Event-based text mining for biology and functional genomics

Automatic extraction of transcriptional regulatory interactions of bacteria from biomedical literature using a BERT-based approach

Transcriptional regulatory networks (TRNs) give a global view of the regulatory mechanisms of bacteria to respond to environmental signals. These networks are published in biological databases as a valuable resource for experimental and bioinformatics researchers. Despite the efforts to publish TRNs of diverse bacteria, many of them still lack one and many of the existing TRNs are incomplete. In addition, the manual extraction of information from biomedical literature ("literature curation") has been the traditional way to extract these networks, despite this being demanding and time-consuming. Recently, language models based on pretrained transformers have been used to extract relevant knowledge from biomedical literature. Moreover, the benefit of fine-tuning a large pretrained model with new limited data for a specific task ("transfer learning") opens roads to address new problems of biomedical information extraction. Here, to alleviate this lack of knowledge and assist literature curation, we present a new approach based on the Bidirectional Transformer for Language Understanding (BERT) architecture to classify transcriptional regulatory interactions of bacteria as a first step to extract TRNs from literature. The approach achieved a significant performance in a test dataset of sentences of Escherichia coli (F1-Score: 0.8685, Matthew's correlation coefficient: 0.8163). The examination of model predictions revealed that the model learned different ways to express the regulatory interaction. The approach was evaluated to extract a TRN of Salmonella using 264 complete articles. The evaluation showed that the approach was able to accurately extract 82% of the network and that it was able to extract interactions absent in curation data. To the best of our knowledge, the present study is the first effort to obtain a BERT-based approach to extract this specific kind of interaction. This approach is a starting point to address the limitations of reconstructing TRNs of bacteria and diseases of biological interest. Database URL: https://github.com/laigen-unam/BERT-trn-extraction.

Text mining for contexts and relationships in cancer genomics literature

MOTIVATION

Scientific advances build on the findings of existing research. The 2001 publication of the human genome has led to the production of huge volumes of literature exploring the context-specific functions and interactions of genes. Technology is needed to perform large-scale text mining of research papers to extract the reported actions of genes in specific experimental contexts and cell states, such as cancer, thereby facilitating the design of new therapeutic strategies.

RESULTS

We present a new corpus and Text Mining methodology that can accurately identify and extract the most important details of cancer genomics experiments from biomedical texts. We build a Named Entity Recognition model that accurately extracts relevant experiment details from PubMed abstract text, and a second model that identifies the relationships between them. This system outperforms earlier models and enables the analysis of gene function in diverse and dynamically evolving experimental contexts.

AVAILABILITY AND IMPLEMENTATION

Code and data are available here: https://github.com/cambridgeltl/functional-genomics-ie.

Automated assembly of molecular mechanisms at scale from text mining and curated databases

The analysis of omic data depends on machine-readable information about protein interactions, modifications, and activities as found in protein interaction networks, databases of post-translational modifications, and curated models of gene and protein function. These resources typically depend heavily on human curation. Natural language processing systems that read the primary literature have the potential to substantially extend knowledge resources while reducing the burden on human curators. However, machine-reading systems are limited by high error rates and commonly generate fragmentary and redundant information. Here, we describe an approach to precisely assemble molecular mechanisms at scale using multiple natural language processing systems and the Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA identifies full and partial overlaps in information extracted from published papers and pathway databases, uses predictive models to improve the reliability of machine reading, and thereby assembles individual pieces of information into non-redundant and broadly usable mechanistic knowledge. Using INDRA to create high-quality corpora of causal knowledge we show it is possible to extend protein-protein interaction databases and explain co-dependencies in the Cancer Dependency Map.

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.

A biomedical event extraction method based on fine-grained and attention mechanism

BACKGROUND

Biomedical event extraction is a fundamental task in biomedical text mining, which provides inspiration for medicine research and disease prevention. Biomedical events include simple events and complex events. Existing biomedical event extraction methods usually deal with simple events and complex events uniformly, and the performance of complex event extraction is relatively low.

