Making fundamental scientific discoveries by combining information from literature, databases, and computational tools - An example.
Ontology highlight
ABSTRACT:
SUBMITTER: Stielow B
PROVIDER: S-EPMC8175269 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Systematically evaluating scientific literature is a time consuming endeavor that requires hours of coding and rating. Here, we describe a method to distribute these tasks across a large group through online crowdsourcing. Using Amazon's Mechanical Turk, crowdsourced workers (microworkers) completed four groups of tasks to evaluate the question, "Do nutrition-obesity studies with conclusions concordant with popular opinion receive more attention in the scientific community than do those that are discordant?" 1) Microworkers who passed a qualification test (19% passed) evaluated abstracts to determine if they were about human studies investigating nutrition and obesity. Agreement between the first two raters' conclusions was moderate (κ = 0.586), with consensus being reached in 96% of abstracts. 2) Microworkers iteratively synthesized free-text answers describing the studied foods into one coherent term. Approximately 84% of foods were agreed upon, with only 4 and 8% of ratings failing manual review in different steps. 3) Microworkers were asked to rate the perceived obesogenicity of the synthesized food terms. Over 99% of responses were complete and usable, and opinions of the microworkers qualitatively matched the authors' expert expectations (e.g., sugar-sweetened beverages were thought to cause obesity and fruits and vegetables were thought to prevent obesity). 4) Microworkers extracted citation counts for each paper through Google Scholar. Microworkers reached consensus or unanimous agreement for all successful searches. To answer the example question, data were aggregated and analyzed, and showed no significant association between popular opinion and attention the paper received as measured by Scimago Journal Rank and citation counts. Direct microworker costs totaled $221.75, (estimated cost at minimum wage: $312.61). We discuss important points to consider to ensure good quality control and appropriate pay for microworkers. With good reliability and low cost, crowdsourcing has potential to evaluate published literature in a cost-effective, quick, and reliable manner using existing, easily accessible resources.
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Project description:Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.
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Project description:The paper proposes a new method for conducting a literature review by structured data of more than 2200 scientific articles and 1300 researchers on SSHPA (Social Sciences and Humanities Peer Awards), an open database of Vietnamese social scientists' scientific productivity. Based on the logical structure of SSHPA, the authors create a specialized database for the literature review: SDA (SSHPA Data Analysis). Combining expert's caliber and computational algorithms, SDA is expected to offer an immensely efficient and analytical based method of scanning data, hence ameliorating the traditional approach to conducting a literature review.•A specialized database for literature review is created using the scientific articles and author profiles from SSHPA, an open database of Vietnamese social scientists' productivity.•The review database assigns values of topics or methodological attributes to articles sourced from SSHPA.•Then, the authors can query comprehensive data tables, graphs, or diagrams to use for literature review.
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Project description:BackgroundWeb-based, free-text documents on science and technology have been increasing growing on the web. However, most of these documents are not immediately processable by computers slowing down the acquisition of useful information. Computational ontologies might represent a possible solution by enabling semantically machine readable data sets. But, the process of ontology creation, instantiation and maintenance is still based on manual methodologies and thus time and cost intensive.MethodWe focused on a large corpus containing information on researchers, research fields, and institutions. We based our strategy on traditional entity recognition, social computing and correlation. We devised a semi automatic approach for the recognition, correlation and extraction of named entities and relations from textual documents which are then used to create, instantiate, and maintain an ontology.ResultsWe present a prototype demonstrating the applicability of the proposed strategy, along with a case study describing how direct and indirect relations can be extracted from academic and professional activities registered in a database of curriculum vitae in free-text format. We present evidence that this system can identify entities to assist in the process of knowledge extraction and representation to support ontology maintenance. We also demonstrate the extraction of relationships among ontology classes and their instances.ConclusionWe have demonstrated that our system can be used for the conversion of research information in free text format into database with a semantic structure. Future studies should test this system using the growing number of free-text information available at the institutional and national levels.
