Supporting secondary research in early drug discovery process through a Natural Language Processing based system

Abstract Last decades were characterised by a constant decline in the productivity of research and development activities of pharmaceutical companies. This is due to the fact that the drug discovery process contains an intrinsic risk that should be managed efficiently. Within this process, the early phase projects could be streamlined by doing more secondary research. These activities would involve the integration of chemical and biological knowledge from scientific literature in order to extract an overview and the evolution of a certain research area. This would then help refine the research and development operations. Considering the vast amount of pharmaceutical studies publications, it is not easy to identify the important information. For this task, a series of projects leveraged the advantages of the open pharmacological space through state-of-the-art technologies. The most popular are Knowledge Graphs methods. Although extremely useful, this technology requires increased investments of time and human resources. An alternative would be to develop a system that uses Natural Language Processing blocks. Still, there is no defined framework and reusable code template for the use-case of compounds development. In this study, it is presented the design and development of a system that uses Dynamic Topic Modelling and Named Entity Recognition modules in order to extract meaningful information from a large volume of unstructured texts. Moreover, the dynamic character of the topic modelling technique allows to analyse the evolution of different subject areas over time. In order to validate the system, a collection of articles from the Pharmaceutical Research Journal was used. Our results show that the system is able to identify the main research areas in the last 20 years, namely crystalline and amorphous systems, insulin resistance, paracellular permeability. Additionally, the evolution of the subjects is a highly valuable resource and should be used to get an in-depth understanding about the shifts that happened in a specific domain. However, a limitation of this system is that it cannot detect association between two concepts or entities if they are not involved in the same document.

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Report on the 4th Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries at SIGIR 2019

ACM SIGIR Forum ◽

10.1145/3458553.3458554 ◽

2019 ◽

Vol 53 (2) ◽

pp. 3-10

Author(s):

Muthu Kumar Chandrasekaran ◽

Philipp Mayr

Keyword(s):

Information Retrieval ◽

Natural Language Processing ◽

Natural Language ◽

Research And Development ◽

Language Processing ◽

Digital Libraries ◽

State Of The Art ◽

Shared Task ◽

Processing Information ◽

Joint Workshop

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

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Obtaining Knowledge in Pathology Reports Through a Natural Language Processing Approach With Classification, Named-Entity Recognition, and Relation-Extraction Heuristics

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00008 ◽

2019 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Tomasz Oliwa ◽

Steven B. Maron ◽

Leah M. Chase ◽

Samantha Lomnicki ◽

Daniel V.T. Catenacci ◽

...

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Named Entity Recognition ◽

Entity Recognition ◽

Classification Model ◽

Supervised Machine Learning ◽

Named Entity ◽

Pathology Reports

PURPOSE Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology notes, which complicate data abstraction. Our motivation is to develop a natural language processing method that dynamically identifies existing pathology specimen elements necessary for locating specimens for future use in a manner that can be re-implemented by other institutions. PATIENTS AND METHODS Pathology reports from patients with gastroesophageal cancer enrolled in The University of Chicago GI oncology tumor bank were used to train and validate a novel composite natural language processing-based pipeline with a supervised machine learning classification step to separate notes into internal (primary review) and external (consultation) reports; a named-entity recognition step to obtain label (accession number), location, date, and sublabels (block identifiers); and a results proofreading step. RESULTS We analyzed 188 pathology reports, including 82 internal reports and 106 external consult reports, and successfully extracted named entities grouped as sample information (label, date, location). Our approach identified up to 24 additional unique samples in external consult notes that could have been overlooked. Our classification model obtained 100% accuracy on the basis of 10-fold cross-validation. Precision, recall, and F1 for class-specific named-entity recognition models show strong performance. CONCLUSION Through a combination of natural language processing and machine learning, we devised a re-implementable and automated approach that can accurately extract specimen attributes from semistructured pathology notes to dynamically populate a tumor registry.

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Probing Patient Messages Enhanced by Natural Language Processing: A Top-Down Message Corpus Analysis

Health Data Science ◽

10.34133/2021/1504854 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

George Mastorakos ◽

Aditya Khurana ◽

Ming Huang ◽

Sunyang Fu ◽

Ahmad P. Tafti ◽

...