RESULTS

In this paper, we propose a fine-grained Bidirectional Long Short Term Memory method for biomedical event extraction, which designs different argument detection models for simple and complex events respectively. In addition, multi-level attention is designed to improve the performance of complex event extraction, and sentence embeddings are integrated to obtain sentence level information which can resolve the ambiguities for some types of events. Our method achieves state-of-the-art performance on the commonly used dataset Multi-Level Event Extraction.

CONCLUSIONS

The sentence embeddings enrich the global sentence-level information. The fine-grained argument detection model improves the performance of complex biomedical event extraction. Furthermore, the multi-level attention mechanism enhances the interactions among relevant arguments. The experimental results demonstrate the effectiveness of the proposed method for biomedical event extraction.

Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference

The cost of drug development continues to rise and may be prohibitive in cases of unmet clinical need, particularly for rare diseases. Artificial intelligence-based methods are promising in their potential to discover new treatment options. The task of drug repurposing hypothesis generation is well-posed as a link prediction problem in a knowledge graph (KG) of interacting of drugs, proteins, genes and disease phenotypes. KGs derived from biomedical literature are semantically rich and up-to-date representations of scientific knowledge. Inference methods on scientific KGs can be confounded by unspecified contexts and contradictions. Extracting context enables incorporation of relevant pharmacokinetic and pharmacodynamic detail, such as tissue specificity of interactions. Contradictions in biomedical KGs may arise when contexts are omitted or due to contradicting research claims. In this review, we describe challenges to creating literature-scale representations of pharmacological knowledge and survey current approaches toward incorporating context and resolving contradictions.

Comparing neural models for nested and overlapping biomedical event detection

Background

Nested and overlapping events are particularly frequent and informative structures in biomedical event extraction. However, state-of-the-art neural models either neglect those structures during learning or use syntactic features and external tools to detect them. To overcome these limitations, this paper presents and compares two neural models: a novel EXhaustive Neural Network (EXNN) and a Search-Based Neural Network (SBNN) for detection of nested and overlapping events.

Results

We evaluate the proposed models as an event detection component in isolation and within a pipeline setting. Evaluation in several annotated biomedical event extraction datasets shows that both EXNN and SBNN achieve higher performance in detecting nested and overlapping events, compared to the state-of-the-art model Turku Event Extraction System (TEES).

Conclusions

The experimental results reveal that both EXNN and SBNN are effective for biomedical event extraction. Furthermore, results on a pipeline setting indicate that our models improve detection of events compared to models that use either gold or predicted named entities.

A Text Mining Protocol for Mining Biological Pathways and Regulatory Networks from Biomedical Literature

A biological pathway or regulatory network is a collection of molecular regulators which can activate the changes in cellular processes leading to an assembly of new molecules by series of actions among the molecules. There are three important pathways in system biology studies namely signaling pathways, metabolic pathways, and genetic pathways (or) gene regulatory networks. Recently, biological pathway construction from scientific literature is given much attention as the scientific literature contains a rich set of linguistic features to extract biological associations between genes and proteins. These associations can be united to construct biological networks. Here, we present a brief overview about various biological pathways, biomedical text resources/corpora for network construction and state-of-the-art existing methods for network construction followed by our hybrid text mining protocol for extracting pathways and regulatory networks from biomedical literature.

SWACG: A Hybrid Neural Network Integrating Sliding Window for Biomedical Event Trigger Extraction

Abstract As an important part of biomedical text mining, biomedical events play a key role in improving biomedical research and disease prevention. Trigger identification, extracting the words describing the event types, is a critical and prerequisite step for biomedical event extraction. Traditional methods excessively rely on natural language processing tools in the feature extraction process, incurring a significant manual cost. In addition, because of the particularity of the biomedical literature, the problem of long-distance dependency is obvious. To solve these problems, we propose a hybrid structure SWACG, which consists of the ReCNN-BiGRU (Residual CNN and Bidirectional Gated Recurrent Unit) hybrid neural network and MH-attention (Multi-Head attention) mechanism. The proposed model uses ReCNN to extract vocabulary-level features and BiGRU to obtain contextual semantic information. Furthermore, sliding window divides long sentences into equal-length short sentences without destroying context information, which can avoid long-distance dependency. Experimental results show that our method advances the state-of-the-art performance on the commonly used Multi-Level Event Extraction (MLEE) corpus, achieving 82.20% F-score.

Knowledge extraction for assisted curation of summaries of bacterial transcription factor properties

Transcription factors (TFs) play a main role in transcriptional regulation of bacteria, as they regulate transcription of the genetic information encoded in DNA. Thus, the curation of the properties of these regulatory proteins is essential for a better understanding of transcriptional regulation. However, traditional manual curation of article collections to compile descriptions of TF properties takes significant time and effort due to the overwhelming amount of biomedical literature, which increases every day. The development of automatic approaches for knowledge extraction to assist curation is therefore critical. Here, we show an effective approach for knowledge extraction to assist curation of summaries describing bacterial TF properties based on an automatic text summarization strategy. We were able to recover automatically a median 77% of the knowledge contained in manual summaries describing properties of 177 TFs of Escherichia coli K-12 by processing 5961 scientific articles. For 71% of the TFs, our approach extracted new knowledge that can be used to expand manual descriptions. Furthermore, as we trained our predictive model with manual summaries of E. coli, we also generated summaries for 185 TFs of Salmonella enterica serovar Typhimurium from 3498 articles. According to the manual curation of 10 of these Salmonella typhimurium summaries, 96% of their sentences contained relevant knowledge. Our results demonstrate the feasibility to assist manual curation to expand manual summaries with new knowledge automatically extracted and to create new summaries of bacteria for which these curation efforts do not exist. Database URL: The automatic summaries of the TFs of E. coli and Salmonella and the automatic summarizer are available in GitHub (https://github.com/laigen-unam/tf-properties-summarizer.git).

Advanced strategy for metabolite exploration in filamentous fungi

Filamentous fungi comprise an abundance of gene clusters that encode high-value metabolites, whereas affluent gene clusters remain silent during laboratory conditions. Complex cellular metabolism further limits these metabolite yields. Therefore, diverse strategies such as genetic engineering and chemical mutagenesis have been developed to activate these cryptic pathways and improve metabolite productivity. However, lower efficiencies of gene modifications and screen tools delayed the above processes. To address the above issues, this review describes an alternative design-construction evaluation optimization (DCEO) approach. The DCEO tool provides theoretical and practical principles to identify potential pathways, modify endogenous pathways, integrate exogenous pathways, and exploit novel pathways for their diverse metabolites and desirable productivities. This DCEO method also offers different tactics to balance the cellular metabolisms, facilitate the genetic engineering, and exploit the scalable metabolites in filamentous fungi.

Topic-informed neural approach for biomedical event extraction

As a crucial step of biological event extraction, event trigger identification has attracted much attention in recent years. Deep representation methods, which have the superiorities of less feature engineering and end-to-end training, show better performance than statistical methods. While most deep learning methods have been done on sentence-level event extraction, there are few works taking document context into account, losing potentially informative knowledge that is beneficial for trigger detection. In this paper, we propose a variational neural approach for biomedical event extraction, which can take advantage of latent topics underlying documents. By adopting a joint modeling manner of topics and events, our model is able to produce more meaningful and event-indicative words compare to prior topic models. In addition, we introduce a language model embeddings to capture context-dependent features. Experimental results show that our approach outperforms various baselines in a commonly used multi-level event extraction corpus.

Text mining for human resources competencies: Taiwan example

Purpose

The purpose of this study is to explore the capabilities required by entry-level human resources (HR) professionals based on job advertisements by using text mining (TM) technique.

Design/methodology/approach

This study used TM techniques to explore the capabilities required by entry-level HR professionals based on job advertisements on HR agency 104’s website in Taiwan. Python was used to crawl the advertisements on the website, and 841 posts were collected. Next, the author used TM to explore and understand hidden trends and patterns in numerous data sets.

Findings

The results of this study revealed four critical success factors (specific skills, educational level, experience and specific capabilities), five clusters and ten classifications.

