similar terms. Next, relations in the Epilepsy Knowledge Assembly were annotated with mechanisms based on whether their entities were involved in the mechanism as outlined by Domingo-Fernández et al [24]. During the annotation process, new mechanisms not yet included in the inventory were found in the literature; thus, the mechanism inventory was parallely updated until concluding the annotation with a total of 32 annotated subgraphs ( Table 2) .…”

Section: Preparation For Mechanism Enrichmentmentioning

confidence: 99%

A systematic approach for identifying shared mechanisms in epilepsy and its comorbidities

Hoyt

Domingo‐Fernándéz

Balzer

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

Cross-sectional epidemiological studies have shown that the incidence of several nervous system diseases is more frequent in epilepsy patients than in the general population. Some comorbidities (e.g., Alzheimer's disease and Parkinson's disease) are also risk factors for the development of seizures; suggesting they may share pathophysiological mechanisms with epilepsy.A literature-based approach was used to identify gene overlap between epilepsy and its comorbidities as a proxy for a shared genetic basis for disease, or genetic pleiotropy, as a first effort to identify shared mechanisms. While the results identified neurological disorders as the group of diseases with the highest gene overlap, this analysis was insufficient for identifying putative common mechanisms shared across epilepsy and its comorbidities. This motivated the use of a dedicated literature mining and knowledge assembly approach in which a cause-and-effect model of epilepsy was captured with Biological Expression Language.After enriching the knowledge assembly with information surrounding epilepsy, its risk factors, its comorbidities, and antiepileptic drugs, a novel comparative mechanism enrichment approach was used to propose several downstream effectors (including the GABA receptor, GABAergic pathways, etc.) that could explain the therapeutic effects carbamazepine in both the contexts of epilepsy and AD.We have made the Epilepsy Knowledge Assembly available at

show abstract

“…that can be directly embedded as HTML in email, Jupyter Notebook, or a web application. It has already been used to produce visualizations in the NeuroMMSig Web Service ( Domingo-Fernández et al , 2017 ). Supplementary Figures S3–S5 present these visualizations side-by-side.…”

Section: Software Architecturementioning

confidence: 99%

PyBEL: a computational framework for Biological Expression Language

et al. 2017

Self Cite

View full text Add to dashboard Cite

SummaryBiological Expression Language (BEL) assembles knowledge networks from biological relations across multiple modes and scales. Here, we present PyBEL; a software package for parsing, validating, converting, storing, querying, and visualizing networks encoded in BEL.Availability and implementationPyBEL is implemented in platform-independent, universal Python code. Its source is distributed under the Apache 2.0 License at https://github.com/pybel.Supplementary information Supplementary data are available at Bioinformatics online.

show abstract

Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): a web server for mechanism enrichment

Cited by 40 publications

References 8 publications

BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language

BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language

A systematic approach for identifying shared mechanisms in epilepsy and its comorbidities

PyBEL: a computational framework for Biological Expression Language

Contact Info

Product

Resources

About