Data-driven theory refinement algorithms for bioinformatics

Bioinformatics and related applications call for efficient algorithms for knowledge-intensive learning and data-driven knowledge refinement. Knowledge based artificial neural networks offer an attractive approach to extending or modifying incomplete knowledge bases or domain theories. We present results of experiments with several such algorithms for data-driven knowledge discovery and theory refinement in some simple bioinformatics applications. Results of experiments on the ribosome binding site and promoter site identification problems indicate that the performance of KBDistAl and Tiling-Pyramid algorithms compares quite favorably with those of substantially more computationally demanding techniques.

[1]  Dekang Lin,et al.  An Information-Theoretic Definition of Similarity , 1998, ICML.

[2]  Vasant Honavar,et al.  Distributed knowledge networks , 1998, 1998 IEEE Information Technology Conference, Information Environment for the Future (Cat. No.98EX228).

[3]  Ronen Feldman,et al.  Bias-Driven Revision of Logical Domain Theories , 1993, J. Artif. Intell. Res..

[4]  Enrico Gobbetti,et al.  Encyclopedia of Electrical and Electronics Engineering , 1999 .

[5]  Rajesh Parekh,et al.  Constructive Neural Network Learning Algorithms for Multi-Category Real-Valued Pattern Classification , 1997 .

[6]  Stephen I. Gallant,et al.  Perceptron-based learning algorithms , 1990, IEEE Trans. Neural Networks.

[7]  DistAl: An inter-pattern distance-based constructive learning algorithm , 1999, Intell. Data Anal..

[8]  Allen Ginsberg,et al.  Theory Reduction, Theory Revision, and Retranslation , 1990, AAAI.

[9]  Vasant Honavar.  Machine learning: Principles and applications , 1999 .

[10]  Rajesh Parekh,et al.  Constructive Theory Reenement in Knowledge Based Neural Networks , 1998 .

[11]  Li-Min Fu,et al.  Knowledge-based connectionism for revising domain theories , 1993, IEEE Trans. Syst. Man Cybern..

[12]  Zoran Obradovic,et al.  Combining Prior Symbolic Knowledge and Constructive Neural Network Learning , 1993 .

[13]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[14]  Munindar P. Singh,et al.  Agents on the Web: Mobile Agents , 1997, IEEE Internet Comput..

[15]  Raymond J. Mooney,et al.  Comparing Methods for Refining Certainty-Factor Rule-Bases , 1994, ICML.

[16]  Raymond J. Mooney,et al.  Theory Refinement Combining Analytical and Empirical Methods , 1994, Artif. Intell..

[17]  David W. Opitz,et al.  Dynamically adding symbolically meaningful nodes to knowledge-based neural networks , 1995, Knowl. Based Syst..

[18]  L.-M. Fu,et al.  Integration of neural heuristics into knowledge-based inference , 1989, International 1989 Joint Conference on Neural Networks.

[19]  Jude Shavlik,et al.  A Framework for Combining Symbolic and Neural Learning , 1992 .

[20]  Mark Craven,et al.  Extracting comprehensible models from trained neural networks , 1996 .

[21]  Larry A. Rendell,et al.  Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach , 1994, J. Artif. Intell. Res..

[22]  Rajesh Parekh,et al.  Constructive theory refinement in knowledge based neural networks , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[23]  Jude W. Shavlik,et al.  Knowledge-Based Artificial Neural Networks , 1994, Artif. Intell..

[24]  Sebastian Thrun,et al.  Lifelong Learning: A Case Study. , 1995 .

[25]  Jude Shavlik,et al.  Refinement ofApproximate Domain Theories by Knowledge-Based Neural Networks , 1990, AAAI.

[26]  Eric B. Baum,et al.  Constructing Hidden Units Using Examples and Queries , 1990, NIPS.

[27]  Raymond J. Mooney,et al.  Automated refinement of first-order horn-clause domain theories , 2005, Machine Learning.

[28]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[29]  Jihoon Yang,et al.  Data-Driven Theory Refinement Using KBDistAl , 1999, IDA.

[30]  David W. Opitz,et al.  Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies , 1997, J. Artif. Intell. Res..

[31]  Marcus R. Frean,et al.  A "Thermal" Perceptron Learning Rule , 1992, Neural Computation.

[32]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .