BioCause: Annotating and analysing causality in the biomedical domain

Whole genome-based reclassification of several species of the genus Microbispora

The classification of Microbispora, a bacterial genus of significant ecological, agricultural, biotechnological, and clinical importance, has traditionally been carried out based on 16S rRNA gene sequences or phenotypic characteristics, which may lead to equivocal conclusions and it is not in line with the current standards. Moreover, some of the recent species descriptions have not been made using whole genome sequences (WGS), or when used, not all the species were included in the analyses. Consequently, some of the taxonomic conclusions drawn are equivocal, and therefore some currently accepted species should be synonymized. In this study, we revised the taxonomy of the genus Microbispora using digital DNA-DNA hybridization (dDDH) and average nucleotide identity (ANI) values, and by reconstructing phylogenetic relationships using whole genome sequences. Based on the clear phylogenomic separations and on the sequence divergence values, we propose to elevate Microbispora rosea subsp. rosea and Microbispora rosea subsp. aerata to the species level, and therefore to create Microbispora aerata sp. nov. with JCM 3076T (= DSM 43176T = ATCC 15448T = IFO 14624T = NBRC 14624T = VKM Ac-1507T) as the type strain. Hence, with this proposition, the correct name for Microbispora rosea subsp. rosea is M. rosea. Furthermore, we propose to reclassify M. camponoti as a subspecies within M. bryophytorum. Consequently, we propose the creation of the following two subspecies: Microbispora bryophytorum subsp. bryophytorum subsp. nov. with DSM 46710T (= CGMCC 4.7138T = NEAU TX2-2T) as the type strain, and Microbispora bryophytorum subsp. camponoti subsp. nov., comb. nov. with DSM 2C-HV3T (= DSM 100527T = CGMCC 4.7281T) as the type strain. In addition, we propose to reinstate M. amethystogenes as an independent species and not as a M. rosea synonym, and reclassify "M. cellulosiformans" as a subspecies within M. amethystogenes. Hence, we propose the creation of the following two subspecies: Microbispora amethystogenes subsp. amethystogenes subsp. nov. with NBRC 101907T (= DSM 43164T = JCM 3021T = NRRL B-2637T) as the type strain, and Microbispora amethystogenes subsp. cellulosiformans subsp. nov., comb. nov. with Gxj-6T (= DSM 109712T = CGMCC 4.7605T) as the type strain. Lastly, we propose M. fusca NEAU-HEGS1-5T and "M. tritici" MT50T as later homotypic synonyms of M. triticiradicis NEAU-HRDPA2-9T.

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019-2022 was 80 times that of 2007-2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

MedTAG: a portable and customizable annotation tool for biomedical documents

BACKGROUND

Semantic annotators and Natural Language Processing (NLP) methods for Named Entity Recognition and Linking (NER+L) require plenty of training and test data, especially in the biomedical domain. Despite the abundance of unstructured biomedical data, the lack of richly annotated biomedical datasets poses hindrances to the further development of NER+L algorithms for any effective secondary use. In addition, manual annotation of biomedical documents performed by physicians and experts is a costly and time-consuming task. To support, organize and speed up the annotation process, we introduce MedTAG, a collaborative biomedical annotation tool that is open-source, platform-independent, and free to use/distribute.

RESULTS

We present the main features of MedTAG and how it has been employed in the histopathology domain by physicians and experts to annotate more than seven thousand clinical reports manually. We compare MedTAG with a set of well-established biomedical annotation tools, including BioQRator, ezTag, MyMiner, and tagtog, comparing their pros and cons with those of MedTag. We highlight that MedTAG is one of the very few open-source tools provided with an open license and a straightforward installation procedure supporting cross-platform use.

CONCLUSIONS

MedTAG has been designed according to five requirements (i.e. available, distributable, installable, workable and schematic) defined in a recent extensive review of manual annotation tools. Moreover, MedTAG satisfies 20 over 22 criteria specified in the same study.

Causal relationship extraction from biomedical text using deep neural models: A comprehensive survey

The identification of causal relationships between events or entities within biomedical texts is of great importance for creating scientific knowledge bases and is also a fundamental natural language processing (NLP) task. A causal (cause-effect) relation is defined as an association between two events in which the first must occur before the second. Although this task is an open problem in artificial intelligence, and despite its important role in information extraction from the biomedical literature, very few works have considered this problem. However, with the advent of new techniques in machine learning, especially deep neural networks, research increasingly addresses this problem. This paper summarizes state-of-the-art research, its applications, existing datasets, and remaining challenges. For this survey we have implemented and evaluated various techniques including a Multiview CNN (MVC), attention-based BiLSTM models and state-of-the-art word embedding models, such as those obtained with bidirectional encoder representations (ELMo) and transformer architectures (BioBERT). In addition, we have evaluated a graph LSTM as well as a baseline rule based system. We have investigated the class imbalance problem as an innate property of annotated data in this type of task. The results show that a considerable improvement of the results of state-of-the-art systems can be achieved when a simple random oversampling technique for data augmentation is used in order to reduce class imbalance.

