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Overview
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This page outlines the resources generated for the Human Brain Pharmacome project lead by Charles Tapley Hoyt in 2018 and 2019.

Knowledge Storage, Manipulation, and Exploration

One of the first goals of the Human Brain Pharmacome Project is to bring order to the unstructured knowledge locked in the biomedical literature, patents, and electronic health records.

Storing Knowledge: Biological Expression Language

We heavily rely on Biological Expression Language (BEL) as a format for storing qualitative causal and correlative relations between biological entities across multiple modes and scales, with full provenance information including namespace references, relation provenance (citation and evidence), and biological context-specific relation metadata (anatomy, cell, disease etc.)

EpiCom Workflow

Reference

Slater, T. (2014). Recent advances in modeling languages for pathway maps and computable biological networks. Drug Discovery Today, 19(2), 193–198.

Resources

Manipulating Knowledge: PyBEL

PyBEL Logo We built PyBEL for parsing, validating, compiling, and converting networks encoded in BEL. PyBEL comprises five main components: (i) network data container, (ii) parser and validator, (iii) network database manager, (iv) data converter and (v) network visualizer.

Reference

Hoyt, C. T., Konotopez, A., Ebeling, C. (2018). PyBEL: a computational framework for Biological Expression Language. Bioinformatics, 34(4), 703–704.

Resources

Exploring Knowledge: BEL Commons

BEL Commons Logo

BEL Commons is an environment for curating, validating, and exploring knowledge assemblies encoded in BEL to support elucidating disease-specific, mechanistic insight. BEL Commons comprises five components: (i) the network uploader and validator, (ii) user rights and project management, (iii) the query builder, (iv) the biological network explorer and (iv) the analytical service.

BEL Commons Components

Reference

Hoyt, C. T., Domingo-Fernández, D., & Hofmann-Apitius, M. (2018). BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language. Database, 2018(3), 1–11.

Resources

Curation of Unstructured Knowledge

BEL Curation Workflow

We developed a workflow using git, GitHub, PyBEL, and a novel PyBEL extension in order to identify and address syntactical issues in BEL documents in a Continuous Integration setting.

Curation Workflow

Resources

Curation of Neurodegeneration Supporting Ontology (CONSO)

While there are several useful public terminologies useful for curation of biomedical relations, there is often the need to develop new controlled vocabularies, thesauri, taxonomies, and ontologies. We have maintained ours with the ability to export it as OBO, OWL, and as a BEL namespace.

Resources

Curation of Neurodegeneration in BEL (CONIB)

We have prioritized manual curation of the highest granularity for new content related to several neurodegenerative disease, with special focus on the human Tau protein, nicotoinic receptor biology, and proteostasis. They can be downloaded and handled with PyBEL (or other BEL tools) using the link below.

Resources

NeuroMMSig

NeuroMMSig Logo

Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig) consists of three parts: a taxonomy of candidate pathophysiological mechanisms in three neurodegenerative diseases (i.e., Alzheimer's disease, Parkinson's disease, and epilepsy), disease-specific mechanistic models of each, and a web server implementing a novel mechanism enrichment algorithm.

Mechanism Taxonomy Resources

Mechanism Inventory Resources

Mechanism Enrichment Server Reference

Domingo-Fernández, D., et al. (2017). Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): A web server for mechanism enrichment. Bioinformatics, 33(22), 3679–3681.

Mechanism Enrichment Server Resources

Rational Enrichment of NeuroMMSig

Enrichment Workflow

Reference

Hoyt, C. T., et al (2019). Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language. Database, 2019.

Resources

Topic-driven Semi-Automated Curation

We used the tooling developed for the rational enrichment workflow to support topic-specific, semi-automated curation. So far, it has been applied to the MAPT and GSK3B proteins.

Resources

TauBase

We've generated a dynamic web application for exploring the content curated during this project that can be found at https://github.com/pharmacome/taubase. It will be deployed publicly soon.

