Ancient human genome sequence of an extinct Palaeo-Eskimo (2010)
Morten Rasmussen, Yingrui Li, Stinus Lindgreen, Jakob Skou Pedersen, Anders Albrechtsen, Ida Moltke, ...
miRMaid: a unified programming interface for microRNA data resources (2010)
Jacobsen, Anders, Krogh, Anders, Kauppinen, Sakari, Lindow, Morten
Abstract Background MicroRNAs (miRNAs) are endogenous small RNAs that play a key role in post-transcriptional regulation of gene expression in animals and plants. The number of known miRNAs has...
Nygaard, Sanne, Jacobsen, Anders, Lindow, Morten, Eriksen, Jens, Balslev, Eva, Flyger, Henrik, ...
Abstract Background MiRNAs play important roles in cellular control and in various disease states such as cancers, where they may serve as markers or possibly even therapeutics. Identifying the whole...
Genome-wide detection and analysis of hippocampus core promoters using DeepCAGE (2009)
Valen, Eivind, Pascarella, Giovanni, Chalk, Alistair, Maeda, Norihiro, Kojima, Miki, Kawazu, Chika, ...
Finding and characterizing mRNAs, their transcription start sites (TSS), and their associated promoters is a major focus in post-genome biology. Mammalian cells have at least 5–10 magnitudes more...
CCHMM_PROF: a HMM-based coiled-coil predictor with evolutionary information (2009)
Bartoli, Lisa, Fariselli, Piero, Krogh, Anders, Casadio, Rita
Motivation:The widespread coiled-coil structural motif in proteins is known to mediate a variety of biological interactions. Recognizing a coiled-coil containing sequence and locating its coiled-coil...
BMC Medical Genomics BioMed Central (2009)
Sanne Nygaard, Anders Jacobsen, Morten Lindow, Jens Eriksen, Eva Balslev, Henrik Flyger, ...
Research article Identification and analysis of miRNAs in human breast cancer and teratoma samples using deep sequencing
Suzuki, Harukazu, Forrest, Alistair Raymond Russell, Nimwegen, Erik Van, Daub, Carsten O, Balwierz, Piotr J, Irvine, Katharine M, ...
Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth...
Suzuki, Harukazu, Forrest, Alistair Raymond Russell, Nimwegen, Erik Van, Daub, Carsten O, Balwierz, Piotr J, Irvine, Katharine M, ...
Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth...
Evolving the Structure of Hidden Markov Models (2008)
Kyoung-jae Won, Adam Prügel-bennett, Anders Krogh
Abstract—A genetic algorithm (GA) is proposed for finding the structure of hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful...
Dirichlet Mixtures: A Method for Improved Detection of Weak (2008)
But Signicant Protein, Kimmen Sjolander Y, Richard Hughey, Anders Krogh, Kevin Karplus, I. Saira Mian, ...
We present a method for condensing the information in multiple alignments of proteins into a mixture of Dirichlet densities over amino acid distributions. Dirichlet mixture densities are designed to...
Genome-wide detection and analysis of hippocampus core promoters using DeepCAGE (2008)
Valen, Eivind, Pascarella, Giovanni, Chalk, Alistair Morgan, Maeda, Norihiro, Kojima, Miki, Kawazu, Chika, ...
Finding and characterizing mRNAs, their transcription start sites (TSS) and their associated promoters is a major focus in post-genome biology. Mammalian cells have at least 5-10 magnitudes more TSS...
Genome-wide detection and analysis of hippocampus core promoters using DeepCAGE (2008)
Valen, Eivind, Pascarella, Giovanni, Chalk, Alistair Morgan, Maeda, Norihiro, Kojima, Miki, Kawazu, Chika, ...
Finding and characterizing mRNAs, their transcription start sites (TSS) and their associated promoters is a major focus in post-genome biology. Mammalian cells have at least 5-10 magnitudes more TSS...
Genome-wide detection and analysis of hippocampus core promoters using DeepCAGE (2008)
Valen, Eivind, Pascarella, Giovanni, Chalk, Alistair Morgan, Maeda, Norihiro, Kojima, Miki, Kawazu, Chika, ...
Finding and characterizing mRNAs, their transcription start sites (TSS) and their associated promoters is a major focus in post-genome biology. Mammalian cells have at least 5-10 magnitudes more TSS...
Bryne, Jan Christian, Valen, Eivind, Tang, Man-Hung Eric, Marstrand, Troels, Winther, Ole, Da Piedade, Isabelle, ...
JASPAR is a popular open-access database for matrix models describing DNA-binding preferences for transcription factors and other DNA patterns. With its third major release, JASPAR has been expanded...
Modeling promoter grammars with evolving hidden Markov models (2008)
Won, Kyoung-Jae, Sandelin, Albin, Marstrand, Troels Torben, Krogh, Anders
Motivation: Describing and modeling biological features of eukaryotic promoters remains an important and challenging problem within computational biology. The promoters of higher eukaryotes in...
A code for transcription initiation in mammalian genomes (2008)
Frith, Martin C., Valen, Eivind, Krogh, Anders, Hayashizaki, Yoshihide, Carninci, Piero, Sandelin, Albin
Genome-wide detection of transcription start sites (TSSs) has revealed that RNA Polymerase II transcription initiates at millions of positions in mammalian genomes. Most core promoters do not have a...
Genome-wide detection and analysis of hippocampus core promoters using DeepCAGE (2008)
Valen, Eivind, Pascarella, Giovanni, Chalk, Alistair Morgan, Maeda, Norihiro, Kojima, Miki, Kawazu, Chika, ...
Finding and characterizing mRNAs, their transcription start sites (TSS) and their associated promoters is a major focus in post-genome biology. Mammalian cells have at least 5-10 magnitudes more TSS...
For Oral Presentation at the Learning Methods Session. (2007)
Ensemble methods, which combine several classiers, have been successfully applied to decrease generalization error of machine learning methods. For most ensemble methods the ensemble members are...
Testing Ensemble Methods on Prediction of Protein Secondary Structure (2007)
Jakob V. Hansen, Ny Munkegade, Anders Krogh
The geometric opinion pool (GOP) ensemble method uses a multiplicative combination of predictors, and it is tailored to probability estimation in multi-class problems. This enables a decomposition of...
Anders Krogh, Michael Brown, I. Saira Mian, Kimmen Sjolander, David Haussler
Hidden Markov Models (HMMs) are applied to the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are...
University of Edinburgh, U.K. (2007)
We study the characteristics of learning with ensembles. Solving exactly the simple model of an ensemble of linear students, we find surprisingly rich behaviour. For learning in large ensembles, it...
Kimmen Sjolander, Kevin Karplus, Michael Brown, Richard Hughey, Anders Krogh, I. Saira Mian, ...
This paper presents the mathematical foundations of Dirichlet mixtures, which have been used to improve database search results for homologous sequences, when a variable number of sequences from a...
Sren Kamaric Riis, Anders Krogh
It has been proven by several authors that hybrids of Hidden Markov Models (HMM) and Neural Networks (NN) yield good performance in speech recognition. However, in many of the current hybrids the HMM...
Multiple alignment and structure prediction of non-coding RNA sequences (2007)
Lindgreen, Stinus, Gardner, Paul P, Krogh, Anders
No abstract available.
University of Edinburgh, U.K. (2007)
We study the general characteristics of learning with ensembles. Solving exactly the simple model scenario of an ensemble of linear students, we find surprisingly rich behaviour. For learning in...
Helicobacter pylori is a gram negative bacterium that has been linked to numerous severe gastroduodenal diseases, including peptic ulcer and gastric cancer. The basis for these diverse clinical...
Intragenomic Matching Reveals a Huge Potential for miRNA-Mediated Regulation in Plants (2007)
Morten Lindow, Anders Jacobsen, Sanne Nygaard, Yuan Mang, Anders Krogh
microRNAs (miRNAs) are important post-transcriptional regulators, but the extent of this regulation is uncertain, both with regard to the number of miRNA genes and their targets. Using an algorithm...
An evolutionary method for learning HMM structure: prediction of protein secondary structure (2007)
Won, Kyoung-Jae, Hamelryck, Thomas, Prügel-Bennett, Adam, Krogh, Anders
Abstract Background The prediction of the secondary structure of proteins is one of the most studied problems in bioinformatics. Despite their success in many problems of biological sequence...
Käll, Lukas, Krogh, Anders, Sonnhammer, Erik L.L.
