The DOI matches its Crossref record; 25 of the 28 references it cites were found in a registry or an index.
The Effect of Relational Background Knowledge on Learning of Protein Three-Dimensional Fold Signatures
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ILP has been applied to learning protein folding signatures.
“learning protein folding signatures (Turcotte et al., 2001)”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Turcotte et al., 2001
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1992) · Cited by 23,431
The structure of proteins: Two hydrogen-bonded helical configurations of the polypeptide chain
Pauling, L. C., Corey, R. B., & Branson, H. R. (1951) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.37.4.205 · Cited by 3,007
Introduction to Protein Structure
Brändén, C.-I., & Tooze, J. (2012) · Garland Science eBooks · doi:10.1201/9781136969898 · Cited by 2,398
Creighton, T. E. (1992) · Cited by 1,583
Inductive Logic Programming: Theory and methods
Muggleton, S., & de Raedt, L. (1994) · The Journal of Logic Programming · doi:10.1016/0743-1066(94)90035-3 · Cited by 1,537
Self-citationEnhanced genome annotation using structural profiles in the program 3D-PSSM 1 1Edited by J. Thornton
Kelley, L. A., MacCallum, R. M., & Sternberg, M. J. E. (2000) · Journal of Molecular Biology · doi:10.1006/jmbi.2000.3741 · Cited by 1,464
Self-citationWierenga, R. K., Terpstra, P., & Hol, W. G. J. (1986) · Journal of Molecular Biology · doi:10.1016/0022-2836(86)90409-2 · Cited by 1,159
Introduction to Protein Structure
Banaszak, L. J. (2000) · Elsevier eBooks · doi:10.1016/b978-012077700-6/50001-8 · Cited by 1,063
PROMOTIF—A program to identify and analyze structural motifs in proteins
Hutchinson, E. G., & Thornton, J. M. (1996) · Protein Science · doi:10.1002/pro.5560050204 · Cited by 1,054
Protein superfamilles and domain superfolds
Orengo, C. A., Jones, D. T., & Thornton, J. M. (1994) · Nature · doi:10.1038/372631a0 · Cited by 783
Determinants of a protein fold
Bashford, D., Chothia, C., & Lesk, A. M. (1987) · Journal of Molecular Biology · doi:10.1016/0022-2836(87)90521-3 · Cited by 496
Theories for mutagenicity: a study in first-order and feature-based induction
Srinivasan, A., Muggleton, S., Sternberg, M. J. E., & King, R. D. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(95)00122-0 · Cited by 298
Self-citationProtein secondary structure prediction using logic-based machine learning
Muggleton, S., King, R. D., & Stenberg, M. (1992) · Protein Engineering Design and Selection · doi:10.1093/protein/5.7.647 · Cited by 246
Self-citationKing, R. D., Muggleton, S., Srinivasan, A., & Sternberg, M. J. E. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.1.438 · Cited by 225
Self-citationKing, R. D., Muggleton, S., Lewis, R. A., & Sternberg, M. J. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.23.11322 · Cited by 219
Self-citationKuntz, I. D. (1972) · Journal of the American Chemical Society · doi:10.1021/ja00766a060 · Cited by 183
Structural comparisons among the short-chain helical cytokines
Rozwarski, D. A., Gronenborn, A. M., Clore, G. M., Bazán, J. F., Bohm, A., Wlodawer, A., Hatada, M. H., & Karplus, P. A. (1994) · Structure · doi:10.1016/s0969-2126(00)00018-6 · Cited by 166
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
Finn, P., Muggleton, S., Page, D., & Srinivasan, A. (1998) · Machine Learning · doi:10.1023/a:1007460424845 · Cited by 138
Self-citationShining a light on structural genomics
Kim, S. H. (1998) · Nature Structural Biology · doi:10.1038/1334 · Cited by 120
Carcinogenesis predictions using ILP
Srinivasan, A., King, R. D., Muggleton, S., & Sternberg, M. J. E. (1997) · Lecture notes in computer science · doi:10.1007/3540635149_56 · Cited by 116
Self-citation[37] Understanding protein structure: Using scop for fold interpretation
Brenner, S. E., Chothia, C., Hubbard, T., & Murzin, A. G. (1996) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(96)66039-x · Cited by 100
The Computer-Aided Discovery of Scientific Knowledge
Pat Langley, B. (1998) · Lecture notes in computer science · doi:10.1007/3-540-49292-5_3 · Cited by 58
Application of machine learning to structural molecular biology
Sternberg, M. J. E., King, R. D., Lewis, R. A., & Muggleton, S. (1994) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.1994.0075 · Cited by 36
Self-citationProtein secondary structure prediction using logic-based machine learning
Muggleton, S., King, R. D., & Sternberg, M. J. E. (1993) · Protein Engineering Design and Selection · doi:10.1093/protein/6.5.549 · Cited by 23
Self-citationTheories for mutagenicity: a study in first-order and feature-based induction
Srinivasan, A., Muggieton, S. H., Sternberg, M. J. E., & King, R. D. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)81369-5 · Cited by 18
Self-citation
Flagged first, then by citations
3 of the 28 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Bourne, P. E. (1998). Editorial. Bioinformatics, 15(9), 715–716.
- Muggleton, S.& Firth, J. (in press). CProgol4.4: Theory and use. In S. Džeroski& N. Lavrac (Eds.), Inductive Logic Programing and Knowledge Discovery in Databases.
Cited by
Sorted by citations
Three-dimensional protein structure prediction: Methods and computational strategies
Dorn, M., Silva, M. B. E., Buriol, L. S., & Lamb, L. C. (2014) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2014.10.001 · Cited by 220
Inductive Logic Programming At 30: A New Introduction
Cropper, A., & Dumančić, S. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13507 · Cited by 83
“, 1997, 1996), learning protein folding signatures (Turcotte et al., 2001), inferring missing pathways in protein signalling networks (Inoue et al.”
