Protein Ontology (PRO): enhancing and scaling up the representation of protein entities

Natale, Darren A.; Arighi, Cecilia N.; Blake, Judith A.; Bona, Jonathan P.; Chen, Chuming; Chen, Sheng‐Chih; Christie, Karen R.; Cowart, Julie; D’Eustachio, Peter; Diehl, Alexander D.; Drabkin, Harold J.; Duncan, William D.; Huang, Hongzhan; Ren, Jia; Ross, Karen; Ruttenberg, Alan; Shamovsky, Veronica; Smith, Barry; Wang, Qinghua; Zhang, Jian; Elsayed, Abdelrahman; Wu, Cathy H.

doi:10.1093/nar/gkw1075

Cited by 85 publications

(71 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several drug target related new resources have been developed, such as the ChEMBL Drug Target Slim [35], where GO annotations are available for drug targets in ChEMBL. Protein Ontology recently enhanced the protein annotation with pathway information and phosphorylation sites information [36]. Comprehensive FDA proved drug and target information is available in [3].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Drug Target Ontology to Classify and Integrate Drug Discovery Data

Lin

Mehta

McGinty

et al. 2017

Preprint

View full text Add to dashboard Cite

Background: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. TheIlluminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. Conclusions: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/117564 doi: bioRxiv preprint first posted online Mar. 16, 2017; -3 -binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery.The first version of DTO is publically available via several mechanisms.The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.peer-reviewed)

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

Drug Target Ontology to Classify and Integrate Drug Discovery Data

Lin

Mehta

McGinty

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Ontologies employ a directed acyclic graph (DAG) representation, usually described as a list of "triples" (subject, predicate, object). The KG included concepts from the Gene Ontology [4,5], Protein Ontology [22], Cell Ontology [23], Human Phenotype Ontology [24], Human Disease Ontology [25], and Chemical Entities of Biological Interest [26], among others, in a semantically-consistent fashion. Only human entities and the relations between them were included.…”

Section: Knowledge Graphmentioning

confidence: 99%

Applying knowledge-driven mechanistic inference to toxicogenomics

Tripodi¹,

Callahan²,

Westfall³

et al. 2019

Preprint

View full text Add to dashboard Cite

AbstractGovernment regulators and others concerned about toxic chemicals in the environment hold that a mechanistic, causal explanation of toxicity is strongly preferred over a statistical or machine learning-based prediction by itself. Elucidating a mechanism of toxicity is, however, a costly and time-consuming process that requires the participation of specialists from a variety of fields, often relying on animal models. We present an innovative mechanistic inference framework (MechSpy), which can be used as a hypothesis generation aid to narrow the scope of mechanistic toxicology analysis. MechSpy generates hypotheses of the most likely mechanisms of toxicity, by combining a semantically-interconnected knowledge representation of human biology, toxicology and biochemistry with gene expression time series on human tissue. Using vector representations of biological entities, MechSpy seeks enrichment in a manually-curated list of high-level mechanisms of toxicity, represented as biochemically- and causally-linked ontology concepts. Besides predicting the canonical mechanism of toxicity for many well-studied compounds, we experimentally validated some of our predictions for other chemicals without an established mechanism of toxicity. This framework can be modified to include additional mechanisms of toxicity, and is generalizable to other types of mechanisms of human biology.Author summarySeveral recent computational methods have displayed excellent performance in predicting toxicity outcomes [1–3] of chemicals. Yet, to our knowledge, there is to date no computational approach to generate mechanistic hypotheses to answer why these chemicals elicit a toxic response. There is great value in understanding the mechanism of toxicity for a chemical that appears to elicit an adverse response. Novel small molecule development is one example, where a chemical that failed initial toxicological screenings could be assessed to evaluate the actual mechanism of toxicity, greatly reducing research time and expenses on subsequent ones. The value of a mechanistic awareness of toxicity also applies to pharmacovigilance, when researching rare adverse effects of a drug in subsets of the population. The development of oncological chemotherapeutics is another example, where certain mechanisms of cytotoxicity can actually be desirable to eliminate different types of tumor cells. More importantly, the costs, time expenditure, and ethical concerns of toxicity animal models, make in vitro and in silico approaches an enticing alternative. We present a solution that uses a combination of gene expression assays and biomedical knowledge to address the gap of answering the why question.

show abstract

“…The string " CD4+IL-17-IFN-γhi" cannot be tokenized well without knowledge of protein boundaries so we developed a method hereinafter referred to as "ptkn", which would partition such an example as [CD4 + , IL-17 -, IFN-γ + ] . This method combines prior knowledge of protein aliases (from Protein Ontology [6]) as well as cytokine and transcription factor aliases with a recursive partitioning algorithm to segment character sequences with no white space into their constituent parts and normalize recognized proteins into common forms (e.g. 4-1BB and CDw137 become CD137).…”

Section: Expression Signature Tokenizationmentioning

confidence: 99%

Extracting T Cell Function and Differentiation Characteristics from the Biomedical Literature

Czech

Hammerbacher

2019

Preprint

View full text Add to dashboard Cite

The role of many cytokines and transcription factors in the function and development of human T cells has been the subject of extensive research, however much of this work only demonstrates experimental findings for a relatively small portion of the molecular signaling network that enables the plasticity inherent to these cells. We apply recent advancements in methods for weak supervision and transfer learning for natural language models to aid in extracting these individual findings as 283k cell type, cytokine, and transcription factor relations from 64k relevant documents (53k full-text PMC articles and 11k PubMed abstracts). All data, results and source code available at https://github.com/hammerlab/t-cell-relation-extraction .

show abstract

Protein Ontology (PRO): enhancing and scaling up the representation of protein entities

Cited by 85 publications

References 21 publications

Drug Target Ontology to Classify and Integrate Drug Discovery Data

Drug Target Ontology to Classify and Integrate Drug Discovery Data

Applying knowledge-driven mechanistic inference to toxicogenomics

Extracting T Cell Function and Differentiation Characteristics from the Biomedical Literature

Contact Info

Product

Resources

About