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Leveraging Structured biological Knowledge for Counterfactual Inference: A Case Study of Viral Pathogenesis

IEEE TRANSACTIONS ON BIG DATA 2021年第1期7卷 25-37页

作者： Zucker, Jeremy Paneri, Kaushal Mohammad-Taheri, Sara Bhargava, Somya Kolambkar, Pallavi Bakker, Craig Teuton, Jeremy Hoyt, Charles Tapley Oxford, Kristie Ness, Robert Vitek, Olga Pacific Northwest Natl Lab Richland WA 99354 USA Microsoft Redmond WA 98052 USA Northeastern Univ Boston MA 02115 USA Enveda Biosci D-53225 Bonn Germany Altdeep Boston MA 02115 USA

Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on exogenous variables, and functional assignments. Specifying such models can be extremely difficult in practice. The process requires substantial domain expertise, and does not scale easily to large systems, multiple systems, or novel system modifications. At the same time, many application domains, such as molecular biology, are rich in structured causal knowledge that is qualitative in nature. This article proposes a general approach for querying a causal biological knowledge graph, and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question. We demonstrate the feasibility, accuracy and versatility of this approach using two case studies in systems biology. The first demonstrates the appropriateness of the underlying assumptions and the accuracy of the results. The second demonstrates the versatility of the approach by querying a knowledge base for the molecular determinants of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced cytokine storm, and performing counterfactual inference to estimate the causal effect of medical countermeasures for severely ill patients.

关键词： biological system modeling Proteins Mathematical model Stochastic processes biological processes Data models COVID-19 biological expression language structural causal model counterfactual inference causal biological knowledge graph systems biology SARS-CoV-2

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A Computational Approach for Mapping Heme Biology in the Context of Hemolytic Disorders

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2020年 8卷 74-74页

作者： Humayun, Farah Domingo-Fernandez, Daniel George, Ajay Abisheck Paul Hopp, Marie-Therese Syllwasschy, Benjamin F. Detzel, Milena S. Hoyt, Charles Tapley Hofmann-Apitius, Martin Imhof, Diana Univ Bonn Pharmaceut Inst Pharmaceut Biochem & Bioanalyt Bonn Germany Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat St Augustin Germany

Heme is an iron ion-containing molecule found within hemoproteins such as hemoglobin and cytochromes that participates in diverse biological processes. Although excessive heme has been implicated in several diseases including malaria, sepsis, ischemia-reperfusion, and disseminated intravascular coagulation, little is known about its regulatory and signaling functions. Furthermore, the limited understanding of heme's role in regulatory and signaling functions is in part due to the lack of curated pathway resources for heme cell biology. Here, we present two resources aimed to exploit this unexplored information to model heme biology. The first resource is a terminology covering heme-specific terms not yet included in standard controlled vocabularies. Using this terminology, we curated and modeled the second resource, a mechanistic knowledge graph representing the heme's interactome based on a corpus of 46 scientific articles. Finally, we demonstrated the utility of these resources by investigating the role of heme in the Toll-like receptor signaling pathway. Our analysis proposed a series of crosstalk events that could explain the role of heme in activating the TLR4 signaling pathway. In summary, the presented work opens the door to the scientific community for exploring the published knowledge on heme biology.

关键词： heme hemolytic disorders signaling pathways knowledge graphs biological expression language

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PathMe: merging and exploring mechanistic pathway knowledge

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BMC BIOINFORMATICS 2019年第1期20卷 1-12页

作者： Domingo-Fernandez, Daniel Mubeen, Sarah Marin-Llao, Josep Hoyt, Charles Tapley Hofmann-Apitius, Martin Fraunhofer Inst Algorithms & Sci Comp Dept Bioinformat D-53754 St Augustin Germany Rheinische Friedrich Wilhelms Univ Bonn Bonn Aachen Int Ctr IT D-53115 Bonn Germany

BackgroundThe complexity of representing biological systems is compounded by an ever-expanding body of knowledge emerging from multi-omics experiments. A number of pathway databases have facilitated pathway-centric approaches that assist in the interpretation of molecular signatures yielded by these experiments. However, the lack of interoperability between pathway databases has hindered the ability to harmonize these resources and to exploit their consolidated knowledge. Such a unification of pathway knowledge is imperative in enhancing the comprehension and modeling of biological ***, we present PathMe, a Python package that transforms pathway knowledge from three major pathway databases into a unified abstraction using biological expression language as the pivotal, integrative schema. PathMe is complemented by a novel web application (freely available at https://***/) which allows users to comprehensively explore pathway crosstalk and compare areas of consensus and *** work has harmonized three major pathway databases and transformed them into a unified schema in order to gain a holistic picture of pathway knowledge. We demonstrate the utility of the PathMe framework in: i) integrating pathway landscapes at the database level, ii) comparing the degree of consensus at the pathway level, and iii) exploring pathway crosstalk and investigating consensus at the molecular level.

