Integrating high dimensional bi-directional parsing models for gene mention tagging

Motivation: Tagging gene and gene product mentions in scientific text is an important initial step of literature mining. In this article, we describe in detail our gene mention tagger participated in BioCreative 2 challenge and analyze what contributes to its good performance. Our tagger is based on the conditional random fields model (CRF), the most prevailing method for the gene mention tagging task in BioCreative 2. Our tagger is interesting because it accomplished the highest F-scores among CRF-based methods and second over all. Moreover, we obtained our results by mostly applying open source packages, making it easy to duplicate our results

Soft tagging of overlapping high confidence gene mention variants for cross-species full-text gene normalization

Abstract Background Previously, gene normalization (GN) systems are mostly focused on disambiguation using contextual information. An effective gene mention tagger is deemed unnecessary because the subsequent steps will filter out false positives and high recall is sufficient. However, unlike similar tasks in the past BioCreative challenges, the BioCreative III GN task is particularly challenging because it is not species-specific. Required to process full-length articles, an ineffective gene mention tagger may produce a huge number of ambiguous false positives that overwhelm subsequent filtering steps while still missing many true positives. Results We present our GN system participated in the BioCreative III GN task. Our system applies a typical 2-stage approach to GN but features a soft tagging gene mention tagger that generates a set of overlapping gene mention variants with a nearly perfect recall. The overlapping gene mention variants increase the chance of precise match in the dictionary and alleviate the need of disambiguation. Our GN system achieved a precision of 0.9 (F-score 0.63) on the BioCreative III GN test corpus with the silver annotation of 507 articles. Its TAP-k scores are competitive to the best results among all participants. Conclusions We show that despite the lack of clever disambiguation in our gene normalization system, effective soft tagging of gene mention variants can indeed contribute to performance in cross-species and full-text gene normalization.</p

Large-scale event extraction from literature with multi-level gene normalization

Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons -Attribution - Share Alike (CC BY-SA) license

Finding Related Publications: Extending the Set of Terms Used to Assess Article Similarity.

Recommendation of related articles is an important feature of the PubMed. The PubMed Related Citations (PRC) algorithm is the engine that enables this feature, and it leverages information on 22 million citations. We analyzed the performance of the PRC algorithm on 4584 annotated articles from the 2005 Text REtrieval Conference (TREC) Genomics Track data. Our analysis indicated that the PRC highest weighted term was not always consistent with the critical term that was most directly related to the topic of the article. We implemented term expansion and found that it was a promising and easy-to-implement approach to improve the performance of the PRC algorithm for the TREC 2005 Genomics data and for the TREC 2014 Clinical Decision Support Track data. For term expansion, we trained a Skip-gram model using the Word2Vec package. This extended PRC algorithm resulted in higher average precision for a large subset of articles. A combination of both algorithms may lead to improved performance in related article recommendations

Optimizing text mining methods for improving biomedical natural language processing

