Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from VLDB 2026 / reviewers in the wild / expert

Meenakshi Upreti

dblp:09/4132 · DBLP profile ↗
← Back
3ranked-venue papers
0as first author
0since 2021 · last 2016
0000-0002-8734-0408ORCID · corroborated

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

Applied, interdisciplinary, general and emerging computing · 3

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
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
gene expression analysis		 0.112008
Comment on causality and pathway search in microarray time series experiment · Bioinform. 2008
Bioinformatics and computational biology › gene expression analysis › time-series gene expression analysis
time-course microarray analysis		 0.112008
Comment on causality and pathway search in microarray time series experiment · Bioinform. 2008
Bioinformatics and computational biology › systems bioinformatics
pathway analysis		 0.012008
Comment on causality and pathway search in microarray time series experiment · Bioinform. 2008

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

granger causality · 0.1
YearPublicationVenuePosition
2016 An approach for deciphering patient-specific variations with application to breast cancer molecular expression profiles
Radhakrishnan Nagarajan, Meenakshi Upreti
 J. Biomed. Informatics2
2008 Comment on causality and pathway search in microarray time series experiment
abstract
Abstract Contact: [email protected]
Radhakrishnan Nagarajan, Meenakshi Upreti
 Bioinform.2
2006 Correlation Statistics for cDNA Microarray Image Analysis
abstract
In this paper, correlation of the pixels comprising a microarray spot is investigated. Subsequently, correlation statistics, namely, Pearson correlation and Spearman rank correlation, are used to segment the foreground and background intensity of microarray spots. The performance of correlation-based segmentation is compared to clustering-based (PAM, k-means) and seeded-region growing techniques (SPOT). It is shown that correlation-based segmentation is useful in flagging poorly hybridized spots, thus minimizing false-positives. The present study also raises the intriguing question of whether a change in correlation can be an indicator of differential gene expression.
Radhakrishnan Nagarajan, Meenakshi Upreti
 IEEE ACM Trans. Comput. Biol. Bioinform.2