When using conventional transmembrane topology and signal peptide predictors, such as TMHMM and SignalP, there is a substantial overlap between these two types of predictions. Applying these methods...
An evolutionary method for learning HMM structure: prediction of protein secondary structure (2007)
Won, Kyoung-Jae, Hamelryck, Thomas, Prügel-Bennett, Adam, Krogh, Anders
Background The prediction of the secondary structure of proteins is one of the most studied problems in bioinformatics. Despite their success in many problems of biological sequence analysis, Hidden...
protein secondary structure (2007)
Kyoung-jae Won, Thomas Hamelryck, Adam Prügel-bennett, Anders Krogh
An evolutionary method for learning HMM structure: prediction of
Lindgreen, Stinus, Gardner, Paul P., Krogh, Anders
Motivation: As more non–coding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs...
Sampling Realistic Protein Conformations Using Local Structural Bias (2006)
Thomas Hamelryck, John T. Kent, Anders Krogh
The prediction of protein structure from sequence remains a major unsolved problem in biology. The most successful protein structure prediction methods make use of a divide-and-conquer strategy to...
Sampling realistic protein conformations using local structural bias (2006)
Thomas Hamelryck, John T. Kent, Anders Krogh
The prediction of protein structure from sequence remains a major unsolved problem in biology. The most successful protein structure prediction methods make use of a divide-and-conquer strategy to...
Automatic generation of gene finders for eukaryotic species (2006)
Abstract Background The number of sequenced eukaryotic genomes is rapidly increasing. This means that over time it will be hard to keep supplying customised gene finders for each genome. This calls...
A hidden Markov model approach for determining expression from genomic tiling micro arrays (2006)
Munch, Kasper, Gardner, Paul P, Arctander, Peter, Krogh, Anders
Abstract Background Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have...
Evolving the Structure of Hidden Markov Models (2006)
Won, Kyoung-Jae, Prügel-Bennett, Adam, Krogh, Anders
A Genetic Algorithm (GA) is proposed for finding the structure of Hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful building...
Evolving the Structure of Hidden Markov Models (2006)
Won, Kyoung-Jae, Prügel-Bennett, Adam, Krogh, Anders
A Genetic Algorithm (GA) is proposed for finding the structure of Hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful building...
Evolving the Structure of Hidden Markov Models (2006)
Won, Kyoung-Jae, Prügel-Bennett, Adam, Krogh, Anders
A Genetic Algorithm (GA) is proposed for finding the structure of Hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful building...
PONGO: a web server for multiple predictions of all-alpha transmembrane proteins (2006)
Amico, Mauro, Finelli, Michele, Rossi, Ivan, Zauli, Andrea, Elofsson, Arne, Viklund, Håkan, ...
The annotation efforts of the BIOSAPIENS European Network of Excellence have generated several distributed annotation systems (DAS) with the aim of integrating Bioinformatics resources and annotating...
BMC Bioinformatics BioMed Central Methodology article (2006)
Kasper Munch, Paul P Gardner, Peter Arct, Anders Krogh, Open Access
genomic tiling micro arrays
Computational evidence for hundreds of non-conserved plant microRNAs (2005)
Abstract Background MicroRNAs (miRNA) are small (20–25 nt) non-coding RNA molecules that regulate gene expression through interaction with mRNA in plants and metazoans. A few hundred miRNAs are...
Evolving Hidden Markov Models for Protein Secondary Structure Prediction (2005)
Won, Kyoung Jae, Hamelryck, Thomas, Prugel-Bennett, Adam, Krogh, Anders
New results are presented for the prediction of secondary structure information for protein sequences using Hidden Markov Models (HMMs) evolved using a Genetic Algorithm (GA). We achieved a Q3...
Evolving Hidden Markov Models for Protein Secondary Structure Prediction (2005)
Won, Kyoung Jae, Hamelryck, Thomas, Prugel-Bennett, Adam, Krogh, Anders
New results are presented for the prediction of secondary structure information for protein sequences using Hidden Markov Models (HMMs) evolved using a Genetic Algorithm (GA). We achieved a Q3...
Evolving Hidden Markov Models for Protein Secondary Structure Prediction (2005)
Won, Kyoung Jae, Hamelryck, Thomas, Prugel-Bennett, Adam, Krogh, Anders
New results are presented for the prediction of secondary structure information for protein sequences using Hidden Markov Models (HMMs) evolved using a Genetic Algorithm (GA). We achieved a Q3...
