Collaborative development of the Arrowsmith two node search interface designed for laboratory investigators.

Arrowsmith is a unique computer-assisted strategy designed to assist investigators in detecting biologically-relevant connections between two disparate sets of articles in Medline. This paper describes how an inter-institutional consortium of neuroscientists used the UIC Arrowsmith web interface http://arrowsmith.psych.uic.edu in their daily work and guided the development, refinement and expansion of the system into a suite of tools intended for use by the wider scientific community

Discovering information from an integrated graph database

The information explosion in science has become a different problem, not the sheer amount per se, but the multiplicity and heterogeneity of massive sets of data sources. Relations mined from these heterogeneous sources, namely texts, database records, and ontologies have been mapped to Resource Description Framework (RDF) triples in an integrated database. The subject and object resources are expressed as references to concepts in a biomedical ontology consisting of the Unified Medical Language System (UMLS), UniProt and EntrezGene and for the predicate resource to a predicate thesaurus. All RDF triples have been stored in a graph database, including provenance. For evaluation we used an actual formal PRISMA literature study identifying 61 cerebral spinal fluid biomarkers and 200 blood biomarkers for migraine. These biomarkers sets could be retrieved with weighted mean average precision values of 0.32 and 0.59, respectively, and can be used as a first reference for further refinements

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

Report of the Stanford Linked Data Workshop

The Stanford University Libraries and Academic Information Resources (SULAIR) with the Council on Library and Information Resources (CLIR) conducted at week-long workshop on the prospects for a large scale, multi-national, multi-institutional prototype of a Linked Data environment for discovery of and navigation among the rapidly, chaotically expanding array of academic information resources. As preparation for the workshop, CLIR sponsored a survey by Jerry Persons, Chief Information Architect emeritus of SULAIR that was published originally for workshop participants as background to the workshop and is now publicly available. The original intention of the workshop was to devise a plan for such a prototype. However, such was the diversity of knowledge, experience, and views of the potential of Linked Data approaches that the workshop participants turned to two more fundamental goals: building common understanding and enthusiasm on the one hand and identifying opportunities and challenges to be confronted in the preparation of the intended prototype and its operation on the other. In pursuit of those objectives, the workshop participants produced:1. a value statement addressing the question of why a Linked Data approach is worth prototyping;2. a manifesto for Linked Libraries (and Museums and Archives and …);3. an outline of the phases in a life cycle of Linked Data approaches;4. a prioritized list of known issues in generating, harvesting & using Linked Data;5. a workflow with notes for converting library bibliographic records and other academic metadata to URIs;6. examples of potential “killer apps” using Linked Data: and7. a list of next steps and potential projects.This report includes a summary of the workshop agenda, a chart showing the use of Linked Data in cultural heritage venues, and short biographies and statements from each of the participants

Using the literature based discovery research method in a context of built Environment research

For two disparate research groups, unaware of each other's work, one group can inadvertently solve a problem prevalent in the other. Without considering work from both groups together, such breakthroughs may remain undiscovered. The solution is literature based discovery (LBD), a method which involves investigation or search for novel hypotheses connecting work from two or more disparate contexts. However, LBD has predominantly been used to address medical problems, and its uptake outside medical research remains scanty. In the context of built environment research, there are countable studies that have claimed using LBD and moreover, they presented sparse details. On one hand, studies that have claimed using LBD as a research method seem to confuse it with traditional literature reviews, and on the other hand, even those that could have used LBD seem unaware that they used some kind of LBD-style analysis. Following the original principles of LBD, this paper presents an LBD-inspired research method and a demonstration of its applicability within a built environment research context. The findings indicate promising leads to encouraging LBD and elucidating several misconceptions surrounding its use in built environment research. It is hoped that this paper will encourage future research in built environment, like construction management research, to confidently use LBD appropriately and consciously

MOLIERE: Automatic Biomedical Hypothesis Generation System

Hypothesis generation is becoming a crucial time-saving technique which allows biomedical researchers to quickly discover implicit connections between important concepts. Typically, these systems operate on domain-specific fractions of public medical data. MOLIERE, in contrast, utilizes information from over 24.5 million documents. At the heart of our approach lies a multi-modal and multi-relational network of biomedical objects extracted from several heterogeneous datasets from the National Center for Biotechnology Information (NCBI). These objects include but are not limited to scientific papers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses using Latent Dirichlet Allocation applied on abstracts found near shortest paths discovered within this network, and demonstrate the effectiveness of MOLIERE by performing hypothesis generation on historical data. Our network, implementation, and resulting data are all publicly available for the broad scientific community

MOLIERE: Automatic Biomedical Hypothesis Generation System

Hypothesis generation is becoming a crucial time-saving technique which allows biomedical researchers to quickly discover implicit connections between important concepts. Typically, these systems operate on domain-specific fractions of public medical data. MOLIERE, in contrast, utilizes information from over 24.5 million documents. At the heart of our approach lies a multi-modal and multi-relational network of biomedical objects extracted from several heterogeneous datasets from the National Center for Biotechnology Information (NCBI). These objects include but are not limited to scientific papers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses using Latent Dirichlet Allocation applied on abstracts found near shortest paths discovered within this network, and demonstrate the effectiveness of MOLIERE by performing hypothesis generation on historical data. Our network, implementation, and resulting data are all publicly available for the broad scientific community

