Logical Operation Based Literature Association with Genes and its application, PosMed.

PosMed prioritizes candidate genes for positional cloning by employing our original database search engine GRASE, which uses an inferential process similar to an artificial neural network comprising documental neurons (or 'documentrons') that represent each document contained in databases such as MEDLINE and OMIM (Yoshida, _et al_. 2009, Makita, _et al_. 2009). PosMed immediately ranks the candidate genes by connecting phenotypic keywords to the genes through connections representing gene–gene interactions other biological relationships, such as metabolite–gene, mutant mouse–gene, drug–gene, disease–gene, and protein–protein interactions, ortholog data, and gene–literature connections.

To make proper relationships between genes and literature, we manually curate queries, which are defined by logical operation rules, against MEDLINE. For example, to detect a set of MEDLINE documents for the AT1G03880 gene in _A. thaliana_, we applied the following logical query: (‘AT1G03880’ OR ‘CRU2’ OR ‘CRB’ OR ‘CRUCIFERIN 2' OR ‘CRUCIFERIN B’) AND (‘Arabidopsis’) NOT (‘chloroplast RNA binding’). Curators refined these queries in mouse, rice and _A. thaliana_. For human and rat genes, we use mouse curation results via ortholog genes in PosMed.

PosMed is available at "http://omicspace.riken.jp/PosMed":http://omicspace.riken.jp/PosMed

References:
Yoshida Y, et al. _Nucleic Acids Res_. 37(Web Server issue):W147-52. 2009. 
Makita Y, et al. _Plant Cell Physiol_. 2009 Jul;50(7):1249-59.
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Automated detection and classification of desmoplastic reaction at the colorectal tumour front using deep learning

Funding: This study was funded by Medical Research Scotland, the Japan Society for the Promotion of Science, the British Council and Indica Labs, Inc. who also provided in kind resource.The categorisation of desmoplastic reaction (DR) present at the colorectal cancer (CRC) invasive front into mature, intermediate or immature type has been previously shown to have high prognostic significance. However, the lack of an objective and reproducible assessment methodology for the assessment of DR has been a major hurdle to its clinical translation. In this study, a deep learning algorithm was trained to automatically classify immature DR on haematoxylin and eosin digitised slides of stage II and III CRC cases (n = 41). When assessing the classifier’s performance on a test set of patient samples (n = 40), a Dice score of 0.87 for the segmentation of myxoid stroma was reported. The classifier was then applied to the full cohort of 528 stage II and III CRC cases, which was then divided into a training (n = 396) and a test set (n = 132). Automatically classed DR was shown to have superior prognostic significance over the manually classed DR in both the training and test cohorts. The findings demonstrated that deep learning algorithms could be applied to assist pathologists in the detection and classification of DR in CRC in an objective, standardised and reproducible manner.Publisher PDFPeer reviewe

A flexible representation of omic knowledge for thorough analysis of microarray data

BACKGROUND: In order to understand microarray data reasonably in the context of other existing biological knowledge, it is necessary to conduct a thorough examination of the data utilizing every aspect of available omic knowledge libraries. So far, a number of bioinformatics tools have been developed. However, each of them is restricted to deal with one type of omic knowledge, e.g., pathways, interactions or gene ontology. Now that the varieties of omic knowledge are expanding, analysis tools need a way to deal with any type of omic knowledge. Hence, we have designed the Omic Space Markup Language (OSML) that can represent a wide range of omic knowledge, and also, we have developed a tool named GSCope3, which can statistically analyze microarray data in comparison with the OSML-formatted omic knowledge data. RESULTS: In order to test the applicability of OSML to represent a variety of omic knowledge specifically useful for analysis of Arabidopsis thaliana microarray data, we have constructed a Biological Knowledge Library (BiKLi) by converting eight different types of omic knowledge into OSML-formatted datasets. We applied GSCope3 and BiKLi to previously reported A. thaliana microarray data, so as to extract any additional insights from the data. As a result, we have discovered a new insight that lignin formation resists drought stress and activates transcription of many water channel genes to oppose drought stress; and most of the 20S proteasome subunit genes show similar expression profiles under drought stress. In addition to this novel discovery, similar findings previously reported were also quickly confirmed using GSCope3 and BiKLi. CONCLUSION: GSCope3 can statistically analyze microarray data in the context of any OSML-represented omic knowledge. OSML is not restricted to a specific data type structure, but it can represent a wide range of omic knowledge. It allows us to convert new types of omic knowledge into datasets that can be used for microarray data analysis with GSCope3. In addition to BiKLi, by collecting various types of omic knowledge as OSML libraries, it becomes possible for us to conduct detailed thorough analysis from various biological viewpoints. GSCope3 and BiKLi are available for academic users at our web site

Semantic-JSON: a lightweight web service interface for Semantic Web contents integrating multiple life science databases

Global cloud frameworks for bioinformatics research databases become huge and heterogeneous; solutions face various diametric challenges comprising cross-integration, retrieval, security and openness. To address this, as of March 2011 organizations including RIKEN published 192 mammalian, plant and protein life sciences databases having 8.2 million data records, integrated as Linked Open or Private Data (LOD/LPD) using SciNetS.org, the Scientists' Networking System. The huge quantity of linked data this database integration framework covers is based on the Semantic Web, where researchers collaborate by managing metadata across public and private databases in a secured data space. This outstripped the data query capacity of existing interface tools like SPARQL. Actual research also requires specialized tools for data analysis using raw original data. To solve these challenges, in December 2009 we developed the lightweight Semantic-JSON interface to access each fragment of linked and raw life sciences data securely under the control of programming languages popularly used by bioinformaticians such as Perl and Ruby. Researchers successfully used the interface across 28 million semantic relationships for biological applications including genome design, sequence processing, inference over phenotype databases, full-text search indexing and human-readable contents like ontology and LOD tree viewers. Semantic-JSON services of SciNetS.org are provided at http://semanticjson.org

Co-activation of macrophages and T cells contribute to chronic GVHD in human IL-6 transgenic humanised mouse model.

BACKGROUND: Graft-versus host disease (GVHD) is a complication of stem cell transplantation associated with significant morbidity and mortality. Non-specific immune-suppression, the mainstay of treatment, may result in immune-surveillance dysfunction and disease recurrence. METHODS: We created humanised mice model for chronic GVHD (cGVHD) by injecting cord blood (CB)-derived human CD34 FINDINGS: In cGVHD humanised mice, we found activation of T cells in the spleen, lung, liver, and skin, activation of macrophages in lung and liver, and loss of appendages in skin, obstruction of bronchioles in lung and portal fibrosis in liver recapitulating cGVHD. Acute GVHD humanised mice showed activation of T cells with skewed TCR repertoire without significant macrophage activation. INTERPRETATION: Using humanised mouse models, we demonstrated distinct immune mechanisms contributing acute and chronic GVHD. In cGVHD model, co-activation of human HSPC-derived macrophages and T cells educated in the recipient thymus contributed to delayed onset, multi-organ disease. In acute GVHD model, mature human T cells contained in the graft resulted in rapid disease progression. These humanised mouse models may facilitate future development of new molecular medicine targeting GVHD

PosMed: ranking genes and bioresources based on Semantic Web Association Study

ABSTRACT Positional MEDLINE (PosMed; http://biolo

The RIKEN integrated database of mammals

The RIKEN integrated database of mammals (http://scinets.org/db/mammal) is the official undertaking to integrate its mammalian databases produced from multiple large-scale programs that have been promoted by the institute. The database integrates not only RIKEN’s original databases, such as FANTOM, the ENU mutagenesis program, the RIKEN Cerebellar Development Transcriptome Database and the Bioresource Database, but also imported data from public databases, such as Ensembl, MGI and biomedical ontologies. Our integrated database has been implemented on the infrastructure of publication medium for databases, termed SciNetS/SciNeS, or the Scientists’ Networking System, where the data and metadata are structured as a semantic web and are downloadable in various standardized formats. The top-level ontology-based implementation of mammal-related data directly integrates the representative knowledge and individual data records in existing databases to ensure advanced cross-database searches and reduced unevenness of the data management operations. Through the development of this database, we propose a novel methodology for the development of standardized comprehensive management of heterogeneous data sets in multiple databases to improve the sustainability, accessibility, utility and publicity of the data of biomedical information

Safety confirmation of induced pluripotent stem cell-derived cardiomyocyte patch transplantation for ischemic cardiomyopathy: first three case reports

IntroductionWith the expected increase in patients with heart failure and ischemic 15 cardiomyopathy, the development of myocardial regenerative medicine using cell transplantation as a novel treatment method is progressing. This first-in-human clinical trial aimed to confirm the safety of cardiomyocyte patch transplantation derived from allogeneic induced pluripotent stem (iPS) cells based on the results of several preclinical studies.Study designThe inclusion criteria were left ventricular ejection fraction of 35% or less; heart failure symptoms of New York Heart Association class III or higher despite existing therapies such as revascularization; and a 1-year observation period that included a 3-month immunosuppressive drug administration period after transplantation of iPS cell-derived cardiomyocyte patches to evaluate adverse events, cardiac function, myocardial blood flow, heart failure symptoms, and immune response.ResultsIn the first three cases of this trial, no transplanted cell-related adverse events were observed during the 1-year observation period, and improvement in heart failure symptoms was observed. In addition, improvements in left ventricular contractility and myocardial blood flow were observed in two of the three patients. Regarding immune response, an increase in transplant cell-specific antibody titer was observed in all three patients after immunosuppressive drug administration. In one patient with poor improvement in cardiac function and myocardial blood flow, an increase in antibody titer against HLA-DQ was observed even before cell transplantation.ConclusionsOur case findings demonstrate that the transplantation of iPS cell-derived cardiomyocyte patches for ischemic cardiomyopathy can be safely performed; however, further investigation of the therapeutic effect and its relationship with an immune response is needed by accumulating the number of patients through continued clinical trials