Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text

<p>Abstract</p> <p>Background</p> <p>Pharmacogenomics studies the relationship between genetic variation and the variation in drug response phenotypes. The field is rapidly gaining importance: it promises drugs targeted to particular subpopulations based on genetic background. The pharmacogenomics literature has expanded rapidly, but is dispersed in many journals. It is challenging, therefore, to identify important associations between drugs and molecular entities – particularly genes and gene variants, and thus these critical connections are often lost. Text mining techniques can allow us to convert the free-style text to a computable, searchable format in which pharmacogenomic concepts (such as genes, drugs, polymorphisms, and diseases) are identified, and important links between these concepts are recorded. Availability of full text articles as input into text mining engines is key, as literature abstracts often do not contain sufficient information to identify these pharmacogenomic associations.</p> <p>Results</p> <p>Thus, building on a tool called Textpresso, we have created the Pharmspresso tool to assist in identifying important pharmacogenomic facts in full text articles. Pharmspresso parses text to find references to human genes, polymorphisms, drugs and diseases and their relationships. It presents these as a series of marked-up text fragments, in which key concepts are visually highlighted. To evaluate Pharmspresso, we used a gold standard of 45 human-curated articles. Pharmspresso identified 78%, 61%, and 74% of target gene, polymorphism, and drug concepts, respectively.</p> <p>Conclusion</p> <p>Pharmspresso is a text analysis tool that extracts pharmacogenomic concepts from the literature automatically and thus captures our current understanding of gene-drug interactions in a computable form. We have made Pharmspresso available at <url>http://pharmspresso.stanford.edu</url>.</p

Dynamic summarization of bibliographic-based data

<p>Abstract</p> <p>Background</p> <p>Traditional information retrieval techniques typically return excessive output when directed at large bibliographic databases. Natural Language Processing applications strive to extract salient content from the excessive data. Semantic MEDLINE, a National Library of Medicine (NLM) natural language processing application, highlights relevant information in PubMed data. However, Semantic MEDLINE implements manually coded schemas, accommodating few information needs. Currently, there are only five such schemas, while many more would be needed to realistically accommodate all potential users. The aim of this project was to develop and evaluate a statistical algorithm that automatically identifies relevant bibliographic data; the new algorithm could be incorporated into a dynamic schema to accommodate various information needs in Semantic MEDLINE, and eliminate the need for multiple schemas.</p> <p>Methods</p> <p>We developed a flexible algorithm named Combo that combines three statistical metrics, the Kullback-Leibler Divergence (KLD), Riloff's RlogF metric (RlogF), and a new metric called PredScal, to automatically identify salient data in bibliographic text. We downloaded citations from a PubMed search query addressing the genetic etiology of bladder cancer. The citations were processed with SemRep, an NLM rule-based application that produces semantic predications. SemRep output was processed by Combo, in addition to the standard Semantic MEDLINE genetics schema and independently by the two individual KLD and RlogF metrics. We evaluated each summarization method using an existing reference standard within the task-based context of genetic database curation.</p> <p>Results</p> <p>Combo asserted 74 genetic entities implicated in bladder cancer development, whereas the traditional schema asserted 10 genetic entities; the KLD and RlogF metrics individually asserted 77 and 69 genetic entities, respectively. Combo achieved 61% recall and 81% precision, with an F-score of 0.69. The traditional schema achieved 23% recall and 100% precision, with an F-score of 0.37. The KLD metric achieved 61% recall, 70% precision, with an F-score of 0.65. The RlogF metric achieved 61% recall, 72% precision, with an F-score of 0.66.</p> <p>Conclusions</p> <p>Semantic MEDLINE summarization using the new Combo algorithm outperformed a conventional summarization schema in a genetic database curation task. It potentially could streamline information acquisition for other needs without having to hand-build multiple saliency schemas.</p

