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A machine learning system for identifying transmembrane domains from amino acid sequences

  • Intelligent systems
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Abstract

We present our machine learning system, that uses inductive logic programming techniques to learn how to identify transmembrane domains from amino acid sequences. Our system facilitates the use of operators such as ‘contains’, that act on entire sequences, rather than on individual elements of a sequence. The prediction accuracy of our new system is around 93%, and this compares favourably with earlier results.

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References

  • Arikawa S, Kuhara S, Miyano S, Mukouchi Y, Shinohara A, Shinohara T 1992 A machine discovery from amino acid sequences by decision trees over regular patterns.Proceedings of the International Conference on Fifth Generation Computer Systems, pp 618–625

  • King R, Muggleton S, Lewis R, Sternberg M 1992 Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.Proc. Natl. Acad. Sci. USA 89: 11322–11326

    Article  Google Scholar 

  • Michalski R S 1983 A theory and methodology of inductive learning.Artif. Intell. 20: 111–161

    Article  MathSciNet  Google Scholar 

  • Michalski R S, Carbonell J G, Mitchell T M (eds) 1983 An overview of machine learning. InMachine learning — An artificial intelligence approach (Palo Alto, CA: Tioga) vol. 1, pp 3–24

    Google Scholar 

  • Muggleton S H 1991 Inductive logic programming.New Generation Comput. 8: 295–318

    Article  MATH  Google Scholar 

  • Muggleton S H, Feng C 1990 Efficient induction of logic programs.Proceedings of the First International Workshop on Algorithmic Learning Theory (Tokyo: Ohmsha) pp 368–81

    Google Scholar 

  • Muggleton S H, King R D, Sternberg M J E 1992 Protein secondary structure prediction using logic-based machine learning.Protein Eng. 5: 647–657

    Article  Google Scholar 

  • Quinlan J R 1986 Induction of decision trees.Mach. Learning 1: 81–106

    Google Scholar 

  • Quinlan J R 1990 Learning logical definitions from relations.Mach. Learning 5: 239–266

    Google Scholar 

  • Quinlan J R 1991 Knowledge acquisition from structured data.IEEE Expert 6(6): 32–37

    Article  Google Scholar 

  • Sakakibara Y, Siromoney R 1992 A noise model on learning sets of strings.Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory (ACM Press) pp 295–302

  • Shimozono S, Shinohara A, Shinohara T, Miyano S, Kuhara S, Arikawa S 1993 Finding alphabet indexing for decision trees over regular patterns: An approach to bioinformatical knowledge acquisition.Proceedings of the 26th Hawaii International Conference on System Sciences, pp 763–772

  • Siromoney A, Siromoney R 1993 Local exceptions in inductive logic programming. Presented at the International Workshop on Machine Intelligence, ARL Labs, Hitachi, Japan

  • Siromoney A, Siromoney R 1995 Variations and local exceptions in inductive logic programming.Machine intelligence (eds) K Furukawa, D Michie, S Muggleton (Oxford: Clarendon) vol. 14, pp 211–232

    Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Engineering, Anna University, 600 025, Madras, India

    Arul Siromoney

  2. Madras Christian College, 600 059, Tambaram, Madras, India

    Rani Siromoney

Authors
  1. Arul Siromoney
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  2. Rani Siromoney
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Additional information

This work was carried out with the support of a research grant from ISIS, Fujitsu Laboratories.

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Siromoney, A., Siromoney, R. A machine learning system for identifying transmembrane domains from amino acid sequences. Sadhana 21, 317–325 (1996). https://doi.org/10.1007/BF02745526

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