Practical implications

The results can aid HR curriculum developers and educators in customizing and improving HR education curricula, such that HR students can develop capabilities required to secure employment in the current HR job market.

Originality/value

Our results may facilitate the understanding of the current trends in the HR job market and provide useful suggestions to HR curriculum developers for improving training and professional course design, such that students’ competitiveness is enhanced and professional capabilities improved.

Contextual label sensitive gated network for biomedical event trigger extraction

Biomedical events play a key role in improving biomedical research. Event trigger identification, extracting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. There exist two main problems in previous methods: (1) The association among contextual trigger labels which can provide significant clues is ignored. (2)The weight between word embeddings and contextual features needs to be adjusted dynamically according to the trigger candidate. In this paper, we propose a novel contextual label sensitive gated network for biomedical event trigger extraction to solve the above two problems, which can mix the two parts dynamically and capture the contextual label clues automatically. Furthermore, we also introduce the dependency-based word embeddings to represent dependency-based semantic information as well as attention mechanism to get more focused representations. Experimental results show that our approach advances state-of-the-arts and achieves the best F1-score on the commonly used Multi-Level Event Extraction (MLEE) corpus.

Extraction of chemical-protein interactions from the literature using neural networks and narrow instance representation

The scientific literature contains large amounts of information on genes, proteins, chemicals and their interactions. Extraction and integration of this information in curated knowledge bases help researchers support their experimental results, leading to new hypotheses and discoveries. This is especially relevant for precision medicine, which aims to understand the individual variability across patient groups in order to select the most appropriate treatments. Methods for improved retrieval and automatic relation extraction from biomedical literature are therefore required for collecting structured information from the growing number of published works. In this paper, we follow a deep learning approach for extracting mentions of chemical-protein interactions from biomedical articles, based on various enhancements over our participation in the BioCreative VI CHEMPROT task. A significant aspect of our best method is the use of a simple deep learning model together with a very narrow representation of the relation instances, using only up to 10 words from the shortest dependency path and the respective dependency edges. Bidirectional long short-term memory recurrent networks or convolutional neural networks are used to build the deep learning models. We report the results of several experiments and show that our best model is competitive with more complex sentence representations or network structures, achieving an F1-score of 0.6306 on the test set. The source code of our work, along with detailed statistics, is publicly available.

Annotation and detection of drug effects in text for pharmacovigilance

Pharmacovigilance (PV) databases record the benefits and risks of different drugs, as a means to ensure their safe and effective use. Creating and maintaining such resources can be complex, since a particular medication may have divergent effects in different individuals, due to specific patient characteristics and/or interactions with other drugs being administered. Textual information from various sources can provide important evidence to curators of PV databases about the usage and effects of drug targets in different medical subjects. However, the efficient identification of relevant evidence can be challenging, due to the increasing volume of textual data. Text mining (TM) techniques can support curators by automatically detecting complex information, such as interactions between drugs, diseases and adverse effects. This semantic information supports the quick identification of documents containing information of interest (e.g., the different types of patients in which a given adverse drug reaction has been observed to occur). TM tools are typically adapted to different domains by applying machine learning methods to corpora that are manually labelled by domain experts using annotation guidelines to ensure consistency. We present a semantically annotated corpus of 597 MEDLINE abstracts, PHAEDRA, encoding rich information on drug effects and their interactions, whose quality is assured through the use of detailed annotation guidelines and the demonstration of high levels of inter-annotator agreement (e.g., 92.6% F-Score for identifying named entities and 78.4% F-Score for identifying complex events, when relaxed matching criteria are applied). To our knowledge, the corpus is unique in the domain of PV, according to the level of detail of its annotations. To illustrate the utility of the corpus, we have trained TM tools based on its rich labels to recognise drug effects in text automatically. The corpus and annotation guidelines are available at: http://www.nactem.ac.uk/PHAEDRA/ .

FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining

Background

For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or “grounding.” Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers.