Constructing a semantic predication gold standard from the biomedical literature

<p>Abstract</p> <p>Background</p> <p>Semantic relations increasingly underpin biomedical text mining and knowledge discovery applications. The success of such practical applications crucially depends on the quality of extracted relations, which can be assessed against a gold standard reference. Most such references in biomedical text mining focus on narrow subdomains and adopt different semantic representations, rendering them difficult to use for benchmarking independently developed relation extraction systems. In this article, we present a multi-phase gold standard annotation study, in which we annotated 500 sentences randomly selected from MEDLINE abstracts on a wide range of biomedical topics with 1371 semantic predications. The UMLS Metathesaurus served as the main source for conceptual information and the UMLS Semantic Network for relational information. We measured interannotator agreement and analyzed the annotations closely to identify some of the challenges in annotating biomedical text with relations based on an ontology or a terminology.</p> <p>Results</p> <p>We obtain fair to moderate interannotator agreement in the practice phase (0.378-0.475). With improved guidelines and additional semantic equivalence criteria, the agreement increases by 12% (0.415 to 0.536) in the main annotation phase. In addition, we find that agreement increases to 0.688 when the agreement calculation is limited to those predications that are based only on the explicitly provided UMLS concepts and relations.</p> <p>Conclusions</p> <p>While interannotator agreement in the practice phase confirms that conceptual annotation is a challenging task, the increasing agreement in the main annotation phase points out that an acceptable level of agreement can be achieved in multiple iterations, by setting stricter guidelines and establishing semantic equivalence criteria. Mapping text to ontological concepts emerges as the main challenge in conceptual annotation. Annotating predications involving biomolecular entities and processes is particularly challenging. While the resulting gold standard is mainly intended to serve as a test collection for our semantic interpreter, we believe that the lessons learned are applicable generally.</p

Mining clinical relationships from patient narratives

Background The Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records in order to support clinical research, evidence-based healthcare and genotype-meets-phenotype informatics. One part of this system is the identification of relationships between clinically important entities in the text. Typical approaches to relationship extraction in this domain have used full parses, domain-specific grammars, and large knowledge bases encoding domain knowledge. In other areas of biomedical NLP, statistical machine learning (ML) approaches are now routinely applied to relationship extraction. We report on the novel application of these statistical techniques to the extraction of clinical relationships. Results We have designed and implemented an ML-based system for relation extraction, using support vector machines, and trained and tested it on a corpus of oncology narratives hand-annotated with clinically important relationships. Over a class of seven relation types, the system achieves an average F1 score of 72%, only slightly behind an indicative measure of human inter annotator agreement on the same task. We investigate the effectiveness of different features for this task, how extraction performance varies between inter- and intra-sentential relationships, and examine the amount of training data needed to learn various relationships. Conclusion We have shown that it is possible to extract important clinical relationships from text, using supervised statistical ML techniques, at levels of accuracy approaching those of human annotators. Given the importance of relation extraction as an enabling technology for text mining and given also the ready adaptability of systems based on our supervised learning approach to other clinical relationship extraction tasks, this result has significance for clinical text mining more generally, though further work to confirm our encouraging results should be carried out on a larger sample of narratives and relationship types

OpenDMAP: An open source, ontology-driven concept analysis engine, with applications to capturing knowledge regarding protein transport, protein interactions and cell-type-specific gene expression

<p>Abstract</p> <p>Background</p> <p>Information extraction (IE) efforts are widely acknowledged to be important in harnessing the rapid advance of biomedical knowledge, particularly in areas where important factual information is published in a diverse literature. Here we report on the design, implementation and several evaluations of OpenDMAP, an ontology-driven, integrated concept analysis system. It significantly advances the state of the art in information extraction by leveraging knowledge in ontological resources, integrating diverse text processing applications, and using an expanded pattern language that allows the mixing of syntactic and semantic elements and variable ordering.</p> <p>Results</p> <p>OpenDMAP information extraction systems were produced for extracting protein transport assertions (transport), protein-protein interaction assertions (interaction) and assertions that a gene is expressed in a cell type (expression). Evaluations were performed on each system, resulting in F-scores ranging from .26 – .72 (precision .39 – .85, recall .16 – .85). Additionally, each of these systems was run over all abstracts in MEDLINE, producing a total of 72,460 transport instances, 265,795 interaction instances and 176,153 expression instances. </p> <p>Conclusion</p> <p>OpenDMAP advances the performance standards for extracting protein-protein interaction predications from the full texts of biomedical research articles. Furthermore, this level of performance appears to generalize to other information extraction tasks, including extracting information about predicates of more than two arguments. The output of the information extraction system is always constructed from elements of an ontology, ensuring that the knowledge representation is grounded with respect to a carefully constructed model of reality. The results of these efforts can be used to increase the efficiency of manual curation efforts and to provide additional features in systems that integrate multiple sources for information extraction. The open source OpenDMAP code library is freely available at <url>http://bionlp.sourceforge.net/</url></p