An HMM posterior decoder for sequence feature prediction that includes homology information (2005)
Lukas Käll, Anders Krogh, Erik L. Sonnhammer
Motivation: When predicting sequence features like transmembrane topology, signal peptides, coil--coil structures, protein secondary structure or genes, extra support can be gained from homologs.
Large-scale prokaryotic gene prediction and comparison to genome annotation (2005)
Nielsen, Pernille, Krogh, Anders
Motivation: Prokaryotic genomes are sequenced and annotated at an increasing rate. The methods of annotation vary between sequencing groups. It makes genome comparison difficult and may lead to...
An HMM posterior decoder for sequence feature prediction that includes homology information (2005)
Käll, Lukas, Krogh, Anders, Sonnhammer, Erik L. L.
Motivation: When predicting sequence features like transmembrane topology, signal peptides, coil–coil structures, protein secondary structure or genes, extra support can be gained from homologs....
Large scale prokaryotic gene prediction and comparison to genome annotation (2005)
Nielsen, Pernille, Krogh, Anders
Motivation: Prokaryotic genomes are sequenced and annotated at an increasing rate. The methods of annotation vary between sequencing groups. It makes genome comparison difficult and may lead to...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background: The number of sequenced eukaryotic genomes is rapidly...
BMC Genomics BioMed Central (2005)
Research article Computational evidence for hundreds of non-conserved plant microRNAs
The Block Hidden Markov Model for Biological Sequence Analysis (2004)
Won, Kyoung-Jae, Prugel-Bennett, Adam, Krogh, Anders
The Hidden Markov Models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of optimising...
The Block Hidden Markov Model for Biological Sequence Analysis (2004)
Won, Kyoung-Jae, Prugel-Bennett, Adam, Krogh, Anders
The Hidden Markov Models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of optimising...
The Block Hidden Markov Model for Biological Sequence Analysis (2004)
Won, Kyoung-Jae, Prugel-Bennett, Adam, Krogh, Anders
The Hidden Markov Models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of optimising...
Training HMM Structure with Genetic Algorithm for Biological Sequence Analysis (2004)
Won, Kyoung-Jae, Prügel-Bennett, Adam, Krogh, Anders
Motivation : Hidden Markov Models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of...
A Combined Transmembrane Topology and Signal Peptide Prediction Method (2004)
Lukas Käll, Anders Krogh, Erik L. L. Sonnhammer
to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at http://phobius.cgb.ki.se/ as well as at...
Training HMM structure with genetic algorithm for biological sequence analysis (2004)
Won, Kyoung-Jae, Prügel-Bennett, Adam, Krogh, Anders
Summary: Hidden Markov models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of...
Training HMM structure with genetic algorithm for biological sequence analysis (2004)
Won, Kyoung-Jae, Prügel-Bennett, Adam, Krogh, Anders
Motivation: Hidden Markov Models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of...
Training HMM structure with genetic algorithm for biological sequence analysis (2004)
Won, Kyoung-Jae, Prügel-Bennett, Adam, Krogh, Anders
Motivation: Hidden Markov Models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of...
Teaching computers to fold proteins (2003)
A new general algorithm for optimization of potential functions for protein folding is introduced. It is based upon gradient optimization of the thermodynamic stability of native folds of a training...
EasyGene – a prokaryotic gene finder that ranks ORFs by statistical significance (2003)
Abstract Background Contrary to other areas of sequence analysis, a measure of statistical significance of a putative gene has not been devised to help in discriminating real genes from the masses of...
Prediction of lipoprotein signal peptides in Gram-negative bacteria (2003)
Juncker, Agnieszka S., Willenbrock, Hanni, Von Heijne, Gunnar, Brunak, Søren, Nielsen, Henrik, Krogh, Anders
A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved...
Bias of purine stretches in sequenced chromosomes (2002)
David Ussery, Dikeos Mario Soumpasis, Søren Brunak, Hans Henrik Stærfeldt, Peder Worning, Anders Krogh
ABSTRACT. We examined more than 700 DNA sequences (full length chromosomes and plasmids) for stretches of purines (R) or pyrimidines (Y) and alternating YR stretches; such re-gions will likely adopt...