Results

In a task involving automated reading of ∼215,000 articles using the REACH event extraction software we found that grounding was disproportionately inaccurate for multi-protein families (e.g., “AKT”) and complexes with multiple subunits (e.g.“NF- κB”). To address this problem we constructed FamPlex, a manually curated resource defining protein families and complexes as they are commonly encountered in biomedical text. In FamPlex the gene-level constituents of families and complexes are defined in a flexible format allowing for multi-level, hierarchical membership. To create FamPlex, text strings corresponding to entities were identified empirically from literature and linked manually to uniform identifiers; these identifiers were also mapped to equivalent entries in multiple related databases. FamPlex also includes curated prefix and suffix patterns that improve named entity recognition and event extraction. Evaluation of REACH extractions on a test corpus of ∼54,000 articles showed that FamPlex significantly increased grounding accuracy for families and complexes (from 15 to 71%). The hierarchical organization of entities in FamPlex also made it possible to integrate otherwise unconnected mechanistic information across families, subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM reading system and the Biocreative VI Bioentity Normalization Task dataset demonstrated the utility of FamPlex in other settings.

Conclusion

FamPlex is an effective resource for improving named entity recognition, grounding, and relationship resolution in automated reading of biomedical text. The content in FamPlex is available in both tabular and Open Biomedical Ontology formats at https://github.com/sorgerlab/famplex under the Creative Commons CC0 license and has been integrated into the TRIPS/DRUM and REACH reading systems.

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823-2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein-protein, disease-gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

Using uncertainty to link and rank evidence from biomedical literature for model curation

MOTIVATION

In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author. Despite textual uncertainty being acknowledged in biomedical text mining as an attribute of text mined interactions (events), it is significantly understudied as a means of providing a confidence measure for interactions in pathways or other biomedical models. In this work, we focus on improving identification of textual uncertainty for events and explore how it can be used as an additional measure of confidence for biomedical models.

RESULTS

We present a novel method for extracting uncertainty from the literature using a hybrid approach that combines rule induction and machine learning. Variations of this hybrid approach are then discussed, alongside their advantages and disadvantages. We use subjective logic theory to combine multiple uncertainty values extracted from different sources for the same interaction. Our approach achieves F-scores of 0.76 and 0.88 based on the BioNLP-ST and Genia-MK corpora, respectively, making considerable improvements over previously published work. Moreover, we evaluate our proposed system on pathways related to two different areas, namely leukemia and melanoma cancer research.

AVAILABILITY AND IMPLEMENTATION

The leukemia pathway model used is available in Pathway Studio while the Ras model is available via PathwayCommons. Online demonstration of the uncertainty extraction system is available for research purposes at http://argo.nactem.ac.uk/test. The related code is available on https://github.com/c-zrv/uncertainty_components.git. Details on the above are available in the Supplementary Material.

CONTACT

sophia.ananiadou@manchester.ac.uk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

SciLite: a platform for displaying text-mined annotations as a means to link research articles with biological data

The tremendous growth in biological data has resulted in an increase in the number of research papers being published. This presents a great challenge for scientists in searching and assimilating facts described in those papers. Particularly, biological databases depend on curators to add highly precise and useful information that are usually extracted by reading research articles. Therefore, there is an urgent need to find ways to improve linking literature to the underlying data, thereby minimising the effort in browsing content and identifying key biological concepts.   As part of the development of Europe PMC, we have developed a new platform, SciLite, which integrates text-mined annotations from different sources and overlays those outputs on research articles. The aim is to aid researchers and curators using Europe PMC in finding key concepts more easily and provide links to related resources or tools, bridging the gap between literature and biological data.

Multiple kernels learning-based biological entity relationship extraction method

Background

Automatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts included in MEDLINE, which is the most authoritative textual database in the field of biomedicine, and follow an exponential growth over time. This frantic expansion of the biomedical literature can often be difficult to absorb or manually analyze. Thus efficient and automated search engines are necessary to efficiently explore the biomedical literature using text mining techniques.