Acquisition and Transformation of Structured Knowledge

Bio2BEL

Bio2BEL Logo

BEL is well-suited as an biomedical knowledge integration standard as it has precise semantics and is extensible. We generated several reusable packages for converting and harmonizing databases across modes and scales in BEL.

Bio2BEL Components

Reference

Hoyt, C. T., et al. (2019). Integration of Structured Biological Data Sources using Biological Expression Language. bioRxiv, 631812.

Resources

ComPath

ComPath Logo

We curated mappings between three major pathway databases (KEGG, Reactome, and WikiPathways) and MSigDB to identify their overlapping entries.

Equivalent pathways across three major resources

Reference

Domingo-Fernandez, D., et al. (2018). ComPath: an ecosystem for exploring, analyzing, and curating mappings across pathway databases. Npj Systems Biology and Applications, 4:43.

Resources

PathMe

PathMe Logo

We harmonized and extracted pathway knowledge from three major pathway databases: KEGG, Reactome, and WikiPathways in order to make comparisons about their coverage and to generate consensus pathways (by using ComPath) for downstream applications, such as with Signalling Pathway Impact Analysis (SPIA).

PathMe harmonization workflow

Reference

Domingo-Fernandez, D., et al. (2018). PathMe: Merging and exploring mechanistic pathway knowledge. BMC Bioinformatics, 20:243.

Resources

PathwayForte

We generated super-pathways using ComPath and PathMe in order to benchmark several pathway-based functional enrichment and prediction methods.

Building a consensus dataset using ComPath and PathMe

Reference

Mubeen, S., et al. (2019). The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling. Frontiers in Genetics, 10:1203.

Resources

Analytical Approaches

Comparative Mechanism Enrichment

We proposed an explanation for the cross-indication effects of Carbamazepine towards epilepsy and Alzheimer's disease by using the NeuroMMSig mechanism enrichment server to identify overlapping mechanisms with its experimentally measured and computationally predicted targets.

EpiCom Workflow

Reference

Hoyt, C. T. and Domingo-Fernández, D., et al. (2018). A systematic approach for identifying shared mechanisms in epilepsy and its comorbidities. Database, 2018(1).

Target Prioritization with Network Representation Learning

We used GAT2VEC to generate random walk-based node embeddings for protein-protein interaction (PPI) networks annotated with disease-specific differential gene expression profiles from GEO, ArrayExpress, and other sources. Following the prediction and evaluation pipeline from Emig, et al. (2013), we built simple generalized linear models using annotations from OpenTargets as a training set and generated rankings for all targets.

References

Muslu, Ö., Hoyt, C. T., Hofmann-Apitius, M., & Fröhlich, H. (2019). GuiltyTargets: Prioritization of Novel Therapeutic Targets with Deep Network Representation Learning. bioRxiv, 1–14.

Resources

Link Prediction with Network Representation Learning

We generated two network representation learning (NRL) libraries: one for random walk-based NRL and another for semantic translations models and neural network models.

Reference

Ali, M., Hoyt, C. T., Domingo-Fernández, D., Lehmann, J., & Jabeen, H. (2019). BioKEEN: A library for learning and evaluating biological knowledge graph embeddings. Bioinformatics, btz117.

Resources

Side Effect Prediction with Network Representation Learning

We generated node and edge embeddings for a graph of drugs, their chemical similarities, their side effects, their indications, their targets in order to predict side effects for new chemicals as well as to propose mechanisms of action for some side effects.

Resources

  • Code
  • master's thesis in preparation

Drug Repositioning with Network Representation Learning

We replaced the engineered features used by Himmelstein et al. (2017) with features learned by node2vec on the RepHetNet.

Resources

  • Code
  • master's thesis in preparation

Funding

We're funded by the Fraunhofer Society's MAVO program as a joint undertaking between Fraunhofer SCAI, Fraunhofer IAIS, and Fraunhofer IME. Also, see our blog.