The overwhelming amount and the increasing rate of publication in the biomedical domain make it difficult for life sciences researchers to acquire and maintain all information that is necessary for their research. Pubmed (the primary citation database for the biomedical literature) currently contains over 21 million article abstracts and more than one million of them were published in 2020 alone. Even though existing article databases provide capable keyword search services, typical everyday-life queries usually return thousands of relevant articles. For instance, a cancer research scientist may need to acquire a complete list of genes that interact with BRCA1 (breast cancer 1) gene. The PubMed keyword search for BRCA1 returns over 16,500 article abstracts, making manual inspection of the retrieved documents impractical. Missing even one of the interacting gene partners in this scenario may jeopardize successful development of a potential new drug or vaccine. Although manually curated databases of biomolecular interactions exist, they are usually not up-to-date and they require notable human effort to maintain. To summarize, new discoveries are constantly being shared within the community via scientific publishing, but unfortunately the probability of missing vital information for research in life sciences is increasing. In response to this problem, the biomedical natural language processing (BioNLP) community of researchers has emerged and strives to assist life sciences researchers by building modern language processing and text mining tools that can be applied at large-scale and scan the whole publicly available literature and extract, classify, and aggregate the information found within, thus keeping life sciences researchers always up-to-date with the recent relevant discoveries and facilitating their research in numerous fields such as molecular biology, biomedical engineering, bioinformatics, genetics engineering and biochemistry. My research has almost exclusively focused on biomedical relation and event extraction tasks. These foundational information extraction tasks deal with automatic detection of biological processes, interactions and relations described in the biomedical literature. Precisely speaking, biomedical relation and event extraction systems can scan through a vast amount of biomedical texts and automatically detect and extract the semantic relations of biomedical named entities (e.g. genes, proteins, chemical compounds, and diseases). The structured outputs of such systems (i.e., the extracted relations or events) can be stored as relational databases or molecular interaction networks which can easily be queried, filtered, analyzed, visualized and integrated with other structured data sources. Extracting biomolecular interactions has always been the primary interest of BioNLP researcher because having knowledge about such interactions is crucially important in various research areas including precision medicine, drug discovery, drug repurposing, hypothesis generation, construction and curation of signaling pathways, and protein function and structure prediction. State-of-the-art relation and event extraction methods are based on supervised machine learning, requiring manually annotated data for training. Manual annotation for the biomedical domain requires domain expertise and it is time-consuming. Hence, having minimal training data for building information extraction systems is a common case in the biomedical domain. This demands development of methods that can make the most out of available training data and this thesis gathers all my research efforts and contributions in that direction. It is worth mentioning that biomedical natural language processing has undergone a revolution since I started my research in this field almost ten years ago. As a member of the BioNLP community, I have witnessed the emergence, improvement– and in some cases, the disappearance–of many methods, each pushing the performance of the best previous method one step further. I can broadly divide the last ten years into three periods. Once I started my research, feature-based methods that relied on heavy feature engineering were dominant and popular. Then, significant advancements in the hardware technology, as well as several breakthroughs in the algorithms and methods enabled machine learning practitioners to seriously utilize artificial neural networks for real-world applications. In this period, convolutional, recurrent, and attention-based neural network models became dominant and superior. Finally, the introduction of transformer-based language representation models such as BERT and GPT impacted the field and resulted in unprecedented performance improvements on many data sets. When reading this thesis, I demand the reader to take into account the course of history and judge the methods and results based on what could have been done in that particular period of the history

SR4GN: A Species Recognition Software Tool for Gene Normalization

As suggested in recent studies, species recognition and disambiguation is one of the most critical and challenging steps in many downstream text-mining applications such as the gene normalization task and protein-protein interaction extraction. We report SR4GN: an open source tool for species recognition and disambiguation in biomedical text. In addition to the species detection function in existing tools, SR4GN is optimized for the Gene Normalization task. As such it is developed to link detected species with corresponding gene mentions in a document. SR4GN achieves 85.42% in accuracy and compares favorably to the other state-of-the-art techniques in benchmark experiments. Finally, SR4GN is implemented as a standalone software tool, thus making it convenient and robust for use in many text-mining applications. SR4GN can be downloaded at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/downloads/SR4G

Automated Recognition of Brain Region Mentions in Neuroscience Literature

The ability to computationally extract mentions of neuroanatomical regions from the literature would assist linking to other entities within and outside of an article. Examples include extracting reports of connectivity or region-specific gene expression. To facilitate text mining of neuroscience literature we have created a corpus of manually annotated brain region mentions. The corpus contains 1,377 abstracts with 18,242 brain region annotations. Interannotator agreement was evaluated for a subset of the documents, and was 90.7% and 96.7% for strict and lenient matching respectively. We observed a large vocabulary of over 6,000 unique brain region terms and 17,000 words. For automatic extraction of brain region mentions we evaluated simple dictionary methods and complex natural language processing techniques. The dictionary methods based on neuroanatomical lexicons recalled 36% of the mentions with 57% precision. The best performance was achieved using a conditional random field (CRF) with a rich feature set. Features were based on morphological, lexical, syntactic and contextual information. The CRF recalled 76% of mentions at 81% precision, by counting partial matches recall and precision increase to 86% and 92% respectively. We suspect a large amount of error is due to coordinating conjunctions, previously unseen words and brain regions of less commonly studied organisms. We found context windows, lemmatization and abbreviation expansion to be the most informative techniques. The corpus is freely available at http://www.chibi.ubc.ca/WhiteText/