BackgroundBorguesan, B., Silva, M. B. E., Grisci, B. I., Inostroza-Ponta, M., & Dorn, M. (2015) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2015.08.006 · Cited by 45
Automated discovery of structural signatures of protein fold and function11Edited by J. Thornton
Turcotte, M., Muggleton, S., & Sternberg, M. J. E. (2001) · Journal of Molecular Biology · doi:10.1006/jmbi.2000.4414 · Cited by 37
Self-citationKernels on Prolog Proof Trees:Statistical Learning in the ILP Setting
Passerini, A., Frasconi, P., & Raedt, L. D. (2006) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/dagsemproc.05051.8 · Cited by 36
Data Mining for Financial Applications
Kovalerchuk, B., & Vityaev, E. E. (2009) · Springer eBooks · doi:10.1007/978-0-387-09823-4_60 · Cited by 28
An inductive logic programming approach to statistical relational learning
Kersting, K. (2006) · Cited by 22
Relational Data Mining Applications: An Overview
Džeroski, S. (2001) · Springer eBooks · doi:10.1007/978-3-662-04599-2_14 · Cited by 20
ALLPAD: approximate learning of logic programs with annotated disjunctions
Riguzzi, F. (2007) · Machine Learning · doi:10.1007/s10994-007-5032-8 · Cited by 20
Kristian Kersting, B., Raedt, L. D., Gutmann, B., Karwath, A., & Landwehr, N. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-78652-8_2 · Cited by 19
Data Mining for Financial Applications
Kovalerchuk, B., & Vityaev, E. E. (2006) · Springer eBooks · doi:10.1007/0-387-25465-x_57 · Cited by 17
Learning with Kernels and Logical Representations
Frasconi, P. (2008) · Springer eBooks · doi:10.1007/978-3-540-78469-2_1 · Cited by 9
Generating protein three-dimensional fold signatures using inductive logic programming
Turcotte, M., Muggleton, S., & Sternberg, M. J. E. (2001) · Computers & Chemistry · doi:10.1016/s0097-8485(01)00100-0 · Cited by 9
Self-citationLearning with Kernels and Logical Representations
Frasconi, P., & Passerini, A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-78652-8_3 · Cited by 8
Jiline, M., Matwin, S., & Turcotte, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr337 · Cited by 7
Self-citationUsing Previous Experience for Learning Planning Control Knowledge.
Fernández, S., Aler, R., & Borrajo, D. (2004) · Cited by 6
Wavelet Packet Bases for Speaker Recognition
Siafarikas, M., Ganchev, T. D., & Fakotakis, N. (2007) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2007.97 · Cited by 5
ILP Meets Knowledge Engineering: A Case Study
Lisi, F. A., & Esposito, F. (2005) · Lecture notes in computer science · doi:10.1007/11536314_13 · Cited by 4
Multi-class Prediction Using Stochastic Logic Programs
Chen, J., Kelley, L. A., Muggleton, S., & Sternberg, M. J. E. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73847-3_17 · Cited by 4
Self-citationReasoning with OWL-DL in Inductive Logic Programming.
Lisi, F. A. (2007) · Cited by 3
Protein Fold Discovery Using Stochastic Logic Programs
Chen, J., Kelley, L. A., Muggleton, S., & Sternberg, M. J. E. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-78652-8_9 · Cited by 3
Self-citationKristian Kersting, B., Raiko, T., Kramer, S. J., & Raedt, L. D. (2002) · Cited by 3
ALLPAD: Approximate Learning of Logic Programs with Annotated Disjunctions
Riguzzi, F. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73847-3_11 · Cited by 3
Logic and the Automatic Acquisition of Scientific Knowledge: An Application to Functional Genomics
King, R. D., Karwath, A., Clare, A. J., & Dehaspe, L. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73920-3_13 · Cited by 2
Aprendizaje Automatico de Programas Logico-Funcionales
Ferri, C., Hernandez, J., & Ramírez-Quintana, M. J. (2006) · INTELIGENCIA ARTIFICIAL · doi:10.4114/ia.v4i11.688 · Cited by 2
Induction as a Search Procedure
Konstantopoulos, S., Camacho, R. C., Fonseca, N. A., & Costa, V. S. (2008) · IGI Global eBooks · doi:10.4018/978-1-59904-705-8.ch007 · Cited by 1
Supervised learning with implicit preferences and constraints
Gomes, C. P., & Guo, Y. (2010) · eCommons (Cornell University)
ModuleInducer: Automating the Extraction of Knowledge from Biological Sequences
Korol, O. (2011) · Library and Archives Canada (Government of Canada) · doi:10.20381/ruor-4947
Clustering Relational Data: A Transactional Approach
Costa, G., Cuzzocrea, A., Manco, G., & Ortale, R. (2009) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2009.19
Bayesian Inference in Nonlinear and Relational Latent Variable Models
Raiko, T. (2006) · Aaltodoc (Aalto University) · Cited by 1
Metadata
- Authors
- Turcotte, M., Muggleton, S. H., & Sternberg, M. J. E.
- Affiliations
- University of York (GB)
- Venue
- Machine Learning
- Published
- 2001-04
- Volume
- 43
- Issue
- 1-2
- Pages
- 81-95
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1496361947
- Cited by
- 30
- References
- 28
- Citations by year
- 2022: 1 · 2015: 1 · 2014: 1
- FWCI
- 0.58
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Biotechnology and Biological Sciences Research Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- inductive logic programming, Progol, protein 3D structure, structural biology, cross-validation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review