Corpus annotation for mining biomedical events from literature

<p>Abstract</p> <p>Background</p> <p>Advanced Text Mining (TM) such as semantic enrichment of papers, event or relation extraction, and intelligent Question Answering have increasingly attracted attention in the bio-medical domain. For such attempts to succeed, text annotation from the biological point of view is indispensable. However, due to the complexity of the task, semantic annotation has never been tried on a large scale, apart from relatively simple term annotation.</p> <p>Results</p> <p>We have completed a new type of semantic annotation, event annotation, which is an addition to the existing annotations in the GENIA corpus. The corpus has already been annotated with POS (Parts of Speech), syntactic trees, terms, etc. The new annotation was made on half of the GENIA corpus, consisting of 1,000 Medline abstracts. It contains 9,372 sentences in which 36,114 events are identified. The major challenges during event annotation were (1) to design a scheme of annotation which meets specific requirements of text annotation, (2) to achieve biology-oriented annotation which reflect biologists' interpretation of text, and (3) to ensure the homogeneity of annotation quality across annotators. To meet these challenges, we introduced new concepts such as Single-facet Annotation and Semantic Typing, which have collectively contributed to successful completion of a large scale annotation.</p> <p>Conclusion</p> <p>The resulting event-annotated corpus is the largest and one of the best in quality among similar annotation efforts. We expect it to become a valuable resource for NLP (Natural Language Processing)-based TM in the bio-medical domain.</p

Text Mining Improves Prediction of Protein Functional Sites

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions

IceCube-Gen2: A Vision for the Future of Neutrino Astronomy in Antarctica

20 pages, 12 figures. Address correspondence to: E. Blaufuss, F. Halzen, C. Kopper (Changed to add one missing author, no other changes from initial version.)20 pages, 12 figures. Address correspondence to: E. Blaufuss, F. Halzen, C. Kopper (Changed to add one missing author, no other changes from initial version.)20 pages, 12 figures. Address correspondence to: E. Blaufuss, F. Halzen, C. Kopper (Changed to add one missing author, no other changes from initial version.)The recent observation by the IceCube neutrino observatory of an astrophysical flux of neutrinos represents the "first light" in the nascent field of neutrino astronomy. The observed diffuse neutrino flux seems to suggest a much larger level of hadronic activity in the non-thermal universe than previously thought and suggests a rich discovery potential for a larger neutrino observatory. This document presents a vision for an substantial expansion of the current IceCube detector, IceCube-Gen2, including the aim of instrumenting a $10\,\mathrm{km}^3$ volume of clear glacial ice at the South Pole to deliver substantial increases in the astrophysical neutrino sample for all flavors. A detector of this size would have a rich physics program with the goal to resolve the sources of these astrophysical neutrinos, discover GZK neutrinos, and be a leading observatory in future multi-messenger astronomy programs

The IceCube Neutrino Observatory - Contributions to ICRC 2017 Part VI: IceCube-Gen2, the Next Generation Neutrino Observatory

Papers on research & development towards IceCube-Gen2, the next generation neutrino observatory at South Pole, submitted to the 35th International Cosmic Ray Conference (ICRC 2017, Busan, South Korea) by the IceCube-Gen2 Collaboration

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta