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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File: | <div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Dander BMCBioinformatics2014 15.jpg|220px]]</div> | ||
'''"[[Journal: | '''"[[Journal:Personalized Oncology Suite: Integrating next-generation sequencing data and whole-slide bioimages|Personalized Oncology Suite: Integrating next-generation sequencing data and whole-slide bioimages]]"''' | ||
Cancer immunotherapy has recently entered a remarkable renaissance phase with the approval of several agents for treatment. [[Cancer informatics|Cancer treatment platforms]] have demonstrated profound tumor regressions including complete cure in patients with metastatic cancer. Moreover, technological advances in next-generation sequencing (NGS) as well as the development of devices for scanning whole-slide bioimages from tissue sections and [[Bioimage informatics|image analysis software]] for quantitation of tumor-infiltrating lymphocytes (TILs) allow, for the first time, the development of personalized cancer immunotherapies that target patient specific mutations. However, there is currently no [[bioinformatics]] solution that supports the integration of these heterogeneous datasets. | |||
We | We have developed a bioinformatics platform – Personalized Oncology Suite (POS) – that integrates clinical data, NGS data and whole-slide bioimages from tissue sections. POS is a web-based platform that is scalable, flexible and expandable. The underlying database is based on a data warehouse schema, which is used to integrate [[information]] from different sources. POS stores clinical data, [[Genomics|genomic]] data (SNPs and INDELs identified from NGS analysis), and scanned whole-slide images. ('''[[Journal:Personalized Oncology Suite: Integrating next-generation sequencing data and whole-slide bioimages|Full article...]]''')<br /> | ||
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''Recently featured'': [[Journal:Requirements for data integration platforms in biomedical research networks: A reference model|Requirements for data integration platforms in biomedical research networks: A reference model]], [[Journal:4273π: Bioinformatics education on low cost ARM hardware|4273π: Bioinformatics education on low cost ARM hardware | ''Recently featured'': [[Journal:Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations|Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations]], [[Journal:Requirements for data integration platforms in biomedical research networks: A reference model|Requirements for data integration platforms in biomedical research networks: A reference model]], [[Journal:4273π: Bioinformatics education on low cost ARM hardware|4273π: Bioinformatics education on low cost ARM hardware]] |
Revision as of 15:53, 28 December 2015
"Personalized Oncology Suite: Integrating next-generation sequencing data and whole-slide bioimages"
Cancer immunotherapy has recently entered a remarkable renaissance phase with the approval of several agents for treatment. Cancer treatment platforms have demonstrated profound tumor regressions including complete cure in patients with metastatic cancer. Moreover, technological advances in next-generation sequencing (NGS) as well as the development of devices for scanning whole-slide bioimages from tissue sections and image analysis software for quantitation of tumor-infiltrating lymphocytes (TILs) allow, for the first time, the development of personalized cancer immunotherapies that target patient specific mutations. However, there is currently no bioinformatics solution that supports the integration of these heterogeneous datasets.
We have developed a bioinformatics platform – Personalized Oncology Suite (POS) – that integrates clinical data, NGS data and whole-slide bioimages from tissue sections. POS is a web-based platform that is scalable, flexible and expandable. The underlying database is based on a data warehouse schema, which is used to integrate information from different sources. POS stores clinical data, genomic data (SNPs and INDELs identified from NGS analysis), and scanned whole-slide images. (Full article...)
Recently featured: Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations, Requirements for data integration platforms in biomedical research networks: A reference model, 4273π: Bioinformatics education on low cost ARM hardware