Martelli, Pier Luigi, Fariselli, Piero, Krogh, Anders, Casadio, Rita
Motivation: Membrane proteins are an abundant and functionally relevant subset of proteins that putatively include from about 15 up to 30% of the proteome of organisms fully sequenced. These...
Anders Krogh, Bjoè Rn Larsson, Gunnar Von Heijne, Erik L. L. Sonnhammer, Stockholm Bioinformatics
The prediction of transmembrane helices in integral membrane proteins is an important aspect of bioinformatics. The most successful methods to date not only predict individual transmembrane
A General Method for Combining Predictors Tested on Protein Secondary Structure Prediction (2000)
Ensemble methods, which combine several classifiers, have been successfully applied to decrease generalization error of machine learning methods. For most ensemble methods the ensemble members are...
Prediction of signal peptides and signal anchors by a hidden Markov model (1998)
A hidden Markov model of signal peptides has been developed. It contains submodels for the N-terminal part, the hydrophobic region, and the region around the cleavage site. For known signal peptides,...
A hidden Markov model for predicting transmembrane helices in protein sequences (1998)
Erik Sonnhammer, Gunnar Von Heijne, Anders Krogh
A novel method to model and predict the location and orientation of alpha helices in membrane- spanning proteins is presented. It is based on a hidden Markov model (HMM) with an architecture that...
Studies in Probabilistic Sequence Alignment and Evolution (1998)
Ian Holmes, Ewan Birney, Bill Bruno, Richard Durbin, Sean Eddy, David Haussler, ...
The complete sequencing of whole genomes presents opportunities for detailed study of molecular evolution. This thesis combines theoretical developments of Bayesian approaches in bioinformatics with...
Hidden neural networks: a framework for HMM/NN hybrids (1997)
Sren Kamaric Riis, Anders Krogh
This paper presents a general framework for hybrids of Hidden Markov models (HMM) and neural networks (NN). In the new framework called Hidden Neural Networks (HNN) the usual HMM probability...
Two methods for improving performance of an HMM and their application for gene finding (1997)
A hidden Markov model for gene finding consists of submodels for coding regions, splice sites, introns, intergenic regions and possibly more. It is described how to estimate the model as a whole from...
Sren Kamaric Riis, Anders Krogh
The prediction of protein secondary structure by use of carefully structured neural networks and multiple sequence alignments has been investigated. Separate networks are used for predicting the...
Prediction of beta sheets in proteins (1996)
Anders Krogh, Sren Kamaric Riis
Most current methods for prediction of protein secondary structure use a small window of the protein sequence to predict the structure of the central amino acid. We describe a new method for...
Kimmen Sjolander Computer, Kimmen Sjolander, Kevin Karplus, Michael Brown, Richard Hughey, Anders Krogh, ...
We present a method for condensing the information in multiple alignments of proteins into a mixture of Dirichlet densities over amino acid distributions. Dirichlet mixture densities are designed to...
Hidden Markov models for sequence analysis: extension and analysis of the basic method (1996)
Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned sequences. The trained HMM can then be used for...
Learning with Ensembles: How over-fitting can be useful (1996)
We study the general characteristics of learning with ensembles. Solving exactly the simple model scenario of an ensemble of linear students, we find surprisingly rich behaviour. For learning in...
Learning with Ensembles: How over-fitting can be useful (1996)
Nordita Preprint S, Peter Sollich, Anders Krogh
We study the general characteristics of learning with ensembles. Solving exactly the simple model scenario of an ensemble of linear students, we find surprisingly rich behaviour. For learning in...
Hidden Markov models for sequence analysis: extension and analysis of the basic method (1996)
Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned sequences. The trained HMM can then be used for...
S Ren Kamaric Riis, Anders Krogh
The prediction of protein secondary structure by use of carefully structured neural networks and multiple sequence alignments has been investigated. Separate networks are used for predicting the...
Hidden Markov models for sequence analysis: extension and analysis of the basic method (1996)
Hughey, Richard, Krogh, Anders
Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned sequences. The trained HMM can then be used for...
Sjölander, Kimmen, Karplus, Kevin, Brown, Michael, Hughey, Richard, Krogh, Anders, Mian, I.Saira, ...
We present a method for condensing the information in multiple alignments of proteins into a mixture of Dirichlet densities over amino acid distributions. Dirichiet mixture densities are designed to...
Neural Network Ensembles, Cross Validation, and Active Learning (1995)
Learning of continuous valued functions using neural network ensembles (committees) can give improved accuracy, reliable estimation of the generalization error, and active learning. The ambiguity is...
Maximum Entropy Weighting of Aligned Sequences of Proteins or DNA (1995)
Anders Krogh, Graeme Mitchison
In a family of proteins or other biological sequences like DNA the various subfamilies are often very unevenly represented. For this reason a scheme for assigning weights to each sequence can greatly...
Periodic Sequence Patterns in Human Exons (1995)
Pierre Baldi, Yves Chauvin, Jacob Engelbrecht, Anders Krogh
We analyse the sequential structure of human exons and their flanking introns by hidden Markov models. Together, models of donor site regions, acceptor site regions and flanked internal exons, show...
Søren Kamaric Riis, S��ren Kamaric Riis, Anders Krogh
The prediction of protein secondary structure by use of carefully structured neural networks and multiple sequence alignments have been investigated. Separate networks are used for predicting the...
Neural Network Ensembles, Cross Validation, and Active Learning (1995)
Learning of continuous valued functions using neural network ensembles (committees) can give improved accuracy, reliable estimation of the generalization error, and active learning. The ambiguity is...
Prediction of Beta Sheets in Proteins (1995)
Nordita Preprint S, Anders Krogh, S��ren Kamaric Riis
Most current methods for prediction of protein secondary structure use a small window of the protein sequence to predict the structure of the central amino acid. We describe a new method for...
Periodic Sequence Patterns in Human Exons (1995)
Pierre Baldi, Søren Brunak, Yves Chauvin, Jacob Engelbrecht, Anders Krogh
We analyse the sequential structure of human exons and their flanking introns by hidden Markov models. Together, models of donor site regions, acceptor site regions and flanked internal exons, show...
A hidden Markov model that finds genes in E. coli DNA (1994)
Anders Krogh, I. Saira Mian, David Haussler, Em Algorithm
A hidden Markov model (HMM) has been developed to find protein coding genes in E. coli DNA using E. coli genome DNA sequence from the EcoSeq6 database maintained by Kenn Rudd. This HMM includes...
Bounds on Approximate Steepest Descent for Likelihood Maximization in Exponential Families (1994)
Nicol Cesa-Bianchi Computer, Anders Krogh, Manfred K. Warmuth
An approximate steepest descent strategy converging, in families of regular exponential densities, to maximum likelihood estimates of density functions is described. These density estimates are also...
Anders Krogh, Michael Brown, I. Saira Mian, Kimmen Sjolander, David Haussler
Hidden Markov Models (HMMs) are applied to the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are...
A Hidden Markov Model that finds genes in (1994)
Coli Anders, Anders Krogh, I. Saira Mian, David Haussler, Em Algorithm
A hidden Markov model (HMM) has been developed to find protein coding genes in E. coli DNA using E. coli genome DNA sequence from the EcoSeq6 database maintained by Kenn Rudd. This HMM includes...
A Hidden Markov Model that finds genes in E. coli DNA (1994)
E. Coli Dna, Anders Krogh, I. Saira Mian, David Haussler, Em Algorithm
A hidden Markov model (HMM) has been developed to find protein coding genes in E. coli DNA using E. coli genome DNA sequence from the EcoSeq6 database maintained by Kenn Rudd. This HMM includes...
Hidden Markov Models for Labeled Sequences (1994)
A hidden Markov model for labeled observations, called a CHMM, is introduced and a maximum likelihood method is developed for estimating the parameters of the model. Instead of training it to model...
John Hertz, Anders Krogh, Benny Lautrup, Torsten Lehmann
The conventional linear back-propagation algorithm is replaced by a non-linear version, which avoids the necessity for calculating the derivative of the activation function. This may be exploited in...
John Hertz, Anders Krogh, Benny Lautrup, Torsten Lehmann
The conventional linear back-propagation algorithm is replaced by a non-linear version, which avoids the necessity for calculating the derivative of the activation function. This may be exploited in...
A hidden Markov model that finds genes in E.coli DNA (1994)
Krogh, Anders, Mian, I. Saira, Haussler, David
A hidden Markov model (HMM) has been developed to find protein coding genes in E.coli DNA using E.coli genome DNA sequence from the EcoSeq6 database maintained by Kenn Rudd. This HMM includes states...