The combination of the disciplines of Techmining and semantic TRIZ for better and faster analyzing technology evolution

Tesis por compendioThe purpose of the present thesis is to explore and to demonstrate how the combination of two methodological approaches, text mining plus the systemic vision of TRIZ empowered by semantics, can bring a larger and more comprehensive analysis of the evolution of a technology. Both approaches had been not combined before the first of the four papers constituents of the present thesis based in a compendium of publications. However, this combination applied to the evolution of technologies is increasingly being published in the scientific literature. Such combination shows a second benefit in the form of an improvement in accessing and connecting knowledge from disparate scientific literatures in a systematic manner. The common element in all these papers is the use of the technology mining approach, 'techmining', the application of text mining techniques based on technology management knowledge, combined with the use of semantic TRIZ, the advantage of syntactic applied to the systemic vision of TRIZ. These papers show that a better analysis of evolving technologies, e.g. by profiling technologies from a systemic point of view or, a better access to knowledge, e.g. by semantically connecting concepts with meaning, can be achieved. The research on applying the combination of these approaches to scientific and technological information analysis explores the advantages and new possibilities for technology trends assessment as well as the semantic connection of concepts which represents a change in the way information research can be done. The different applications of the aforementioned combination are explored by means of the here presented articles. The structure followed in this research is the collection of three papers published in international academic journals indexed in the most prestigious databases and one chapter in a proceedings book of an international congress. The attached articles show the research undertaken to demonstrate the aforementioned benefits of the proposed combination. Despite it can be found many methods and approaches about the assessment of the evolution of technologies, distributed across the literature, there is still a need to better understand which technologies may emerge, which may evolve faster and at what pace can they reach the market. The combination of the techmining approach and the semantic TRIZ approaches allows understanding the trends enriched with a systemic vision of the links, functions, and influences of constituent and enabling elements of a technology. Such systemic link of elements with its components and ecosystem also allows for a multi-dimensional view of a technology and further reduces the uncertainty to preview the progress of a technology. The papers presented in this dissertation are based on the combination of the TRIZ methodology, the techmining approach and the semantic TRIZ approach, applied to different technologies in different domains, to proof the advantages and implications of the combination. The articles try the different interactions of the combined approaches, applied to the assessment of different technologies, such as lithium batteries for the electric car, a medical case linked to a disease known as Meniére's Disease, the prognosis of prostate cancer, and the usage of probiotics as substitutes of antibiotics in the animal health. The wide range of technologies was selected to show the clear benefits of either combining the two approaches or applying predominantly one of them in the case of the Meniére's disease article. That difference in the nature of technologies also helped to better understand the systemic point of view of the technology, exploring new applications based on the general system theory from Bertalanffy as well as other related approaches about technologies.El propósito de la presente tesis es la exploración y la demostración de la combinación de dos enfoques metodológicos, la minería de textos y la visión sistémica de TRIZ reforzada con la semántica, pueden aportar un mayor y mas exhaustivo análisis de la evolución de una tecnología. Ambos enfoques no habían sido combinados antes del primero de los cuatro artículos que representan esta tesis por compendio de publicaciones, aunque dicha combinación ha sido crecientemente publicada en la literatura científica, para multiples propósitos desde entonces. Un segundo aporte proporcionado por esta combinación es la mejora de la capacidad de acceso al conocimiento y cómo ello supone un avance para el descubrimiento a través de literaturas no relacionadas "disparate literature discovery" de una forma metódica y científica. El elemento común en los artículos aquí presentados es el aprovechamiento de techmining, esto es, la minería de textos con base en la gestión tecnológica, por ejemplo mediante el perfilado de tecnologías, junto al enfoque de la metodología TRIZ potenciada por el análisis sintáctico y semántico, esto es, mediante la conexión semántica de conceptos, para un análisis más completo de la evolución tecnológica, proporcionando al mismo tiempo un acceso más racional al conocimiento. La investigación sobre la aplicación de la citada combinación al análisis de información científica y tecnológica explora las ventajas y nuevas posibilidades en la evaluación del avance de la tecnología, así como la conexión semántica de conceptos que representa nuevas posibilidades en la forma en que la investigación textual puede hacerse. La estructura de la investigación aquí presentada se muestra a través de los artículos publicados en revistas internacionales de alto impacto y el capítulo de los 