FLUTE: Fast and reliable knowledge retrieval from biomedical literature

Holtzapple, Emilee; Telmer, Cheryl A.; Miskov-Zivanov, Natasa

doi:10.1093/database/baaa056

Cited by 17 publications

(18 citation statements)

References 33 publications

(39 reference statements)

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“…The Filter for Understanding True Events (FLUTE) [27] tool utilizes existing interaction database resources to find support for machine-read interactions. The FLUTE database stores information from five interaction databases, including protein-protein interactions (PPIs), protein-chemical interactions (PCIs), and protein biological process interactions (PBPIs).…”

Section: Methodsmentioning

confidence: 99%

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Automated verification, assembly, and extension of GBM stem cell network model with knowledge from literature and data

Holtzapple¹,

Cochran

Miskov-Zivanov³

2021

Preprint

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Signaling network models are usually assembled from information in literature and expert knowledge or inferred from data. The goal of modeling is to gain mechanistic understanding of key signaling pathways and provide predictions on how perturbations affect large-scale processes such as disease progression. For glioblastoma multiforme (GBM), this task is critical, given the lack of effective treatments and pace of disease progression. Both manual and automated assembly of signaling networks from data or literature have drawbacks. Existing GBM networks, as well as networks assembled using state-of-the-art machine reading, fall short when judged by the quality and quantity of information, as well as certain attributes of the overall network structure. The contributions of this work are two-fold. First, we propose an automated methodology for verification of signaling networks. Next, we discuss automation of network assembly and extension that relies on methods and resources used for network verification, thus, implicitly including verification in these processes. In addition to these methods, we also present, and verify a comprehensive GBM network assembled with a hybrid of manual and automated methods. Finally, we demonstrate that, while an automated network assembly is fast, such networks still lack precision and realistic network topology.

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Section: Methodsmentioning

confidence: 99%

“…Once all network nodes are grounded, we use three online resources, the INDRA database [5], PCnet [20], and FLUTE [27], to verify the network edges. INDRA is a system that draws on natural language processing tools and structured databases to collect statements about mechanistic and causal entity interactions.…”

Section: Methodsmentioning

confidence: 99%

Automated verification, assembly, and extension of GBM stem cell network model with knowledge from literature and data

Holtzapple¹,

Cochran

Miskov-Zivanov³

2021

Preprint

Self Cite

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“…However, the dynamic behavior of AKT (also in the search query), IL2 and STAT5 was not recovered in one out of three scenarios, (high TCR scenario, properties 𝓉 19 , 𝓉 22 and 𝓉 24 ). This is due to potentially erroneous interactions in the CE set extracted by machine readers, e.g., CD8 → AKT, proliferation → AKT, differentiation -| AKT, differentiation -| IL2 and differentiation -| STAT5 ("→" represents positive regulation, "-|" represents negative regulation, also used in Figure As mentioned above, we plan to add pre-processing of CE sets (e.g., using interaction filtering [44]).…”

Section: Assistance In Query Answeringmentioning

confidence: 99%

Context-aware knowledge selection and reliable model recommendation with ACCORDION

Ahmed

Telmer

Miskov-Zivanov

2022

Preprint

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Querying new information from knowledge sources, in general, and published literature, in particular, aims to provide precise and quick answers to questions raised about a system under study. In this paper, we present ACCORDION (ACCelerating and Optimizing model RecommenDatIONs), a novel methodology and a tool to enable efficient addressing of biological questions, by automatically recommending models that recapitulate desired dynamic behavior. Our approach integrates information extraction from literature, clustering, simulation and formal analysis to allow for automated, consistent, and robust assembling, testing and selection of context-specific models. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. ACCORDION is comprehensive as it can capture all relevant knowledge from literature, obtained by automated literature search and machine reading. At the same time, as our results show, ACCORDION is selective, recommending only the most relevant and useful subset (15-20%) of candidate model extensions found in literature, while guided by baseline model context and goal properties. ACCORDION is very effective, also demonstrated by our results, as it can reduce the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools. In this process, ACCORDION can also suggest more than one highly scored model, thus providing alternative solutions to user questions and novel insights for treatment directions.

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“…For this reason, automated methods for information extraction, such as machine reading, are used to retrieve information about intracellular signaling networks, and this information can then be used for model assembly or extension. While automated methods accelerate model assembly, the time required for processing all selected papers still depends on the number and the type of papers chosen for machine reading (Holtzapple, Telmer, & Miskov-Zivanov, 2020).…”

Section: Introductionmentioning

confidence: 99%

Context-aware query design combines knowledge and data for efficient reading and reasoning

Holtzapple¹,

Cochran²,

Miskov-Zivanov³

2021

Proceedings of the 20th Workshop on Biomedical Language Processing

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The amount of biomedical literature has vastly increased over the past few decades. As a result, the sheer quantity of accessible information is overwhelming, and complicates manual information retrieval. Automated methods seek to speed up information retrieval from biomedical literature. However, such automated methods are still too time-intensive to survey all existing biomedical literature. We present a methodology for automatically generating literature queries that select relevant papers based on biological data. By using differentially expressed genes to inform our literature searches, we focus information extraction on mechanistic signaling details that are crucial for the disease or context of interest.

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FLUTE: Fast and reliable knowledge retrieval from biomedical literature

Cited by 17 publications

References 33 publications

Automated verification, assembly, and extension of GBM stem cell network model with knowledge from literature and data

Automated verification, assembly, and extension of GBM stem cell network model with knowledge from literature and data

Context-aware knowledge selection and reliable model recommendation with ACCORDION

Context-aware query design combines knowledge and data for efficient reading and reasoning

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