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Project description:Hemophilia is an inherited blood coagulation disorder caused by mutations on the coagulation factors VIII or IX genes. Although it is a relatively rare disease, the research community is actively working on this topic, producing almost 6000 manuscripts in the last 5 years. Given that the scientific literature is increasing so rapidly, even the most avid reader will find it difficult to follow it closely. In this study, we used sophisticated computational techniques to map the hemophilia literature of the last 60 years. We created a network structure to represent authorship collaborations, where the nodes are the researchers and 2 nodes are connected if they co-authored a manuscript. We accurately identified author clusters, namely, researchers who have collaborated systematically for several years, and used text mining techniques to automatically synthesize their research specialties. Overall, this study serves as a historical appreciation of the effort of thousands of hemophilia researchers and demonstrates that a computational framework is able to automatically identify collaboration networks and their research specialties. Importantly, we made all datasets and source code available for the community, and we anticipate that the methods introduced here will pave the way for the development of systems that generate compelling hypothesis based on patterns that are imperceptible to human researchers.
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Project description:Science is a social process with far-reaching impact on our modern society. In recent years, for the first time we are able to scientifically study the science itself. This is enabled by massive amounts of data on scientific publications that is increasingly becoming available. The data is contained in several databases such as Web of Science or PubMed, maintained by various public and private entities. Unfortunately, these databases are not always consistent, which considerably hinders this study. Relying on the powerful framework of complex networks, we conduct a systematic analysis of the consistency among six major scientific databases. We found that identifying a single "best" database is far from easy. Nevertheless, our results indicate appreciable differences in mutual consistency of different databases, which we interpret as recipes for future bibliometric studies.
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Project description:A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present a computational method that leverages the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in the analysis of gene expression data offers an opportunity to incorporate functional information about the genes when defining expression clusters. We have created a method that associates gene expression profiles with known biological functions. Our method has two steps. First, we apply hierarchical clustering to the given gene expression data set. Secondly, we use text from abstracts about genes to (i) resolve hierarchical cluster boundaries to optimize the functional coherence of the clusters and (ii) recognize those clusters that are most functionally coherent. In the case where a gene has not been investigated and therefore lacks primary literature, articles about well-studied homologous genes are added as references. We apply our method to two large gene expression data sets with different properties. The first contains measurements for a subset of well-studied Saccharomyces cerevisiae genes with multiple literature references, and the second contains newly discovered genes in Drosophila melanogaster; many have no literature references at all. In both cases, we are able to rapidly define and identify the biologically relevant gene expression profiles without manual intervention. In both cases, we identified novel clusters that were not noted by the original investigators.
| S-EPMC169898 | biostudies-literature
Project description:Generative models provide a well-established statistical framework for evaluating uncertainty and deriving conclusions from large data sets especially in the presence of noise, sparsity, and bias. Initially developed for computer vision and natural language processing, these models have been shown to effectively summarize the complexity that underlies many types of data and enable a range of applications including supervised learning tasks, such as assigning labels to images; unsupervised learning tasks, such as dimensionality reduction; and out-of-sample generation, such as de novo image synthesis. With this early success, the power of generative models is now being increasingly leveraged in molecular biology, with applications ranging from designing new molecules with properties of interest to identifying deleterious mutations in our genomes and to dissecting transcriptional variability between single cells. In this review, we provide a brief overview of the technical notions behind generative models and their implementation with deep learning techniques. We then describe several different ways in which these models can be utilized in practice, using several recent applications in molecular biology as examples.
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Project description:In recent years, mass spectrometry has emerged as a core component of fundamental discoveries in virology. As a consequence of their coevolution, viruses and host cells have established complex, dynamic interactions that function either in promoting virus replication and dissemination or in host defense against invading pathogens. Thus, viral infection triggers an impressive range of proteome changes. Alterations in protein abundances, interactions, posttranslational modifications, subcellular localizations, and secretion are temporally regulated during the progression of an infection. Consequently, understanding viral infection at the molecular level requires versatile approaches that afford both breadth and depth of analysis. Mass spectrometry is uniquely positioned to bridge this experimental dichotomy. Its application to both unbiased systems analyses and targeted, hypothesis-driven studies has accelerated discoveries in viral pathogenesis and host defense. Here, we review the contributions of mass spectrometry-based proteomic approaches to understanding viral morphogenesis, replication, and assembly and to characterizing host responses to infection.
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Project description:MotivationMetabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories.ResultsThe use of a Semantic Web framework on biological data allows us to apply ontological based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries.AvailabilityA web interface to browse and download the extracted relations, as well as a SPARQL endpoint to directly probe the whole FORUM knowledge graph, are available at https://forum-webapp.semantic-metabolomics.fr. The code needed to reproduce the triplestore is available at https://github.com/eMetaboHUB/Forum-DiseasesChem.Supplementary informationSupplementary data are available at Bioinformatics online.
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