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Named Entity Recognition ◽

Corpus Analysis ◽

Entity Recognition ◽

Message Content ◽

Named Entity ◽

Medical Concepts ◽

Insight Into

Background. Patients increasingly use asynchronous communication platforms to converse with care teams. Natural language processing (NLP) to classify content and automate triage of these messages has great potential to enhance clinical efficiency. We characterize the contents of a corpus of portal messages generated by patients using NLP methods. We aim to demonstrate descriptive analyses of patient text that can contribute to the development of future sophisticated NLP applications. Methods. We collected approximately 3,000 portal messages from the cardiology, dermatology, and gastroenterology departments at Mayo Clinic. After labeling these messages as either Active Symptom, Logistical, Prescription, or Update, we used NER (named entity recognition) to identify medical concepts based on the UMLS library. We hierarchically analyzed the distribution of these messages in terms of departments, message types, medical concepts, and keywords therewithin. Results. Active Symptom and Logistical content types comprised approximately 67% of the message cohort. The “Findings” medical concept had the largest number of keywords across all groupings of content types and departments. “Anatomical Sites” and “Disorders” keywords were more prevalent in Active Symptom messages, while “Drugs” keywords were most prevalent in Prescription messages. Logistical messages tended to have the lower proportions of “Anatomical Sites,”, “Disorders,”, “Drugs,”, and “Findings” keywords when compared to other message content types. Conclusions. This descriptive corpus analysis sheds light on the content and foci of portal messages. The insight into the content and differences among message themes can inform the development of more robust NLP models.

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EventEpi–A Natural Language Processing Framework for Event-Based Surveillance

10.1101/19006395 ◽

2019 ◽

Author(s):

Auss Abbood ◽

Alexander Ullrich ◽

Rüdiger Busche ◽

Stéphane Ghozzi

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Web Application ◽

Fine Tuning ◽

Entity Recognition ◽

World Health ◽

Support Vector ◽

Event Based ◽

Processing Framework

AbstractAccording to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of epidemiologists sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Reading the articles, discussing their relevance, and putting key information into a database is a time-consuming process. To support EBS, but also to gain insights into what makes an article and the event it describes relevant, we developed a natural-language-processing framework for automated information extraction and relevance scoring. First, we scraped relevant sources for EBS as done at RKI (WHO Disease Outbreak News and ProMED) and automatically extracted the articles’ key data: disease, country, date, and confirmed-case count. For this, we performed named entity recognition in two steps: EpiTator, an open-source epidemiological annotation tool, suggested many different possibilities for each. We trained a naive Bayes classifier to find the single most likely one using RKI’s EBS database as labels. Then, for relevance scoring, we defined two classes to which any article might belong: The article is relevant if it is in the EBS database and irrelevant otherwise. We compared the performance of different classifiers, using document and word embeddings. Two of the tested algorithms stood out: The multilayer perceptron performed best overall, with a precision of 0.19, recall of 0.50, specificity of 0.89, F1 of 0.28, and the highest tested index balanced accuracy of 0.46. The support-vector machine, on the other hand, had the highest recall (0.88) which can be of higher interest for epidemiologists. Finally, we integrated these functionalities into a web application called EventEpi where relevant sources are automatically analyzed and put into a database. The user can also provide any URL or text, that will be analyzed in the same way and added to the database. Each of these steps could be improved, in particular with larger labeled datasets and fine-tuning of the learning algorithms. The overall framework, however, works already well and can be used in production, promising improvements in EBS. The source code is publicly available at https://github.com/aauss/EventEpi.

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Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections

Research Ideas and Outcomes ◽

10.3897/rio.6.e55789 ◽

2020 ◽

Vol 6 ◽

Cited By ~ 3

Author(s):

David Owen ◽

Laurence Livermore ◽

Quentin Groom ◽

Alex Hardisty ◽

Thijs Leegwater ◽

...

