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Vladimir I. Valtchinov

dblp:14/10933 · DBLP profile ↗
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3ranked-venue papers
1as first author
1since 2021 · last 2022
0000-0002-3272-4826ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 3 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
gene expression analysis
0.322012
Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome · Bioinform. 2012
Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome · Bioinform. 2012
Bioinformatics and computational biology › transcriptomics
transcriptome comparison
0.322012
Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome · Bioinform. 2012
Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome · Bioinform. 2012
Bioinformatics and computational biology
transcriptomics
0.322012
Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome · Bioinform. 2012
Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome · Bioinform. 2012
Bioinformatics and computational biology
functional genomics
0.122012
Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome · Bioinform. 2012
Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome · Bioinform. 2012
Bioinformatics and computational biology › knowledge representation in biology › biomedical ontology
gene ontology
0.122012
Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome · Bioinform. 2012
Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome · Bioinform. 2012

Methods — techniques the papers use, named apart from their topics

rank analysis · 0.3correlation analysis · 0.3
YearPublicationVenuePosition
2022 Analytics to monitor local impact of the Protecting Access to Medicare Act's imaging clinical decision support requirements
abstract
OBJECTIVE: This study aimed is to: (1) extend the Integrating the Biology and the Bedside (i2b2) data and application models to include medical imaging appropriate use criteria, enabling it to serve as a platform to monitor local impact of the Protecting Access to Medicare Act's (PAMA) imaging clinical decision support (CDS) requirements, and (2) validate the i2b2 extension using data from the Medicare Imaging Demonstration (MID) CDS implementation. MATERIALS AND METHODS: This study provided a reference implementation and assessed its validity and reliability using data from the MID, the federal government's predecessor to PAMA's imaging CDS program. The Star Schema was extended to describe the interactions of imaging ordering providers with the CDS. New ontologies were added to enable mapping medical imaging appropriateness data to i2b2 schema. z-Ratio for testing the significance of the difference between 2 independent proportions was utilized. RESULTS: The reference implementation used 26 327 orders for imaging examinations which were persisted to the modified i2b2 schema. As an illustration of the analytical capabilities of the Web Client, we report that 331/1192 or 28.1% of imaging orders were deemed appropriate by the CDS system at the end of the intervention period (September 2013), an increase from 162/1223 or 13.2% for the first month of the baseline period, December 2011 (P = .0212), consistent with previous studies. CONCLUSIONS: The i2b2 platform can be extended to monitor local impact of PAMA's appropriateness of imaging ordering CDS requirements.
Vladimir I. Valtchinov, Shawn N. Murphy, Ronilda C. Lacson, Nikolay Ikonomov, Bingxue K. Zhai, Katherine P. Andriole, Justin F. Rousseau, Dick Hanson, Isaac S. Kohane, Ramin Khorasani
J. Am. Medical Informatics Assoc.1
2012 Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome
abstract
Abstract Motivation: We investigate and quantify the generalizability of the white blood cell (WBC) transcriptome to the general, multiorgan transcriptome. We use data from the NCBI's Gene Expression Omnibus (GEO) public repository to define two datasets for comparison, WBC and OO (Other Organ) sets. Results: Comprehensive pair-wise correlation and expression level profiles are calculated for both datasets (with sizes of 81 and 1463, respectively). We have used mapping and ranking across the Gene Ontology (GO) categories to quantify similarity between the two sets. GO mappings of the most correlated and highly expressed genes from the two datasets tightly match, with the notable exceptions of components of the ribosome, cell adhesion and immune response. That is, 10 877 or 48.8% of all measured genes do not change >10% of rank range between WBC and OO; only 878 (3.9%) change rank >50%. Two trans-tissue gene lists are defined, the most changing and the least changing genes in expression rank. We also provide a general, quantitative measure of the probability of expression rank and correlation profile in the OO system given the expression rank and correlation profile in the WBC dataset. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
Isaac S. Kohane, Vladimir I. Valtchinov
Bioinform.2
2012 Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome
abstract
MOTIVATION: We investigate and quantify the generalizability of the white blood cell (WBC) transcriptome to the general, multiorgan transcriptome. We use data from the NCBI's Gene Expression Omnibus (GEO) public repository to define two datasets for comparison, WBC and OO (Other Organ) sets. RESULTS: Comprehensive pair-wise correlation and expression level profiles are calculated for both datasets (with sizes of 81 and 1463, respectively). We have used mapping and ranking across the Gene Ontology (GO) categories to quantify similarity between the two sets. GO mappings of the most correlated and highly expressed genes from the two datasets tightly match, with the notable exceptions of components of the ribosome, cell adhesion and immune response. That is, 10 877 or 48.8% of all measured genes do not change >10% of rank range between WBC and OO; only 878 (3.9%) change rank >50%. Two trans-tissue gene lists are defined, the most changing and the least changing genes in expression rank. We also provide a general, quantitative measure of the probability of expression rank and correlation profile in the OO system given the expression rank and correlation profile in the WBC dataset.
Isaac S. Kohane, Vladimir I. Valtchinov
Bioinform.2