scispace - formally typeset
N

Natalia Maltsev

Researcher at University of Chicago

Publications -  58
Citations -  5058

Natalia Maltsev is an academic researcher from University of Chicago. The author has contributed to research in topics: Grid computing & BioPAX : Biological Pathways Exchange. The author has an hindex of 23, co-authored 58 publications receiving 4849 citations. Previous affiliations of Natalia Maltsev include Argonne National Laboratory & University of Illinois at Chicago.

Papers
More filters
Journal ArticleDOI

The use of gene clusters to infer functional coupling

TL;DR: The characterization of the parameters that determine the utility of the approach are extended, and it is shown that this approach will play a significant role in supporting efforts to assign functionality to the remaining uncharacterized genes in sequenced genomes.
Journal ArticleDOI

The minimum information about a genome sequence (MIGS) specification.

Dawn Field, +71 more
- 01 May 2008 - 
TL;DR: Here, the minimum information about a genome sequence (MIGS) specification is introduced with the intent of promoting participation in its development and discussing the resources that will be required to develop improved mechanisms of metadata capture and exchange.
Journal ArticleDOI

The BioPAX community standard for pathway data sharing

Emek Demir, +94 more
- 01 Sep 2010 - 
TL;DR: Thousands of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases, and this large amount of pathway data in a computable form will support visualization, analysis and biological discovery.
Journal ArticleDOI

WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction

TL;DR: The WIT (What Is There) system has been designed to support comparative analysis of sequenced genomes and to generate metabolic reconstructions based on chromosomal sequences and metabolic modules from the EMP/MPW family of databases.
Journal Article

Use of contiguity on the chromosome to predict functional coupling.

TL;DR: A technique for detecting possible functional coupling between genes based on detection of potential operons, which has revealed a surprisingly rich and apparently accurate set of functionally coupled genes.