Using Dirichlet mixture priors to derive hidden Markov models for protein families (1993)
Michael Brown, Richard Hughey, Anders Krogh, I. Saira Mian, Sinsheimer Laboratories, Kimmen Sjolander, ...
A Bayesian method for estimating the amino acid distributions in the states of a hidden Markov model (HMM) for a protein family or the columns of a multiple alignment of that family is introduced....
Bounds on Approximate Steepest Descent for Likelihood Maximization in Exponential Families (1993)
Nicol Cesa-Bianchi, Anders Krogh, Manfred K. Warmuth
An approximate steepest descent strategy converging, in families of regular exponential densities, to maximum likelihood estimates of density functions is described. These density estimates are also...
Hidden Markov Models in Computational Biology: Applications to Protein Modeling (1993)
Anders Krogh, Michael Brown, I. Saira Mian, Kimmen Sjolander, David Haussler
Hidden Markov Models (HMMs) are applied to the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are...
Protein Modeling using Hidden Markov Models: Analysis of Globins (1993)
T. N Mudge, V. Milutinovic, L. Hunter, David Haussler, Anders Krogh, Saira Mian, ...
We apply Hidden Markov Models (HMMs) to the problem of statistical modeling and multiple alignment of protein families. A variant of the Expectation Maximization (EM) algorithm known as the Viterbi...
Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families (1993)
Michael Brown, Richard Hughey, Anders Krogh, I. Saira Mian, Sinsheimer Laboratories, Kimmen Sjolander, ...
A Bayesian method for estimating the amino acid distributions in the states of a hidden Markov model (HMM) for a protein family or the columns of a multiple alignment of that family is introduced....
Protein Modeling using Hidden Markov Models: Analysis of Globins (1993)
David Haussler, Anders Krogh, I. Saira Mian, Kimmen Sjölander
We apply Hidden Markov Models (HMMs) to the problem of statistical modeling and multiple sequence alignment of protein families. A variant of the Expectation Maximization (EM) algorithm known as the...
Bounds on Approximate Steepest Descent for Likelihood Maximization in Exponential Families (1993)
Nicolò Cesa-Bianchi, Anders Krogh, Manfred K. Warmuth
An approximate steepest descent strategy converging, in families of regular exponential densities, to maximum likelihood estimates of density functions is described. These density estimates are also...
A Simple Weight Decay Can Improve Generalization (1992)
Anders Krogh Connect, Anders Krogh, John A. Hertz
It has been observed in numerical simulations that a weight decay can improve generalization in a feed-forward neural network. This paper explains why. It is proven that a weight decay has two...
A Simple Weight Decay Can Improve Generalization (1992)
J. E. Moody, S. J. Hanson, Anders Krogh, John A. Hertz
It has been observed in numerical simulations that a weight decay can improve generalization in a feed-forward neural network. This paper explains why. It is proven that a weight decay has two...
Using Database Matches with HMMGene for Automated Gene Detection in Drosophila
The application of the gene finder HMMGene to the Adh region of the Drosophila melanogaster is described, and the prediction results are analyzed. HMMGene is based on a probabilistic model called a...
A hidden Markov model approach for determining expression from genomic tiling micro arrays
Munch, Kasper, Gardner, Paul P, Arctander, Peter, Krogh, Anders
PONGO: a web server for multiple predictions of all-alpha transmembrane proteins
Amico, Mauro, Finelli, Michele, Rossi, Ivan, Zauli, Andrea, Elofsson, Arne, Viklund, Håkan, ...
The annotation efforts of the BIOSAPIENS European Network of Excellence have generated several distributed annotation systems (DAS) with the aim of integrating Bioinformatics resources and annotating...
Sampling Realistic Protein Conformations Using Local Structural Bias
Hamelryck, Thomas, Kent, John T, Krogh, Anders
The prediction of protein structure from sequence remains a major unsolved problem in biology. The most successful protein structure prediction methods make use of a divide-and-conquer strategy to...
Using Database Matches with HMMGene for Automated Gene Detection in Drosophila
The application of the gene finder HMMGene to the Adh region of the Drosophila melanogaster is described, and the prediction results are analyzed. HMMGene is based on a probabilistic model called a...