Identifying Cases of Shoulder Injury Related to Vaccine Administration (SIRVA) in the United States: Development and Validation of a Natural Language Processing Method (Preprint)

Background

Shoulder injury related to vaccine administration (SIRVA) accounts for more than half of all claims received by the National Vaccine Injury Compensation Program. However, due to the difficulty of finding SIRVA cases in large health care databases, population-based studies are scarce.

Objective

The goal of the research was to develop a natural language processing (NLP) method to identify SIRVA cases from clinical notes.

Methods

We conducted the study among members of a large integrated health care organization who were vaccinated between April 1, 2016, and December 31, 2017, and had subsequent diagnosis codes indicative of shoulder injury. Based on a training data set with a chart review reference standard of 164 cases, we developed an NLP algorithm to extract shoulder disorder information, including prior vaccination, anatomic location, temporality and causality. The algorithm identified 3 groups of positive SIRVA cases (definite, probable, and possible) based on the strength of evidence. We compared NLP results to a chart review reference standard of 100 vaccinated cases. We then applied the final automated NLP algorithm to a broader cohort of vaccinated persons with a shoulder injury diagnosis code and performed manual chart confirmation on a random sample of NLP-identified definite cases and all NLP-identified probable and possible cases.

Results

In the validation sample, the NLP algorithm had 100% accuracy for identifying 4 SIRVA cases and 96 cases without SIRVA. In the broader cohort of 53,585 vaccinations, the NLP algorithm identified 291 definite, 124 probable, and 52 possible SIRVA cases. The chart-confirmation rates for these groups were 95.5% (278/291), 67.7% (84/124), and 17.3% (9/52), respectively.

Conclusions

The algorithm performed with high sensitivity and reasonable specificity in identifying positive SIRVA cases. The NLP algorithm can potentially be used in future population-based studies to identify this rare adverse event, avoiding labor-intensive chart review validation.

ProtFus: A Comprehensive Method Characterizing Protein-Protein Interactions of Fusion Proteins

Tailored therapy aims to cure cancer patients effectively and safely, based on the complex interactions between patients' genomic features, disease pathology and drug metabolism. Thus, the continual increase in scientific literature drives the need for efficient methods of data mining to improve the extraction of useful information from texts based on patients' genomic features. An important application of text mining to tailored therapy in cancer encompasses the use of mutations and cancer fusion genes as moieties that change patients' cellular networks to develop cancer, and also affect drug metabolism. Fusion proteins, which are derived from the slippage of two parental genes, are produced in cancer by chromosomal aberrations and trans-splicing. Given that the two parental proteins for predicted fusion proteins are known, we used our previously developed method for identifying chimeric protein-protein interactions (ChiPPIs) associated with the fusion proteins. Here, we present a validation approach that receives fusion proteins of interest, predicts their cellular network alterations by ChiPPI and validates them by our new method, ProtFus, using an online literature search. This process resulted in a set of 358 fusion proteins and their corresponding protein interactions, as a training set for a Naïve Bayes classifier, to identify predicted fusion proteins that have reliable evidence in the literature and that were confirmed experimentally. Next, for a test group of 1817 fusion proteins, we were able to identify from the literature 2908 PPIs in total, across 18 cancer types. The described method, ProtFus, can be used for screening the literature to identify unique cases of fusion proteins and their PPIs, as means of studying alterations of protein networks in cancers. Availability: http://protfus.md.biu.ac.il/.

Epitope design of L1 protein for vaccine production against Human Papilloma Virus types 16 and 18

Cervical cancer accounts for about two-thirds of all cancer cases linked etiologically to Human Papilloma Virus (HPV). 15 oncogenic HPV types can cause cervical cancer, of which HPV16 and HPV18 combinedly account for about 70% of it. So, effective epitope design for the clinically relevant HPV types 16 and 18 would be of major medical benefit. Here, a comprehensive analysis is carried out to predict the epitopes against HPV types 16 and 18 through "reverse vaccinology" approach. We attempted to identify the evolutionarily conserved regions of major capsid protein (L1) as well as minor capsid protein (L2) of HPV and designed epitopes within these regions. In this study, we analyzed about 49 and 27 sequences of HPV L2 and L1 proteins respectively. Since we found that the intertype variability of L2 is higher than for L1 proteins, our analysis was emphasized on epitopes of L1 of HPV types 16 and 18. We had selected HLA-A*0201, DRB1*1501, DQB1*0602, DRB1*0401 and DQB1*0301 alleles for the prediction of T cell epitopes of L1 of HPV 16 and 18. Finally, we reported that predicted epitope sequences EEYDLQFIFQLCKITLTA, and RHGEEYDLQFIFQLCKITLTA of L1 protein of HPV 16, and LPDPNKF, PETQRLVWAC, PVPGQYDA, YNPETQRLVWAC, DTGYGAMD, PVPGQYDATK, KQDIPKVSAYQYRVFRV, RDNVSVDYKQTQLCI and YSRHVEEYDLQFIF of L1 protein of HPV 18 could be therapeutic tools for vaccine design against HPV.

