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- 저자 : Kim Gates (검색결과 2 건)
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Liu, Suli,Im, Hogune,Bairoch, Amos,Cristofanilli, Massimo,Chen, Rui,Deutsch, Eric W.,Dalton, Stephen,Fenyo, David,Fanayan, Susan,Gates, Chris,Gaudet, Pascale,Hincapie, Marina,Hanash, Samir,Kim, Hoguen American Chemical Society 2013 JOURNAL OF PROTEOME RESEARCH Vol.12 No.1
<P>We report progress assembling the parts list for chromosome 17 and illustrate the various processes that we have developed to integrate available data from diverse genomic and proteomic knowledge bases. As primary resources, we have used GPMDB, neXtProt, PeptideAtlas, Human Protein Atlas (HPA), and GeneCards. All sites share the common resource of Ensembl for the genome modeling information. We have defined the chromosome 17 parts list with the following information: 1169 protein-coding genes, the numbers of proteins confidently identified by various experimental approaches as documented in GPMDB, neXtProt, PeptideAtlas, and HPA, examples of typical data sets obtained by RNASeq and proteomic studies of epithelial derived tumor cell lines (disease proteome) and a normal proteome (peripheral mononuclear cells), reported evidence of post-translational modifications, and examples of alternative splice variants (ASVs). We have constructed a list of the 59 “missing” proteins as well as 201 proteins that have inconclusive mass spectrometric (MS) identifications. In this report we have defined a process to establish a baseline for the incorporation of new evidence on protein identification and characterization as well as related information from transcriptome analyses. This initial list of “missing” proteins that will guide the selection of appropriate samples for discovery studies as well as antibody reagents. Also we have illustrated the significant diversity of protein variants (including post-translational modifications, PTMs) using regions on chromosome 17 that contain important oncogenes. We emphasize the need for mandated deposition of proteomics data in public databases, the further development of improved PTM, ASV, and single nucleotide variant (SNV) databases, and the construction of Web sites that can integrate and regularly update such information. In addition, we describe the distribution of both clustered and scattered sets of protein families on the chromosome. Since chromosome 17 is rich in cancer-associated genes, we have focused the clustering of cancer-associated genes in such genomic regions and have used the ERBB2 amplicon as an example of the value of a proteogenomic approach in which one integrates transcriptomic with proteomic information and captures evidence of coexpression through coordinated regulation.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/jprobs/2013/jprobs.2013.12.issue-1/pr300985j/production/images/medium/pr-2012-00985j_0009.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/pr300985j'>ACS Electronic Supporting Info</A></P>
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Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models
Shorabuddin Syed,Ahmad Baghal,Fred Prior,Meredith Zozus,Shaymaa Al-Shukri,Hafsa Bareen Syeda,Maryam Garza,Salma Begum,Kim Gates,Mahanazuddin Syed,Kevin W. Sexton 대한의료정보학회 2020 Healthcare Informatics Research Vol.26 No.3
Objectives: The time-dependent study of comorbidities provides insight into disease progression and trajectory. We hypothesizethat understanding longitudinal disease characteristics can lead to more timely intervention and improve clinicaloutcomes. As a first step, we developed an efficient and easy-to-install toolkit, the Time-based Elixhauser Comorbidity Index(TECI), which pre-calculates time-based Elixhauser comorbidities and can be extended to common data models (CDMs). Methods: A Structured Query Language (SQL)-based toolkit, TECI, was built to pre-calculate time-specific Elixhauser comorbidityindices using data from a clinical data repository (CDR). Then it was extended to the Informatics for IntegratingBiology and the Bedside (I2B2) and Observational Medical Outcomes Partnership (OMOP) CDMs. Results: At the Universityof Arkansas for Medical Sciences (UAMS), the TECI toolkit was successfully installed to compute the indices from CDRdata, and the scores were integrated into the I2B2 and OMOP CDMs. Comorbidity scores calculated by TECI were validatedagainst: scores available in the 2015 quarter 1–3 Nationwide Readmissions Database (NRD) and scores calculated usingthe comorbidities using a previously validated algorithm on the 2015 quarter 4 NRD. Furthermore, TECI identified 18,846UAMS patients that had changes in comorbidity scores over time (year 2013 to 2019). Comorbidities for a random sample ofpatients were independently reviewed, and in all cases, the results were found to be 100% accurate. Conclusions: TECI facilitatesthe study of comorbidities within a time-dependent context, allowing better understanding of disease associations andtrajectories, which has the potential to improve clinical outcomes.
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