关键词： Bioinformatics Pathways Database integration Network analysis biological networks biological expression language

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Hierarchical sequence labeling for extracting BEL statements from biomedical literature

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2019年第2-Sup期19卷 63-63页

作者： Liu, Suwen Shao, Yifan Qian, Longhua Zhou, Guodong Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China

BackgroundExtracting relations between bio-entities from biomedical literature is often a challenging task and also an essential step towards biomedical knowledge expansion. The BioCreative community has organized a shared task to evaluate the robustness of the causal relationship extraction algorithms in biological expression language (BEL) from biomedical *** first map the sentence-level BEL statements in the BC-V training corpus to the corresponding text segments, thus generating hierarchically tagged training instances. A hierarchical sequence labeling model was afterwards induced from these training instances and applied to the test sentences in order to construct the BEL *** experimental results on extracting BEL statements from BioCreative V Track 4 test corpus show that our method achieves promising performance with an overall F-measure of 31.6%. Furthermore, it has the potential to be enhanced by adopting more advanced machine learning *** propose a framework for hierarchical relation extraction using hierarchical sequence labeling on the instance-level training corpus derived from the original sentence-level corpus via word alignment. Its main advantage is that we can make full use of the original training corpus to induce the sequence labelers and then apply them to the test corpus.

关键词： Causal relationship extraction biological expression language Hierarchical sequence labeling Word alignment

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Using Drugs as Molecular Probes: A Computational Chemical Biology Approach in Neurodegenerative Diseases

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JOURNAL OF ALZHEIMERS DISEASE 2017年第2期56卷 677-686页

作者： Emon, Mohammad Asif Emran Khan Kodamullil, Alpha Tom Karki, Reagon Younesi, Erfan Hofmann-Apitius, Martin Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53754 St Augustin Germany Univ Bonn Bonn Aachen Int Ctr IT Bonn Germany

Neurodegenerative diseases including Alzheimer's disease are complex to tackle because of the complexity of the brain, both in structure and function. Such complexity is reflected by the involvement of various brain regions and multiple pathways in the etiology of neurodegenerative diseases that render single drug target approaches ineffective. Particularly in the area of neurodegeneration, attention has been drawn to repurposing existing drugs with proven efficacy and safety profiles. However, there is a lack of systematic analysis of the brain chemical space to predict the feasibility of repurposing strategies. Using a mechanism-based, drug-target interaction modeling approach, we have identified promising drug candidates for repositioning. Mechanistic cause-and-effect models consolidate relevant prior knowledge on drugs, targets, and pathways from the scientific literature and integrate insights derived from experimental data. We demonstrate the power of this approach by predicting two repositioning candidates for Alzheimer's disease and one for amyotrophic lateral sclerosis.

关键词： Alzheimer disease amyotrophic lateral sclerosis biological expression language disease-drug modeling drug repositioning neurodegenerative diseases

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Novel approaches to develop community-built biological network models for potential drug discovery

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EXPERT OPINION ON DRUG DISCOVERY 2017年第8期12卷 849-857页

作者： Talikka, Marja Bukharov, Natalia Hayes, William S. Hofmann-Apitius, Martin Alexopoulos, Leonidas Peitsch, Manuel C. Hoeng, Julia Philip Morris Prod SA Philip Morris Int R&D Neuchatel Switzerland Clarivate Analyt Translat Data Management Serv Boston MA USA Appl Dynam Solut LLC Data Sci Rahway NJ USA Fraunhofer Inst Algorithms & Sci Comp Dept Bioinformat Schloss Birlinghoven Sankt Augustin Germany Natl Tech Univ Athens Syst Bioengn Lab Zografos Greece Protavio Ltd Stevenage Herts England

Introduction: Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.