Results

The P, R, and F value of tag graph method in Aimed corpus are 50.82, 69.76, and 58.61%, respectively. The P, R, and F value of tag graph kernel method in other four evaluation corpuses are 2–5% higher than that of all-paths graph kernel. And The P, R and F value of feature kernel and tag graph kernel fuse methods is 53.43, 71.62 and 61.30%, respectively. The P, R and F value of feature kernel and tag graph kernel fuse methods is 55.47, 70.29 and 60.37%, respectively. It indicated that the performance of the two kinds of kernel fusion methods is better than that of simple kernel.

Conclusion

In comparison with the all-paths graph kernel method, the tag graph kernel method is superior in terms of overall performance. Experiments show that the performance of the multi-kernels method is better than that of the three separate single-kernel method and the dual-mutually fused kernel method used hereof in five corpus sets.

SciLite: a platform for displaying text-mined annotations as a means to link research articles with biological data

The tremendous growth in biological data has resulted in an increase in the number of research papers being published. This presents a great challenge for scientists in searching and assimilating facts described in those papers. Particularly, biological databases depend on curators to add highly precise and useful information that are usually extracted by reading research articles. Therefore, there is an urgent need to find ways to improve linking literature to the underlying data, thereby minimising the effort in browsing content and identifying key biological concepts.   As part of the development of Europe PMC, we have developed a new platform, SciLite, which integrates text-mined annotations from different sources and overlays those outputs on research articles. The aim is to aid researchers and curators using Europe PMC in finding key concepts more easily and provide links to related resources or tools, bridging the gap between literature and biological data.

A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries

In the past decade, the volume of "omics" data generated by the different high-throughput technologies has expanded exponentially. The managing, storing, and analyzing of this big data have been a great challenge for the researchers, especially when moving towards the goal of generating testable data-driven hypotheses, which has been the promise of the high-throughput experimental techniques. Different bioinformatics approaches have been developed to streamline the downstream analyzes by providing independent information to interpret and provide biological inference. Text mining (also known as literature mining) is one of the commonly used approaches for automated generation of biological knowledge from the huge number of published articles. In this review paper, we discuss the recent advancement in approaches that integrate results from omics data and information generated from text mining approaches to uncover novel biomedical information.

Can we replace curation with information extraction software?

Can we use programs for automated or semi-automated information extraction from scientific texts as practical alternatives to professional curation? I show that error rates of current information extraction programs are too high to replace professional curation today. Furthermore, current IEP programs extract single narrow slivers of information, such as individual protein interactions; they cannot extract the large breadth of information extracted by professional curators for databases such as EcoCyc. They also cannot arbitrate among conflicting statements in the literature as curators can. Therefore, funding agencies should not hobble the curation efforts of existing databases on the assumption that a problem that has stymied Artificial Intelligence researchers for more than 60 years will be solved tomorrow. Semi-automated extraction techniques appear to have significantly more potential based on a review of recent tools that enhance curator productivity. But a full cost-benefit analysis for these tools is lacking. Without such analysis it is possible to expend significant effort developing information-extraction tools that automate small parts of the overall curation workflow without achieving a significant decrease in curation costs.Database URL.

The landscape of microbial phenotypic traits and associated genes

Bacteria and Archaea display a variety of phenotypic traits and can adapt to diverse ecological niches. However, systematic annotation of prokaryotic phenotypes is lacking. We have therefore developed ProTraits, a resource containing ∼545 000 novel phenotype inferences, spanning 424 traits assigned to 3046 bacterial and archaeal species. These annotations were assigned by a computational pipeline that associates microbes with phenotypes by text-mining the scientific literature and the broader World Wide Web, while also being able to define novel concepts from unstructured text. Moreover, the ProTraits pipeline assigns phenotypes by drawing extensively on comparative genomics, capturing patterns in gene repertoires, codon usage biases, proteome composition and co-occurrence in metagenomes. Notably, we find that gene synteny is highly predictive of many phenotypes, and highlight examples of gene neighborhoods associated with spore-forming ability. A global analysis of trait interrelatedness outlined clusters in the microbial phenotype network, suggesting common genetic underpinnings. Our extended set of phenotype annotations allows detection of 57 088 high confidence gene-trait links, which recover many known associations involving sporulation, flagella, catalase activity, aerobicity, photosynthesis and other traits. Over 99% of the commonly occurring gene families are involved in genetic interactions conditional on at least one phenotype, suggesting that epistasis has a major role in shaping microbial gene content.