'proceedings' de un congreso internacional. Dichos artículos muestran la investigación llevada a cabo para demostrar los beneficios mencionados de la combinación propuesta. A pesar de la gran actividad de investigación y de la existencia de varios enfoques para la prospectiva y la previsión tecnológica presentes en la literatura científica, existe aún la necesidad de entender qué tecnologías pueden emerger, pueden evolucionar más rápido y a qué velocidad pueden llegar al mercado. La combinación de los enfoques de minería tecnológica o techmining y TRIZ semántico permite entender las tendencias de una tecnología dada, enriquecida con una visión de su sistémica, y teniendo en cuenta las conexiones de sus elementos y las influencias de sus elementos constituyentes. Tal conexión entre los components y su entorno permite una vision multidimensional de la tecnología reduciendo más aún la incertidumbre en la previsión de la evolución de una tecnología. Los artículos presentados en esta tesis son aplicaciones y exploraciones de la combinación de mencionada, a diferentes tecnologías de diversos ámbitos muy dispares entre sí, con el fin de demostrar sus ventajas e implicaciones. Los artículos tratan las diferentes interacciones entre ambos enfoques de trabajo, aplicados a tecnologías como baterías de litio para los vehículos eléctricos, un caso médico ligado a una dolencia como el síndrome de Méniere, a la prognosis del cáncer de próstata y al uso de probióticos en la alimentación animal como sustitución de los antibióticos. Este amplio rango de tecnologías han sido seleccionados para mostrar las ventajas, de forma más objetiva, de la combinación de ambos enfoques o con predominancia de alguno en particular, como es el caso del artículo explorando el síndrome de Méniere. Estas exploraciones permiten también entender mejor el punto de vista sistémico de una tecnología, descubriendo nuevas aplicaciones basadas en la teoría general de sistemas de Bertalanffy así como en otros enfoques relacionados.El propòsit de la present tesi és l'exploració i la demostració de la combinació de dos enfocaments metodològics, la minería de textes i la visió sistémica de TRIZ, reforçada amb la sintáctica i la semántica, mostrant que poden oferir un abast més gran i més holístic en l'enteniment de l'evolució d'una tecnología. Tots dos enfocaments no habían estat combinats abans del primer article dels quatre que composen aquesta tesi, però creixentment combinat dins la literatura científica per a múltiples propostes des de la primera publicació. Una segona aportació proporcionada per aquesta combinació és la millora de la capacitat d'accés al coneixement, i de com això suposa un avanç en l'àrea de recerca a traves de literatures no relacionades "disparate literature discovery" d'una forma metòdica i científica. L'element comú en els articles presentats en aquesta tesi és l'aprofitament de la mineria de textos amb base en la gestió tecnològica, 'techmining', per exemple mitjançant el perfilat de tecnologies, al costat de l'enfocament de la metodologia TRIZ potenciada per l'anàlisi sintàctica i semàntica, mitjançant la conexión semántica de conceptes, per assolir un anàlisi més complet de l'evolució tecnològica, així com per a garantir un accés més racional al coneixement. La investigació de l'aplicació de la combinació dels dos enfocaments a l'anàlisi d'informació científica i tecnològica realizat, exploren els avantatges i noves possibilitats en l'avaluació de l'avanç de tecnologies, així com la conexión de conceptes uqe representa noves possibilitats en la forma en què la investigació textual pot fer-se. L'estructura de la investigació ací presentada es mostra a través dels articles publicats i el capítol dels 'proceedings' d'un congrés internacional. Aquests articles mostren la investigació duta a terme per demostrar els beneficis esmentats. Tot i la gran activitat de recerca i enfocaments per a la prospectiva i la previsió tecnològica existents a la literatura científica, existeix encara la necessitat d'entendre quines tecnologies poden emergir, poden evolucionar més ràpid i a quina velocitat poden arribar al mercat. La combinació dels enfocaments de mineria tecnològica o 'techmining' i TRIZ semàntic permet entendre les tendències d'una tecnologia donada, amb una visió del seu sistema, les connexions dels seus elements i les influències dels elements constituents. Els articles presentats en aquesta tesi són aplicacions i exploracions de la combinació de la metodologia TRIZ, la seva potenciació mitjançant la semàntica i el techmining a diferents tecnologies de diversos àmbits, alguns molt dispars entre si, per tal de demostrar les seves avantatges i implicacions. Els articles tracten les diferents interaccions entre els dos enfocaments de treball, aplicats a tecnologies com bateries de liti per als vehicles elèctrics, un cas mèdic lligat a una malaltia com la síndrome de Ménière, a la prognosi del càncer de pròstata i en alimentació, a l'ús de probiòtics en l'alimentació animal com a substitució dels antibiòtics. Aquest ampli rang de tecnologies han estat seleccionats per mostrar els avantatges de forma més objectiva, de la combinació de tots dos enfocaments o amb predominança d'algun en particular, com és el cas de l'article explorant la síndrome de Ménière. Aquestes exploracions permeten també entendre millor el punt de vista sistèmic d'una tecnologia, descobrint noves aplicacions amb base en la teoria general de sistemes de Bertalanffy així com altres treballs relacionats.Vicente Gomila, JM. (2017). The combination of the disciplines of Techmining and semantic TRIZ for better and faster analyzing technology evolution [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/89088TESISCompendi