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 EDBT 2026 / reviewers in the wild / expert

James Puthukattukaran

dblp:33/5114 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 1994
—ORCID · none

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

Systems, architecture and hardware · 1 · 1 first-author

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%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 77% High-performance computing · 23%

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genome annotation
gene prediction		 0.011994
Design and Implementation of Parallel Algorithms for Gene-Finding · HPDC 1994
Bioinformatics and computational biology
genomics		 0.011994
Design and Implementation of Parallel Algorithms for Gene-Finding · HPDC 1994
Parallel and multicore computing › parallel algorithms
parallel algorithm design		 0.011994
Design and Implementation of Parallel Algorithms for Gene-Finding · HPDC 1994
High-performance computing
scientific computing systems		 0.011994
Design and Implementation of Parallel Algorithms for Gene-Finding · HPDC 1994

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

parallel algorithm design · 0.0PVM · 0.0
YearPublicationVenuePosition
1994 Design and Implementation of Parallel Algorithms for Gene-Finding
abstract
Finding genes unequivocally in DNA sequences is one of the key goals of the Human Genome project. The human genome is a 3 billion character long DNA sequence and is estimated to contain about 100000 genes. It has been shown by several biologists that genes in a DNA sequence satisfy certain special properties. We use a combination of these properties to design a serial algorithm for gene-finding. To speed up the process of finding genes in long DNA sequences (of the order of /spl ges/100000 characters), we design a parallel algorithm for gene-finding. We have implemented the parallel gene-finding algorithm on the CM-5 multicomputer as well as on a network of HP Apollo workstations under Parallel Virtual Machine software package. Experimental results indicate that our algorithms predict genes with reasonable accuracy.>
James Puthukattukaran, Suresh Chalasani, Periannan Senapathy
 HPDC1