Propagation of errors in citation networks: a study involving the entire citation network of a widely cited paper published in, and later retracted from, the journal Nature

BACKGROUND

In about one in 10,000 cases, a published article is retracted. This very often means that the results it reports are flawed. Several authors have voiced concerns about the presence of retracted research in the memory of science. In particular, a retracted result is propagated by citing it. In the published literature, many instances are given of retracted articles that are cited both before and after their retraction. Even worse is the possibility that these articles in turn are cited in such a way that the retracted result is propagated further.

METHODS

We have conducted a case study to find out how a retracted article is cited and whether retracted results are propagated through indirect citations. We have constructed the entire citation network for this case.

RESULTS

We show that directly citing articles is an important source of propagation of retracted research results. In contrast, in our case study, indirect citations do not contribute to the propagation of the retracted result.

CONCLUSIONS

While admitting the limitations of a study involving a single case, we think there are reasons for the non-contribution of indirect citations that hold beyond our case study.

An Overview of Biomolecular Event Extraction from Scientific Documents

This paper presents a review of state-of-the-art approaches to automatic extraction of biomolecular events from scientific texts. Events involving biomolecules such as genes, transcription factors, or enzymes, for example, have a central role in biological processes and functions and provide valuable information for describing physiological and pathogenesis mechanisms. Event extraction from biomedical literature has a broad range of applications, including support for information retrieval, knowledge summarization, and information extraction and discovery. However, automatic event extraction is a challenging task due to the ambiguity and diversity of natural language and higher-level linguistic phenomena, such as speculations and negations, which occur in biological texts and can lead to misunderstanding or incorrect interpretation. Many strategies have been proposed in the last decade, originating from different research areas such as natural language processing, machine learning, and statistics. This review summarizes the most representative approaches in biomolecular event extraction and presents an analysis of the current state of the art and of commonly used methods, features, and tools. Finally, current research trends and future perspectives are also discussed.

Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research

Background

Current biomedical research needs to leverage and exploit the large amount of information reported in scientific publications. Automated text mining approaches, in particular those aimed at finding relationships between entities, are key for identification of actionable knowledge from free text repositories. We present the BeFree system aimed at identifying relationships between biomedical entities with a special focus on genes and their associated diseases.

Results

By exploiting morpho-syntactic information of the text, BeFree is able to identify gene-disease, drug-disease and drug-target associations with state-of-the-art performance. The application of BeFree to real-case scenarios shows its effectiveness in extracting information relevant for translational research. We show the value of the gene-disease associations extracted by BeFree through a number of analyses and integration with other data sources. BeFree succeeds in identifying genes associated to a major cause of morbidity worldwide, depression, which are not present in other public resources. Moreover, large-scale extraction and analysis of gene-disease associations, and integration with current biomedical knowledge, provided interesting insights on the kind of information that can be found in the literature, and raised challenges regarding data prioritization and curation. We found that only a small proportion of the gene-disease associations discovered by using BeFree is collected in expert-curated databases. Thus, there is a pressing need to find alternative strategies to manual curation, in order to review, prioritize and curate text-mining data and incorporate it into domain-specific databases. We present our strategy for data prioritization and discuss its implications for supporting biomedical research and applications.

Conclusions

BeFree is a novel text mining system that performs competitively for the identification of gene-disease, drug-disease and drug-target associations. Our analyses show that mining only a small fraction of MEDLINE results in a large dataset of gene-disease associations, and only a small proportion of this dataset is actually recorded in curated resources (2%), raising several issues on data prioritization and curation. We propose that joint analysis of text mined data with data curated by experts appears as a suitable approach to both assess data quality and highlight novel and interesting information.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0472-9) contains supplementary material, which is available to authorized users.