关键词： Mechanistic pathway network model crowd sourcing ontology biological expression language

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Using a Crowdsourcing Approach for Network Verification

Using a Crowdsourcing Approach for Network Verification

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作者： Natalia Boukharov Jennifer S. Park

biological network models have many applications in systems toxicology and in biomarker and target identification for drug discovery. A critical challenge is to keep the models relevant and representative of current scientific knowledge. Harnessing community intelligence in knowledge curation holds great promise in dealing with the flood of biological information. Another important challenge is to be able to share and communicate networks with other scientists to aid in furthering similar scientific goals. To try to address both of these challenges, we designed a crowd verification platform to verify a comprehensive set of networks in a standardized biological expression language. This platform can increase the value of the network models, and promote their use in research applications such as drug discovery, personalized medicine, and toxicological risk assessment. In addition, we have provided practical recommendations for designing intuitive tools for network curation, identifying and recruiting the “target crowd,” and using various approaches to engage and motivate participants.

关键词： Molecular pathways Crowdsourcing Community curation Crowd verification biological network models Reputation system biological expression language Knowledge curation

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Causal biological Network Database: A Comprehensive Platform of Causal biological Network Models Focused on the Pulmonary and Vascular Systems

Causal Biological Network Database: A Comprehensive Platform...

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作者： Marja Talikka Stephanie Boue Walter K. Schlage

While the ever-increasing amounts of scientific data provide a more detailed description of toxic effects, it is not trivial to extract information that will contribute to a better biological understanding. Sophisticated computational methods have been developed to separate mathematically the biological signal from the noise in high-throughput datasets; however, visualizing and putting a signal into a relevant biological context using

关键词： Database biological expression language Causal biological network models Computational toxicology

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Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer's disease

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GENOME MEDICINE 2014年第11期6卷 97-97页

作者： Malhotra, Ashutosh Younesi, Erfan Bagewadi, Shweta Hofmann-Apitius, Martin Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53754 Schloss Birlinghoven Sankt Augustin Germany Rhein Freidrich Wilhelms Univ Bonn Bonn Aachen Int Ctr Informat Technol D-53113 Bonn Germany

Background: A number of compelling candidate Alzheimer's biomarkers remain buried within the literature. Indeed, there should be a systematic effort towards gathering this information through approaches that mine publicly available data and substantiate supporting evidence through disease modeling methods. In the presented work, we demonstrate that an integrative gray zone mining approach can be used as a way to tackle this challenge successfully. Methods: The methodology presented in this work combines semantic information retrieval and experimental data through context-specific modeling of molecular interactions underlying stages in Alzheimer's disease ( AD). Information about putative, highly speculative AD biomarkers was harvested from the literature using a semantic framework and was put into a functional context through disease- and stage-specific models. Staging models of AD were further validated for their functional relevance and novel biomarker candidates were predicted at the mechanistic level. Results: Three interaction models were built representing three stages of AD, namely mild, moderate, and severe stages. Integrated analysis of these models using various arrays of evidence gathered from experimental data and published knowledge resources led to identification of four candidate biomarkers in the mild stage. Mode of action of these candidates was further reasoned in the mechanistic context of models by chains of arguments. Accordingly, we propose that some of these 'emerging' potential biomarker candidates have a reasonable mechanistic explanation and deserve to be investigated in more detail. Conclusions: Systematic exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches. Integrative analysis of this knowledge in the context of disease mechanism is a promising approach towards identification of candidate biomarkers taking into consideration the complex etiology of disease. The added value

关键词： Name Entity Recognition Functional Context Knockout Mouse Phenotype biological expression language GSE1297 Experiment

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On Crowd-verification of biological Networks

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BIOINFORMATICS AND BIOLOGY INSIGHTS 2013年第7期7卷 307-325页

作者： Ansari, Sam Binder, Jean Boue, Stephanie Di Fabio, Anselmo Hayes, William Hoeng, Julia Iskandar, Anita Kleiman, Robin Norel, Raquel O'Neel, Bruce Peitsch, Manuel C. Poussin, Carine Pratt, Dexter Rhrissorrakrai, Kahn Schlage, Walter K. Stolovitzky, Gustavo Talikka, Marja Phillip Morris Prod SA Res & Dev Neuchatel Switzerland IBM Computat Biol Ctr Yorktown Hts NY USA Univ Calif San Diego Sch Med Dept Med La Jolla CA 92093 USA Dept Bioengn La Jolla CA 92093 USA Selventa Cambridge MA USA Appl Dynam Solut LLC Knoxville NJ USA

biological networks with a structured syntax are a powerful way of representing biological information generated from high density data;however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using biological expression language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peerreviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.

关键词： community curation biological network models reputation system biological expression language

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