| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/bmcbi/WonHPK07 |
| P6179 | Dimensions Publication ID | 1030829775 |
| P356 | DOI | 10.1186/1471-2105-8-357 |
| P3181 | OpenCitations bibliographic resource ID | 3562973 |
| P932 | PMC publication ID | 2072961 |
| P698 | PubMed publication ID | 17888163 |
| P5875 | ResearchGate publication ID | 5956187 |
| P50 | author | Anders Krogh | Q4753847 |
| Thomas Hamelryck | Q51369500 | ||
| Kyoung-Jae Won | Q80422438 | ||
| Adam Prugel-Bennett | Q112549452 | ||
| P2860 | cites work | Gapped BLAST and PSI-BLAST: a new generation of protein database search programs | Q24545170 |
| Protein secondary structure prediction based on position-specific scoring matrices | Q27860483 | ||
| Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features | Q27860675 | ||
| Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins | Q27860987 | ||
| Prediction of the Secondary Structure of Proteins from their Amino Acid Sequence | Q28131736 | ||
| Learning representations by back-propagating errors | Q29469983 | ||
| Prediction of protein secondary structure at better than 70% accuracy | Q29547323 | ||
| Stochastic motif extraction using hidden Markov model | Q52220789 | ||
| Hidden neural networks. | Q52224744 | ||
| Two methods for improving performance of an HMM and their application for gene finding. | Q52282383 | ||
| Prediction of protein secondary structure by the hidden Markov model. | Q52398040 | ||
| Simple consensus procedures are effective and sufficient in secondary structure prediction. | Q54564327 | ||
| Evolving the structure of hidden Markov models | Q57029395 | ||
| Training HMM structure with genetic algorithm for biological sequence analysis | Q57029431 | ||
| Algorithms for prediction of alpha-helical and beta-structural regions in globular proteins | Q68822331 | ||
| Position-based sequence weights | Q72819629 | ||
| SABmark--a benchmark for sequence alignment that covers the entire known fold space | Q80500501 | ||
| Application of multiple sequence alignment profiles to improve protein secondary structure prediction | Q29614377 | ||
| Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. | Q30330078 | ||
| HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. | Q30894197 | ||
| Automatic extraction of motifs represented in the hidden Markov model from a number of DNA sequences | Q32060619 | ||
| PDB file parser and structure class implemented in Python | Q33195213 | ||
| Protein secondary structure prediction for a single-sequence using hidden semi-Markov models | Q33237952 | ||
| Analysis of an optimal hidden Markov model for secondary structure prediction | Q33266463 | ||
| UniProt archive | Q33976927 | ||
| EVA: large-scale analysis of secondary structure prediction | Q34113376 | ||
| Predicting the secondary structure of globular proteins using neural network models | Q34168624 | ||
| Exploiting the past and the future in protein secondary structure prediction | Q40771308 | ||
| A novel method for protein secondary structure prediction using dual-layer SVM and profiles. | Q45966836 | ||
| A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. | Q45967263 | ||
| A simple and fast secondary structure prediction method using hidden neural networks. | Q48523184 | ||
| Secondary structure prediction with support vector machines. | Q48581112 | ||
| P275 | copyright license | Creative Commons Attribution 2.0 Generic | Q19125117 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P433 | issue | 1 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | protein structure | Q735188 |
| protein structure prediction | Q899656 | ||
| P304 | page(s) | 357 | |
| P577 | publication date | 2007-09-21 | |
| P1433 | published in | BMC Bioinformatics | Q4835939 |
| P1476 | title | An evolutionary method for learning HMM structure: prediction of protein secondary structure | |
| P478 | volume | 8 |
| Q28086832 | A review of three different studies on hidden markov models for epigenetic problems: a computational perspective |
| Q31061020 | Evolving stochastic context--free grammars for RNA secondary structure prediction |
| Q37700192 | Hidden Markov Models and their Applications in Biological Sequence Analysis |
| Q39256768 | Improving protein secondary structure prediction using a simple k-mer model |
| Q47963481 | Introduction to Hidden Markov Models and Its Applications in Biology |
| Q28743494 | Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure |
| Q30152659 | Predicting Beta Barrel Transmembrane Proteins Using HMMs |
| Q55516095 | Protein Secondary Structure Prediction Based on Data Partition and Semi-Random Subspace Method. |
| Q30392787 | Protein secondary structure prediction using a small training set (compact model) combined with a Complex-valued neural network approach |
| Q35618898 | SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles |
| Q30382761 | Template-based protein modeling: recent methodological advances. |
| Q42278478 | The identification of novel cyclic AMP-dependent protein kinase anchoring proteins using bioinformatic filters and peptide arrays |
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