39 research outputs found
Which gene did you mean?
Computational Biology needs computer-readable information records. Increasingly, meta-analysed and pre-digested information is being used in the follow up of high throughput experiments and other investigations that yield massive data sets. Semantic enrichment of plain text is crucial for computer aided analysis. In general people will think about semantic tagging as just another form of text mining, and that term has quite a negative connotation in the minds of some biologists who have been disappointed by classical approaches of text mining. Efforts so far have tried to develop tools and technologies that retrospectively extract the correct information from text, which is usually full of ambiguities. Although remarkable results have been obtained in experimental circumstances, the wide spread use of information mining tools is lagging behind earlier expectations. This commentary proposes to make semantic tagging an integral process to electronic publishing
Mining microarray datasets aided by knowledge stored in literature
DNA microarray technology produces large amounts of data. For data mining
of these datasets, background information on genes can be helpful.
Unfortunately most information is stored in free text. Here, we present an
approach to use this information for DNA microarray data mining
Towards the tipping point of FAIR implementation
This article explores the global implementation of the FAIR Guiding Principles for scientific management and data stewardship, which provide that data should be findable, accessible, interoperable and reusable. The implementation of these principles is designed to lead to the stewardship of data as FAIR digital objects and the establishment of the Internet of FAIR Data and Services (IFDS). If implementation reaches a tipping point, IFDS has the potential to revolutionize how data is managed by making machine and human readable data discoverable for reuse. Accordingly, this article examines the expansion of the implementation of FAIR Guiding Principles, especially how and in which geographies (locations) and areas (topic domains) implementation is taking place. A literature review of academic articles published between 2016 and 2019 on the use of FAIR Guiding Principles is presented. The investigation also includes an analysis of the domains in the IFDS Implementation Networks (INs). Its uptake has been mainly in the Western hemisphere. The investigation found that implementation of FAIR Guiding Principles has taken firm hold in the domain of bio and natural sciences. To achieve a tipping point for FAIR implementation, is now time to ensure the inclusion of non-European ascendants and of other scientific domains. Apart from equal opportunity and genuine global partnership issues, a permanent European bias poses challenges with regard to the representativeness and validity of data and could limit the potential of IFDS to reach across continental boundaries. The article concludes that, despite efforts to be inclusive, acceptance of the FAIR Guiding Principles and IFDS in different scientific communities is limited and there is a need to act now to prevent dampening of the momentum in the development and implementation of the IFDS. It is further concluded that policy entrepreneurs and the GO FAIR INs may contribute to making the FAIR Guiding Principles more flexible in including different research epistemologies, especially through its GO CHANGE pillar. LIACS-Managemen
Literature-aided meta-analysis of microarray data: a compendium study on muscle development and disease
Background: Comparative analysis of expression microarray studies is difficult due to the large influence of technical factors on experimental outcome. Still, the identified differentially expressed genes may hint at the same biological processes. However, manually curated assignment of genes to biological processes, such as pursued by the Gene Ontology (GO) consortium, is incomplete and limited. We hypothesised that automatic association of genes with biological processes through thesaurus-controlled mining of Medline abstracts would be more effective. Therefore, we developed a novel algorithm (LAMA: Literature-Aided Meta-Analysis) to quantify the similarity between transcriptomics studies. We evaluated our algorithm on a large compendium of 102 microarray studies published in the field of muscle development and disease, and compared it to similarity measures based on gene overlap and over-representation of biological processes assigned by GO. Results: While the overlap in both genes and overrepresented GO-terms was poor, LAMA retrieved many more biologically meaningful links between studies, with substantially lower influence of technical factors. LAMA correctly grouped muscular dystrophy, regeneration and myositis studies, and linked patient and corresponding mouse model studies. LAMA also retrieves the connecting biological concepts. Among other new discoveries, we associated cullin proteins, a class of ubiquitinylation proteins, with genes down-regulated during muscle regeneration, whereas ubiquitinylation was previously reported to be activated during the inverse process: muscle atrophy. Conclusion: Our literature-based association analysis is capable of finding hidden common biological denominators in microarray studies, and circumvents the need for raw data analysis or curated gene annotation databases
Ambiguity of human gene symbols in LocusLink and MEDLINE: creating an inventory and a disambiguation test collection
Genes are discovered almost on a daily basis and new names have to be
found. Although there are guidelines for gene nomenclature, the naming
process is highly creative. Human genes are often named with a gene symbol
and a longer, more descriptive term; the short form is very often an
abbreviation of the long form. Abbreviations in biomedical language are
highly ambiguous, i.e., one gene symbol often refers to more than one
gene.Using an existing abbreviation expansion algorithm,we explore MEDLINE
for the use of human gene symbols derived from LocusLink. It turns out
that just over 40% of these symbols occur in MEDLINE, however, many of
these occurrences are not related to genes. Along the process of making an
inventory, a disambiguation test collection is constructed automatically
Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes
MOTIVATION: The advent of high-throughput experiments in molecular biology creates a need for methods to efficiently extract and use information for large numbers of genes. Recently, the associative concept space (ACS) has been developed for the representation of information extracted from biomedical literature. The ACS is a Euclidean space in which thesaurus concepts are positioned and the distances between concepts indicates their relatedness. The ACS uses co-occurrence of concepts as a source of information. In this paper we evaluate how well the system can retrieve functionally related genes and we compare its performance with a simple gene co-occurrence method. RESULTS: To assess the performance of the ACS we composed a test set of five groups of functionally related genes. With the ACS good scores were obtained for four of the five groups. When compared to the gene co-occurrence method, the ACS is capable of revealing more functional biological relations and can achieve results with less literature available per gene. Hierarchical clustering was performed on the ACS output, as a potential aid to users, and was found to provide useful clusters. Our results suggest that the algorithm can be of value for researchers studying large numbers of genes. AVAILABILITY: The ACS program is available upon request from the authors
Using contextual queries
Search engines generally treat search requests in isolation. The results
for a given query are identical, independent of the user, or the context
in which the user made the request. An approach is demonstrated that
explores implicit contexts as obtained from a document the user is
reading. The approach inserts into an original (web) document
functionality to directly activate context driven queries that yield
related articles obtained from various information sources
Text mining for biology - the way forward: opinions from leading scientists
This article collects opinions from leading scientists about how text mining can provide better access to the biological literature, how the scientific community can help with this process, what the next steps are, and what role future BioCreative evaluations can play. The responses identify several broad themes, including the possibility of fusing literature and biological databases through text mining; the need for user interfaces tailored to different classes of users and supporting community-based annotation; the importance of scaling text mining technology and inserting it into larger workflows; and suggestions for additional challenge evaluations, new applications, and additional resources needed to make progress
The case for open science: rare diseases.
The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally
The implicitome: A resource for rationalizing gene-disease associations
High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing