DYNIQX: A novel meta-search engine for the web

The effect of metadata in collection fusion has not been sufficiently studied. In response to this, we present a novel meta-search engine called Dyniqx for metadata based search. Dyniqx integrates search results from search services of documents, images, and videos for generating a unified list of ranked search results. Dyniqx exploits the availability of metadata in search services such as PubMed, Google Scholar, Google Image Search, and Google Video Search etc for fusing search results from heterogeneous search engines. In addition, metadata from these search engines are used for generating dynamic query controls such as sliders and tick boxes etc which are used by users to filter search results. Our preliminary user evaluation shows that Dyniqx can help users complete information search tasks more efficiently and successfully than three well known search engines respectively. We also carried out one controlled user evaluation of the integration of six document/image/video based search engines (Google Scholar, PubMed, Intute, Google Image, Yahoo Image, and Google Video) in Dyniqx. We designed a questionnaire for evaluating different aspect of Dyniqx in assisting users complete search tasks. Each user used Dyniqx to perform a number of search tasks before completing the questionnaire. Our evaluation results confirm the effectiveness of the meta-search of Dyniqx in assisting user search tasks, and provide insights into better designs of the Dyniqx' interface

PubServer: literature searches by homology.

PubServer, available at http://pubserver.burnham.org/, is a tool to automatically collect, filter and analyze publications associated with groups of homologous proteins. Protein entries in databases such as Entrez Protein database at NCBI contain information about publications associated with a given protein. The scope of these publications varies a lot: they include studies focused on biochemical functions of individual proteins, but also reports from genome sequencing projects that introduce tens of thousands of proteins. Collecting and analyzing publications related to sets of homologous proteins help in functional annotation of novel protein families and in improving annotations of well-studied protein families or individual genes. However, performing such collection and analysis manually is a tedious and time-consuming process. PubServer automatically collects identifiers of homologous proteins using PSI-Blast, retrieves literature references from corresponding database entries and filters out publications unlikely to contain useful information about individual proteins. It also prepares simple vocabulary statistics from titles, abstracts and MeSH terms to identify the most frequently occurring keywords, which may help to quickly identify common themes in these publications. The filtering criteria applied to collected publications are user-adjustable. The results of the server are presented as an interactive page that allows re-filtering and different presentations of the output

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

Science Concierge: A fast content-based recommendation system for scientific publications

Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.Comment: 12 pages, 5 figure

A multilayer network approach for guiding drug repositioning in neglected diseases

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature.Fil: Berenstein, Ariel José. Fundación Instituto Leloir; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Física; ArgentinaFil: Magariños, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Chernomoretz, Ariel. Fundación Instituto Leloir; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Física; ArgentinaFil: Fernandez Aguero, Maria Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentin