Improving average ranking precision in user searches for biomedical research datasets

Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorisation method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries. Our system provides competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP among the participants, being +22.3% higher than the median infAP of the participant's best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system's performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. Our similarity measure algorithm seems to be robust, in particular compared to Divergence From Randomness framework, having smaller performance variations under different training conditions. Finally, the result categorization did not have significant impact on the system's performance. We believe that our solution could be used to enhance biomedical dataset management systems. In particular, the use of data driven query expansion methods could be an alternative to the complexity of biomedical terminologies

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Beyond the paywall

In dieser Dissertation untersuche ich die Forschungswege von sechs Wissenschaftlern, die in verschiedenen Disziplinen und Institutionen in den Vereinigten Staaten und in der Tschechischen Republik arbeiten. Um dies zu tun, verwende ich sogenannte „multi-sited“ ethnographisch-methodische Strategien (d.h. Strategien, die Anthropologen verwenden, um Kulturen an zwei oder mehr geografischen Standorten zu vergleichen), mit dem Ziel, informationsbezogene Verhaltensweisen dieser Wissenschaftler im global vernetzten akademischen Umfeld zu untersuchen, englisch abgekürzt „GNAE“, ein Begriff, der sich speziell auf die komplexe Bricolage von Netzwerkinfrastrukturen, Online-Informationsressourcen und Tools bezieht, die Wissenschaftler heutzutage nutzen, d.h. die weltweite akademische e-IS, oder akademische Infrastruktur (Edwards et al. 2013). Die zentrale Forschungsfrage (RQ1), die in dieser Dissertation beantwortet wird, ist: Gibt es, gemäß der multi-sited ethnographischen Analyse der beteiligten Wissenschaftler in dieser Studie—Personen, die Forschung in verschiedenen Disziplinen und Institutionen sowie an unterschiedlichen Standorten betreiben—Hinweise darauf, dass ein signifikanter Anteil der nicht-institutionellen/informellen informationsbezogenen Forschung über Mechanismen im GNAE, die nicht von Bibliotheken unterstützt werden, betrieben wird, sowie (RQ2): Was für Muster sind vorhanden und wie beziehen sie sich auf informationswissenschaftliche und andere sozialwissenschaftliche Theorien? Und drittens (RQ3): Haben die Resultate praxisnahe Bedeutungen für die Entwicklung von Dienstleistungen in wissenschaftlichen Bibliotheken? Ethnographische Strategien sind bisher noch nicht in der Informationswissenschaft (IS) eingesetzt worden, um Fragen dieser Art zu untersuchen. Die Ergebnisse zeigen, dass eine informelle Informationsexploration nur bei zwei Wissenschaftlern, die mit offenen Daten und Tools einer verteilten Computing-Infrastruktur arbeiten, zu finden ist.In this dissertation I examine the pathways of information exploration and discovery of six scientists working in different research disciplines affiliated with several academic institutions in the United States and in the Czech Republic. To do so, I utilize multi-sited ethnographic methodological strategies (i.e., strategies developed by anthropologists to compare cultures across two or more geographic locations) to examine the information-related behaviors of these scholars within the global networked academic environment (GNAE), a term which specifically refers to the complex bricolage of network infrastructures, online information resources, and tools scholars use to perform their research today (i.e., the worldwide academic e-IS, or academic infrastructure [Edwards et al. 2013]). The central research question (RQ1) to be answered in this dissertation: According to the multi-sited ethnographic analysis of scientists participating in this study—individuals conducting research in various disciplines at different institutions in several geographical locations—is there evidence indicating a significant allotment of non-institutional/informal information-related exploration and discovery occurring beyond official library-supported mechanisms in the GNAE?, and—part two (RQ2) of the central research question—What (if any) patterns are exhibited and how do these patterns relate to information science (IS) and other social science theories? Both RQ1 and RQ2 are exploratory. I additionally ask (RQ3): What might all this mean in the applied sense? by showing examples of services piloted during the research process in response to my observations in the field. Multi-sited ethnographic strategies have not yet been employed in IS, as of the date of publication of this thesis, to examine such questions. Results indicate informal information exploration occurring only with two scientists who use of open data and tools on a distributed computing infrastructure

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs. AI technologies, such as generative chemistry, machine learning, and multi-property optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.Comment: 30 pages, 4 figures, 184 reference