16,403 research outputs found
A network approach for managing and processing big cancer data in clouds
Translational cancer research requires integrative analysis of multiple levels of big cancer data to identify and treat cancer. In order to address the issues that data is decentralised, growing and continually being updated, and the content living or archiving on different information sources partially overlaps creating redundancies as well as contradictions and inconsistencies, we develop a data network model and technology for constructing and managing big cancer data. To support our data network approach for data process and analysis, we employ a semantic content network approach and adopt the CELAR cloud platform. The prototype implementation shows that the CELAR cloud can satisfy the on-demanding needs of various data resources for management and process of big cancer data
Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning
Decision support is a probabilistic and quantitative method designed for
modeling problems in situations with ambiguity. Computer technology can be
employed to provide clinical decision support and treatment recommendations.
The problem of natural language applications is that they lack formality and
the interpretation is not consistent. Conversely, ontologies can capture the
intended meaning and specify modeling primitives. Disease Ontology (DO) that
pertains to cancer's clinical stages and their corresponding information
components is utilized to improve the reasoning ability of a decision support
system (DSS). The proposed DSS uses Case-Based Reasoning (CBR) to consider
disease manifestations and provides physicians with treatment solutions from
similar previous cases for reference. The proposed DSS supports natural
language processing (NLP) queries. The DSS obtained 84.63% accuracy in disease
classification with the help of the ontology
Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine
Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu
Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery
Motivation: Signaling pathways control a large variety of cellular processes.
However, currently, even within the same database signaling pathways are often
curated at different levels of detail. This makes comparative and cross-talk
analyses difficult. Results: We present SignaLink, a database containing 8
major signaling pathways from Caenorhabditis elegans, Drosophila melanogaster,
and humans. Based on 170 review and approx. 800 research articles, we have
compiled pathways with semi-automatic searches and uniform, well-documented
curation rules. We found that in humans any two of the 8 pathways can
cross-talk. We quantified the possible tissue- and cancer-specific activity of
cross-talks and found pathway-specific expression profiles. In addition, we
identified 327 proteins relevant for drug target discovery. Conclusions: We
provide a novel resource for comparative and cross-talk analyses of signaling
pathways. The identified multi-pathway and tissue-specific cross-talks
contribute to the understanding of the signaling complexity in health and
disease and underscore its importance in network-based drug target selection.
Availability: http://SignaLink.orgComment: 9 pages, 4 figures, 2 tables and a supplementary info with 5 Figures
and 13 Table
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