Keyword(s):

Deep Learning ◽

Natural Language Processing ◽

Natural History ◽

Natural Language ◽

Language Processing ◽

Scientific Workflow ◽

Entity Recognition ◽

Research Activities ◽

Handwritten Text ◽

Segmented Images

We describe an effective approach to automated text digitisation with respect to natural history specimen labels. These labels contain much useful data about the specimen including its collector, country of origin, and collection date. Our approach to automatically extracting these data takes the form of a pipeline. Recommendations are made for the pipeline's component parts based on some of the state-of-the-art technologies. Optical Character Recognition (OCR) can be used to digitise text on images of specimens. However, recognising text quickly and accurately from these images can be a challenge for OCR. We show that OCR performance can be improved by prior segmentation of specimen images into their component parts. This ensures that only text-bearing labels are submitted for OCR processing as opposed to whole specimen images, which inevitably contain non-textual information that may lead to false positive readings. In our testing Tesseract OCR version 4.0.0 offers promising text recognition accuracy with segmented images. Not all the text on specimen labels is printed. Handwritten text varies much more and does not conform to standard shapes and sizes of individual characters, which poses an additional challenge for OCR. Recently, deep learning has allowed for significant advances in this area. Google's Cloud Vision, which is based on deep learning, is trained on large-scale datasets, and is shown to be quite adept at this task. This may take us some way towards negating the need for humans to routinely transcribe handwritten text. Determining the countries and collectors of specimens has been the goal of previous automated text digitisation research activities. Our approach also focuses on these two pieces of information. An area of Natural Language Processing (NLP) known as Named Entity Recognition (NER) has matured enough to semi-automate this task. Our experiments demonstrated that existing approaches can accurately recognise location and person names within the text extracted from segmented images via Tesseract version 4.0.0. Potentially, NER could be used in conjunction with other online services, such as those of the Biodiversity Heritage Library to map the named entities to entities in the biodiversity literature (https://www.biodiversitylibrary.org/docs/api3.html). We have highlighted the main recommendations for potential pipeline components. The document also provides guidance on selecting appropriate software solutions. These include automatic language identification, terminology extraction, and integrating all pipeline components into a scientific workflow to automate the overall digitisation process.

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Advances in Computational Linguistics and Text Processing Frameworks

Advances in Computer and Electrical Engineering - Handbook of Research on Engineering Innovations and Technology Management in Organizations ◽

10.4018/978-1-7998-2772-6.ch012 ◽

2020 ◽

pp. 217-244

Author(s):

Ayush Srivastav ◽

Hera Khan ◽

Amit Kumar Mishra

Keyword(s):

Neural Networks ◽

Natural Language Processing ◽

Natural Language ◽

Computational Linguistics ◽

Language Processing ◽

Text Processing ◽

Named Entity Recognition ◽

Entity Recognition ◽

Named Entity ◽

Part Of Speech

The chapter provides an eloquent account of the major methodologies and advances in the field of Natural Language Processing. The most popular models that have been used over time for the task of Natural Language Processing have been discussed along with their applications in their specific tasks. The chapter begins with the fundamental concepts of regex and tokenization. It provides an insight to text preprocessing and its methodologies such as Stemming and Lemmatization, Stop Word Removal, followed by Part-of-Speech tagging and Named Entity Recognition. Further, this chapter elaborates the concept of Word Embedding, its various types, and some common frameworks such as word2vec, GloVe, and fastText. A brief description of classification algorithms used in Natural Language Processing is provided next, followed by Neural Networks and its advanced forms such as Recursive Neural Networks and Seq2seq models that are used in Computational Linguistics. A brief description of chatbots and Memory Networks concludes the chapter.

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CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocx132 ◽

2017 ◽

Vol 25 (3) ◽

pp. 331-336 ◽

Cited By ~ 64

Author(s):

Ergin Soysal ◽

Jingqi Wang ◽

Min Jiang ◽

Yonghui Wu ◽

Serguei Pakhomov ◽

...

Keyword(s):

Natural Language Processing ◽

User Interface ◽

Natural Language ◽

Language Processing ◽

High Performance ◽

Smoking Status ◽

Entity Recognition ◽

Graphic User Interface ◽

Clinical Text ◽

Clinical Natural Language Processing

Abstract Existing general clinical natural language processing (NLP) systems such as MetaMap and Clinical Text Analysis and Knowledge Extraction System have been successfully applied to information extraction from clinical text. However, end users often have to customize existing systems for their individual tasks, which can require substantial NLP skills. Here we present CLAMP (Clinical Language Annotation, Modeling, and Processing), a newly developed clinical NLP toolkit that provides not only state-of-the-art NLP components, but also a user-friendly graphic user interface that can help users quickly build customized NLP pipelines for their individual applications. Our evaluation shows that the CLAMP default pipeline achieved good performance on named entity recognition and concept encoding. We also demonstrate the efficiency of the CLAMP graphic user interface in building customized, high-performance NLP pipelines with 2 use cases, extracting smoking status and lab test values. CLAMP is publicly available for research use, and we believe it is a unique asset for the clinical NLP community.

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Taking chance out of drug discovery

The Biochemist ◽

10.1042/bio03106040 ◽

2009 ◽

Vol 31 (6) ◽

pp. 40-42

Author(s):

Simon Pearce

Keyword(s):

Drug Discovery ◽

Research And Development ◽

Phenyl Ring ◽

Reactive Intermediates ◽

Building Blocks ◽

Bioactive Molecules ◽

Discovery Process ◽

Drug Discovery Process ◽

Drug Discovery And Development ◽

Compound Screening

Searching for bioactive molecules, using rapid compound screening, fragment-based design and unique building blocks, should not be like looking for a needle in a haystack. This article describes a range of innovative and diverse screening compounds for drug discovery and development. Available at both research and development scales, the line includes special products associated with new heterocyclic and phenyl ring-based chemical building blocks, including an exclusive and expanding range of reactive intermediates specifically designed for lead optimization, as well as a growing fragment collection. I explain how these products and services are helping to accelerate the search for bioactive molecules and are shortening the drug discovery process by reducing the element of chance.

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ENCADEAr: ENCADEAmento automático de notícias

Oslo Studies in Language ◽

10.5617/osla.1457 ◽

2015 ◽

Vol 7 (1) ◽

Author(s):

Carla Abreu ◽

Jorge Teixeira ◽

Eugénio Oliveira

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Information Extraction ◽

Supervised Learning ◽

Language Processing ◽

Name Entity Recognition ◽

Entity Recognition ◽

Name Entity ◽

Supervised Learning Algorithms ◽

Processing Information

This work aims at defining and evaluating different techniques to automatically build temporal news sequences. The approach proposed is composed by three steps: (i) near duplicate documents detention; (ii) keywords extraction; (iii) news sequences creation. This approach is based on: Natural Language Processing, Information Extraction, Name Entity Recognition and supervised learning algorithms. The proposed methodology got a precision of 93.1% for news chains sequences creation.

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A Novel Metric to Quantify the Effect of Pathway Enrichment Evaluation With Respect to Biomedical Text-Mined Terms: Development and Feasibility Study (Preprint)

10.2196/preprints.28247 ◽

2021 ◽

Author(s):

Xuan Qin ◽

Xinzhi Yao ◽

Jingbo Xia

Keyword(s):

Natural Language Processing ◽

Text Mining ◽

Drug Use ◽

Natural Language ◽

Language Processing ◽

Entity Recognition ◽

Biomedical Text ◽

Biomedical Text Mining ◽

Related Gene ◽

Pathway Enrichment

BACKGROUND Natural language processing has long been applied in various applications for biomedical knowledge inference and discovery. Enrichment analysis based on named entity recognition is a classic application for inferring enriched associations in terms of specific biomedical entities such as gene, chemical, and mutation. OBJECTIVE The aim of this study was to investigate the effect of pathway enrichment evaluation with respect to biomedical text-mining results and to develop a novel metric to quantify the effect. METHODS Four biomedical text mining methods were selected to represent natural language processing methods on drug-related gene mining. Subsequently, a pathway enrichment experiment was performed by using the mined genes, and a series of inverse pathway frequency (IPF) metrics was proposed accordingly to evaluate the effect of pathway enrichment. Thereafter, 7 IPF metrics and traditional <i>P</i> value metrics were compared in simulation experiments to test the robustness of the proposed metrics. RESULTS IPF metrics were evaluated in a case study of rapamycin-related gene set. By applying the best IPF metrics in a pathway enrichment simulation test, a novel discovery of drug efficacy of rapamycin for breast cancer was replicated from the data chosen prior to the year 2000. Our findings show the effectiveness of the best IPF metric in support of knowledge discovery in new drug use. Further, the mechanism underlying the drug-disease association was visualized by Cytoscape. CONCLUSIONS The results of this study suggest the effectiveness of the proposed IPF metrics in pathway enrichment evaluation as well as its application in drug use discovery.

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