Text Mining the History of Medicine

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while the processing pipeline and its modules may be used and configured within the Argo TM platform.

Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system

Protein phosphorylation is a reversible post-translational modification where a protein kinase adds a phosphate group to a protein, potentially regulating its function, localization and/or activity. Phosphorylation can affect protein-protein interactions (PPIs), abolishing interaction with previous binding partners or enabling new interactions. Extracting phosphorylation information coupled with PPI information from the scientific literature will facilitate the creation of phosphorylation interaction networks of kinases, substrates and interacting partners, toward knowledge discovery of functional outcomes of protein phosphorylation. Increasingly, PPI databases are interested in capturing the phosphorylation state of interacting partners. We have previously developed the eFIP (Extracting Functional Impact of Phosphorylation) text mining system, which identifies phosphorylated proteins and phosphorylation-dependent PPIs. In this work, we present several enhancements for the eFIP system: (i) text mining for full-length articles from the PubMed Central open-access collection; (ii) the integration of the RLIMS-P 2.0 system for the extraction of phosphorylation events with kinase, substrate and site information; (iii) the extension of the PPI module with new trigger words/phrases describing interactions and (iv) the addition of the iSimp tool for sentence simplification to aid in the matching of syntactic patterns. We enhance the website functionality to: (i) support searches based on protein roles (kinases, substrates, interacting partners) or using keywords; (ii) link protein entities to their corresponding UniProt identifiers if mapped and (iii) support visual exploration of phosphorylation interaction networks using Cytoscape. The evaluation of eFIP on full-length articles achieved 92.4% precision, 76.5% recall and 83.7% F-measure on 100 article sections. To demonstrate eFIP for knowledge extraction and discovery, we constructed phosphorylation-dependent interaction networks involving 14-3-3 proteins identified from cancer-related versus diabetes-related articles. Comparison of the phosphorylation interaction network of kinases, phosphoproteins and interactants obtained from eFIP searches, along with enrichment analysis of the protein set, revealed several shared interactions, highlighting common pathways discussed in the context of both diseases.

Topic Categorization of Biomedical Abstracts

Nowadays, people frequently use search engines in order to find the information they need on the Web. Especially, Web search constitutes of a basic tool used by million researchers in their everyday work. A very popular indexing engine, concerning life sciences and biomedical research, is PubMed. PubMed is a free database accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. The present search engines usually return search results in a global ranking, making it difficult for the users to browse in different topics or subtopics.

In this work we propose a novel system to address the issues of clustering biomedical search engine results according to their topic. A methodology that exploits semantic text clustering techniques in order to group biomedical document collections in homogeneous topics, is presented and evaluated. In order to provide more accurate clustering results, we utilize various biomedical ontologies, likeMeSH and GeneOntology. Finally, we embed the proposed methodology in an online system that post-processes the PubMed online database so as to provide users the retrieved results according to well formed topics. So as to expose our method as an online real-time service with reasonable response times, we performed a wide range of engineering optimizations along with experimentation on preprocessing time to precision of results trade off.

Semi-supervised learning of causal relations in biomedical scientific discourse

Background

The increasing number of daily published articles in the biomedical domain has become too large for humans to handle on their own. As a result, bio-text mining technologies have been developed to improve their workload by automatically analysing the text and extracting important knowledge. Specific bio-entities, bio-events between these and facts can now be recognised with sufficient accuracy and are widely used by biomedical researchers. However, understanding how the extracted facts are connected in text is an extremely difficult task, which cannot be easily tackled by machinery.

Results

In this article, we describe our method to recognise causal triggers and their arguments in biomedical scientific discourse. We introduce new features and show that a self-learning approach improves the performance obtained by supervised machine learners to 83.47% for causal triggers. Furthermore, the spans of causal arguments can be recognised to a slightly higher level that by using supervised or rule-based methods that have been employed before.

Conclusion

Exploiting the large amount of unlabelled data that is already available can help improve the performance of recognising causal discourse relations in the biomedical domain. This improvement will further benefit the development of multiple tasks, such as hypothesis generation for experimental laboratories, contradiction detection, and the creation of causal networks.

Recognising discourse causality triggers in the biomedical domain

Current domain-specific information extraction systems represent an important resource for biomedical researchers, who need to process vast amounts of knowledge in a short time. Automatic discourse causality recognition can further reduce their workload by suggesting possible causal connections and aiding in the curation of pathway models. We describe here an approach to the automatic identification of discourse causality triggers in the biomedical domain using machine learning. We create several baselines and experiment with and compare various parameter settings for three algorithms, i.e. Conditional Random Fields (CRF), Support Vector Machines (SVM) and Random Forests (RF). We also evaluate the impact of lexical, syntactic, and semantic features on each of the algorithms, showing that semantics improves the performance in all cases. We test our comprehensive feature set on two corpora containing gold standard annotations of causal relations, and demonstrate the need for more gold standard data. The best performance of 79.35% F-score is achieved by CRFs when using all three feature types.