A hidden Markov model approach for determining expression from genomic tiling micro arrays
Munch, Kasper, Gardner, Paul P, Arctander, Peter, Krogh, Anders
PONGO: a web server for multiple predictions of all-alpha transmembrane proteins
Amico, Mauro, Finelli, Michele, Rossi, Ivan, Zauli, Andrea, Elofsson, Arne, Viklund, Håkan, ...
The annotation efforts of the BIOSAPIENS European Network of Excellence have generated several distributed annotation systems (DAS) with the aim of integrating Bioinformatics resources and annotating...
Sampling Realistic Protein Conformations Using Local Structural Bias
Hamelryck, Thomas, Kent, John T, Krogh, Anders
The prediction of protein structure from sequence remains a major unsolved problem in biology. The most successful protein structure prediction methods make use of a divide-and-conquer strategy to...
Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server
Käll, Lukas, Krogh, Anders, Sonnhammer, Erik L.L.
When using conventional transmembrane topology and signal peptide predictors, such as TMHMM and SignalP, there is a substantial overlap between these two types of predictions. Applying these methods...
An evolutionary method for learning HMM structure: prediction of protein secondary structure
Won, Kyoung-Jae, Hamelryck, Thomas, Prügel-Bennett, Adam, Krogh, Anders
Intragenomic Matching Reveals a Huge Potential for miRNA-Mediated Regulation in Plants
Lindow, Morten, Jacobsen, Anders, Nygaard, Sanne, Mang, Yuan, Krogh, Anders
microRNAs (miRNAs) are important post-transcriptional regulators, but the extent of this regulation is uncertain, both with regard to the number of miRNA genes and their targets. Using an algorithm...
Bryne, Jan Christian, Valen, Eivind, Tang, Man-Hung Eric, Marstrand, Troels, Winther, Ole, Da Piedade, Isabelle, ...
JASPAR is a popular open-access database for matrix models describing DNA-binding preferences for transcription factors and other DNA patterns. With its third major release, JASPAR has been expanded...
Asap: A Framework for Over-Representation Statistics for Transcription Factor Binding Sites
Marstrand, Troels T., Frellsen, Jes, Moltke, Ida, Thiim, Martin, Valen, Eivind, Retelska, Dorota, ...
Prediction of lipoprotein signal peptides in Gram-negative bacteria
Juncker, Agnieszka S., Willenbrock, Hanni, Von Heijne, Gunnar, Brunak, Søren, Nielsen, Henrik, Krogh, Anders
A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved...
A code for transcription initiation in mammalian genomes
Frith, Martin C., Valen, Eivind, Krogh, Anders, Hayashizaki, Yoshihide, Carninci, Piero, Sandelin, Albin
Genome-wide detection of transcription start sites (TSSs) has revealed that RNA Polymerase II transcription initiates at millions of positions in mammalian genomes. Most core promoters do not have a...
A generative, probabilistic model of local protein structure
Boomsma, Wouter, Mardia, Kanti V., Taylor, Charles C., Ferkinghoff-Borg, Jesper, Krogh, Anders, Hamelryck, Thomas
Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is...
Anders Krogh, S��ren Kamaric Riis
A general framework for hybrids of Hidden Markov models (HMMs) and neural networks (NNs) called Hidden Neural Networks (HNNs) is described. The paper begins by reviewing standard HMMs and estimation...
Anders Krogh, Søren Kamaric Riis, S��ren Kamaric Riis
A general framework for hybrids of Hidden Markov models (HMMs) and neural networks (NNs) called Hidden Neural Networks (HNNs) is described. The paper begins by reviewing standard HMMs and estimation...
Genome-wide detection and analysis of hippocampus core promoters using DeepCAGE
Valen, Eivind, Pascarella, Giovanni, Chalk, Alistair, Maeda, Norihiro, Kojima, Miki, Kawazu, Chika, ...
Finding and characterizing mRNAs, their transcription start sites (TSS), and their associated promoters is a major focus in post-genome biology. Mammalian cells have at least 5–10 magnitudes more...
Discovery of Regulatory Elements is Improved by a Discriminatory Approach
Valen, Eivind, Sandelin, Albin, Winther, Ole, Krogh, Anders
A major goal in post-genome biology is the complete mapping of the gene regulatory networks for every organism. Identification of regulatory elements is a prerequisite for realizing this ambitious...