Jacob Engelbrecht

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

dblp:01/6737 · DBLP profile ↗

← Back

4ranked-venue papers

0as first author

0since 2021 · last 1997

—ORCID · none

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

Artificial intelligence and machine learning · 2Applied, interdisciplinary, general and emerging computing · 2

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

3 papers

Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Bioinformatics and computational biology › gene regulation › promoter analysis promoter prediction	1	1995	Investigations of Escherichia coli Promoter Sequences with Artificial Neural Networks: New Signals Discovered Upstream of the Transcriptional Startpoint · ISMB 1995
Bioinformatics and computational biology sequence analysis	1	1995	Investigations of Escherichia coli Promoter Sequences with Artificial Neural Networks: New Signals Discovered Upstream of the Transcriptional Startpoint · ISMB 1995
Bioinformatics and computational biology › genome annotation gene prediction	1	1993	Hidden Markov Models for Human Genes · NIPS 1993
Bioinformatics and computational biology › genome annotation › gene prediction hidden markov model-based gene finding	1	1993	Hidden Markov Models for Human Genes · NIPS 1993

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

artificial neural network · 0.0hidden markov model · 0.0

Year	Publication	Venue	Position
1997	A Neural Network Method for Identification of Prokaryotic and Eukaryotic Signal Peptides and Prediction of their Cleavage Sites abstract We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The method performs significantly better than previous prediction schemes, and can easily be applied to genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision. Predictions can be made on a publicly available WWW server: http://www.cbs.dtu.dk/services/SignalP/. Henrik Nielsen, Jacob Engelbrecht, Søren Brunak, Gunnar von Heijne	Int. J. Neural Syst.	2
1995	Periodic Sequence Patterns in Human Exons Pierre Baldi, Søren Brunak, Yves Chauvin, Jacob Engelbrecht, Anders Krogh	ISMB	4
1995	Investigations of Escherichia coli Promoter Sequences with Artificial Neural Networks: New Signals Discovered Upstream of the Transcriptional Startpoint Anders Gorm Pedersen, Jacob Engelbrecht	ISMB	2
1993	Hidden Markov Models for Human Genes Pierre Baldi, Søren Brunak, Yves Chauvin, Jacob Engelbrecht, Anders Krogh	NIPS	4