Towards a Protein–Protein Interaction information extraction system: Recognizing named entities

Dánger, Roxana; Pla, Ferrán; Molina, Antonio; Rosso, Paolo

doi:10.1016/j.knosys.2013.12.010

Cited by 21 publications

(7 citation statements)

References 51 publications

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“…In order to understand mentions of genes according to their context and map them to standardized identifiers, both rule-based [23] and machine-learning-based [24] approaches were integrated [25] for gene and protein extraction and normalization. To ignore mentions of generic protein families, such as “Histone”, and instead focus on mentions of specific genes and proteins such as “Histone H3” can be very challenging.…”

Section: Methodsmentioning

confidence: 99%

Identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson

Hatz

Spangler

Bender³

et al. 2019

PLoS ONE

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Background Pharmacodynamic biomarkers are becoming increasingly valuable for assessing drug activity and target modulation in clinical trials. However, identifying quality biomarkers is challenging due to the increasing volume and heterogeneity of relevant data describing the biological networks that underlie disease mechanisms. A biological pathway network typically includes entities (e.g. genes, proteins and chemicals/drugs) as well as the relationships between these and is typically curated or mined from structured databases and textual co-occurrence data. We propose a hybrid Natural Language Processing and directed relationships-based network analysis approach using IBM Watson for Drug Discovery to rank all human genes and identify potential candidate biomarkers, requiring only an initial determination of a specific target-disease relationship. Methods Through natural language processing of scientific literature, Watson for Drug Discovery creates a network of semantic relationships between biological concepts such as genes, drugs, and diseases. Using Bruton’s tyrosine kinase as a case study, Watson for Drug Discovery’s automatically extracted relationship network was compared with a prominent manually curated physical interaction network. Additionally, potential biomarkers for Bruton’s tyrosine kinase inhibition were predicted using a matrix factorization approach and subsequently compared with expert-generated biomarkers. Results Watson’s natural language processing generated a relationship network matching 55 (86%) genes upstream of BTK and 98 (95%) genes downstream of Bruton’s tyrosine kinase in a prominent manually curated physical interaction network. Matrix factorization analysis predicted 11 of 13 genes identified by Merck subject matter experts in the top 20% of Watson for Drug Discovery’s 13,595 ranked genes, with 7 in the top 5%. Conclusion Taken together, these results suggest that Watson for Drug Discovery’s automatic relationship network identifies the majority of upstream and downstream genes in biological pathway networks and can be used to help with the identification and prioritization of pharmacodynamic biomarker evaluation, accelerating the early phases of disease hypothesis generation.

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Section: Methodsmentioning

confidence: 99%

Identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson

Hatz

Spangler

Bender³

et al. 2019

PLoS ONE

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“…Even if expert reviews, a massive medical record will be very long and tiring. Artificial intelligence has become a solution for processing patient data more quickly Biomedical and chemical [14,41,[204][205][206][207] Network and security [57,[208][209][210][211][212][213] Biology [59,[214][215][216][217][218][219][220] Chemistry [58,74,[221][222][223][224] Geoscience [47,48,225] Business and economics [40,81,226] History and culture [316-320] Agriculture [99,[321][322][323] Law [83,227] Social media [108,109] Automotive and engineering [98,[325][326][327][328] Military [104] Neuroscience [228] Sport science…”

Section: Ner Research Application Domainmentioning

confidence: 99%

Systematic Literature Review on Named Entity Recognition: Approach, Method, and Application

Warto,

Rustad,

Shidik

et al. 2024

Stat., optim. inf. comput.

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Named entity recognition (NER) is one of the preprocessing stages in natural language processing (NLP), which functions to detect and classify entities in the corpus. NER results are used in various NLP applications, including sentiment analysis, text summarization, chatbot, machine translation, and question answering. Several previous reviews partially discussed NER, for instance, NER reviews in specific domains, NER classification, and NER deep learning. This paper provides a comprehensive and systematic review on NER topic studies published from 2011 to 2020. The main contribution of this review is to present a comprehensive systematic literature review on NER from preprocessing techniques, datasets, application domains, feature extraction techniques, approaches, methods, and evaluation techniques. The result concludes that the deep learning approach and the Bi-directional long short-term memory with a conditional random field (Bi-LSTM-CRF) method are the most interesting methods among NER researchers. At the same time, medical and health are NER researchers' most popular domains. These developments have also led to an increasing number of public datasets in the medical and health fields. At the end of this review, we recommend some opportunities and challenges for NER research going forward.

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“…Recent work has also integrated simple rules with machine learning models. For example, Danger et al, integrate a dictionary look-up and a conditional-random-field classifier for recognizing pertinent entity types in protein-protein interaction contexts [10].…”

Section: Entity Identification and Normalizationmentioning

confidence: 99%

Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature

Muthialu

Wilkins

Bachman

et al. 2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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We present KnIT, the Knowledge Integration Toolkit, a system for accelerating scientific discovery and predicting previously unknown protein-protein interactions. Such predictions enrich biological research and are pertinent to drug discovery and the understanding of disease. Unlike a prior study, KnIT is now fully automated and demonstrably scalable. It extracts information from the scientific literature, automatically identifying direct and indirect references to protein interactions, which is knowledge that can be represented in network form. It then reasons over this network with techniques such as matrix factorization and graph diffusion to predict new, previously unknown interactions. The accuracy and scope of KnIT's knowledge extractions are validated using comparisons to structured, manually curated data sources as well as by performing retrospective studies that predict subsequent literature discoveries using literature available prior to a given date. The KnIT methodology is a step towards automated hypothesis generation from text, with potential application to other scientific domains.

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Towards a Protein–Protein Interaction information extraction system: Recognizing named entities

Cited by 21 publications

References 51 publications

Identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson

Identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson

Systematic Literature Review on Named Entity Recognition: Approach, Method, and Application

Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature

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