Knowledge discovery in databases — An inductive logic programming approach

1997 ◽  
pp. 429-436 ◽  
Author(s):  
Katharina Morik
Keyword(s):  
Logic Programming ◽  
Knowledge Discovery ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Knowledge Discovery In Databases ◽  
Programming Approach

scholarly journals Biochemical Knowledge Discovery Using Inductive Logic Programming

1998 ◽  
pp. 326-341 ◽  
Author(s):  
Stephen Muggleton ◽  
Ashwin Srinivasan ◽  
R. D. King ◽  
M. J. E. Sternberg
Keyword(s):  
Logic Programming ◽  
Knowledge Discovery ◽  
Inductive Logic Programming ◽  
Inductive Logic

Inductive logic programming for relational knowledge discovery

1999 ◽  
Vol 17 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Nada Lavrač ◽  
Saŝo Džeroski ◽  
Masayuki Numao
Keyword(s):  
Logic Programming ◽  
Knowledge Discovery ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Relational Knowledge

scholarly journals Knowledge discovery for pancreatic cancer using inductive logic programming

2014 ◽  
Vol 8 (4) ◽  
pp. 162-168 ◽  
Author(s):  
Yushan Qiu ◽  
Kazuaki Shimada ◽  
Nobuyoshi Hiraoka ◽  
Kensei Maeshiro ◽  
Wai‐Ki Ching ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Logic Programming ◽  
Knowledge Discovery ◽  
Inductive Logic Programming ◽  
Inductive Logic

Inductive logic programming for knowledge discovery from MRI data

2000 ◽  
Vol 19 (4) ◽  
pp. 72-77 ◽  
Author(s):  
Anna Siromoney ◽  
L. Raghuram ◽  
Arul Siromoney ◽  
I. Korah ◽  
G.N.S. Prasad
Keyword(s):  
Logic Programming ◽  
Knowledge Discovery ◽  
Inductive Logic Programming ◽  
Inductive Logic

scholarly journals Knowledge Discovery in Variant Databases Using Inductive Logic Programming

2013 ◽  
Vol 7 ◽  
pp. BBI.S11184 ◽  
Author(s):  
Hoan Nguyen ◽  
Tien-Dao Luu ◽  
Olivier Poch ◽  
Julie D. Thompson
Keyword(s):  
Logic Programming ◽  
Knowledge Discovery ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Single Amino Acid ◽  
Monogenic Disease ◽  
3D Protein Structure ◽  
Evolutionary Features ◽  
Monogenic Diseases ◽  
The Impact

Understanding the effects of genetic variation on the phenotype of an individual is a major goal of biomedical research, especially for the development of diagnostics and effective therapeutic solutions. In this work, we describe the use of a recent knowledge discovery from database (KDD) approach using inductive logic programming (ILP) to automatically extract knowledge about human monogenic diseases. We extracted background knowledge from MSV3d, a database of all human missense variants mapped to 3D protein structure. In this study, we identified 8,117 mutations in 805 proteins with known three-dimensional structures that were known to be involved in human monogenic disease. Our results help to improve our understanding of the relationships between structural, functional or evolutionary features and deleterious mutations. Our inferred rules can also be applied to predict the impact of any single amino acid replacement on the function of a protein. The interpretable rules are available at http://decrypthon.igbmc.fr/kd4v/ .

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