From goldenjb@ctrvax.vanderbilt.edu Wed May 11 15:40:32 EDT 1994 Article: 16341 of comp.ai.neural-nets Xref: glinda.oz.cs.cmu.edu comp.ai.neural-nets:16341 Newsgroups: comp.ai.neural-nets Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!gatech!howland.reston.ans.net!europa.eng.gtefsd.com!darwin.sura.net!news.Vanderbilt.Edu!NewsWatcher!user From: goldenjb@ctrvax.vanderbilt.edu (jim golden) Subject: Bibliography: Adaptive Systems and Mol Bio Message-ID: <goldenjb-030594155447@129.59.170.62> Followup-To: comp.ai.neural-nets Sender: news@news.vanderbilt.edu Nntp-Posting-Host: 129.59.170.62 Organization: vanderbilt Date: Tue, 3 May 1994 20:42:22 GMT Lines: 190 Greetings. The following is a bibliography I began putting together for my dissertation regarding the use of adaptive systems (NN, GA, GP) for approaching interesting problems in molecular biology. Quite a few people have asked me for this bib. so I thought I would post it here with a cross posting in comp.ai.genetic. I am a mechanical engineering graduate student working with micro/molecular biologists on the problem of DNA sequencing and have found that mol. bio. is an area rich with interesting problems that could benefit from techniques available from the AI community. There is some poetic justice in using genetic programming to enhance genetic engineering. What follows is an incomplete bibliography of papers that I have found discussing adaptive systems and a few of the problems of interest to biologists. I have concentrated on NN and GP and ignored some very interesting work using different grammars, immune nets, case-based reasoning, expert systems, etc. My interest is in DNA / protein sequencing and this bib. may be a little heavy in that area. If I have missed a seminal paper or someone's work, I apologize. This is just what I have put together and found useful. I have not attempted to standardize bibliography style but tended to present them as I found them, please forgive errors of style and spelling. Please feel free to pass this around and cross post as you like. Jim Golden goldenjb@ctrvax.vanderbilt.edu Dept. of Mechanical Engineering Vanderbilt University --------------------------------------------------------------------------------------------- Proceedings of the First International Conference on Intelligent Systems for Molecular Biology; edited by Lawrence Hunter, David Searls and Jude Shavlik. July 6 - 9 1993, National Library of Medicine. AAAI Press. ISBN 0-929280-47-4. --- This is a great set of proceedings and many interesting references can be found there. I highly recommend purchasing these proceedings as a source of references. Here are a few papers presented at this conference: Delcher, A., Kasif, H., Goldberg, R., Hsu, W.; Protein Secondary-Structure Modeling with Probabilistic Networks. pg. 109. Dubchak, I., Holbrook, S., Kim, S.; Comparison of Two Variations of Neural Network Approaches to the Prediction of Protein Folding Pattern. pg. 118. Ferran, E., Pflugfelder, B., Ferrara, P.; Protein Classification Using Neural Networks. pg. 127. Golden, J., Torgersen, D., Tibbetts, C.; Pattern Recognition for Automated DNA Sequencing I. On-Line Signal Conditioning and Feature Extraction for Basecalling. pg. 136. Guidi, J., Roderick, T.; Inference of Order in Genetic Systems. pg. 163. Hunter, L., Klein, T.; Finding Relevant Biomolecular Features. pg. 190. Parsons, R., Forrest, S., and Burks, C.; Genetic Algorithms for DNA Sequence Assembly. pg. 310. Vanhala, J., Kaski, K.; Protein Structure Prediction System Based on Artificial Neural Networks. pg. 402. Veretnik, S., Schatz, B.; Pattern Discovery in Gene Regulation: Designing an Analysis Environment. pg. 411. Wu, C., Berry, M., Fung, Y-S., McLarty, J.; Neural Networks for Molecular Sequence Classification. pg. 429 ------------------------------------------------------------------------------------------------ Artificial Intelligence and Molecular Biology; edited by Lawrence Hunter. AAAI Press (1993) ISBN 0-262-58115-9. --- An excellent book covering many areas of AI which includes a chapter on Molecular Biology for Computer Scientists (2nd in value only to the Cartoon Guide to Genetics!). Some papers in this reference are: Searls, D.; The Computational Linguistics of Biological Sequences. pg. 47. Steeg, E.; Neural Networks, Adaptive Optimization, and RNA Secondary Structure Prediction. pg. 121. Holbrook, S.; Muskal, S., Kim, S-H.; Predicting Protein Structural features With Artificial Neural Networks. pg. 161. Lathrop, R. et. al., (5 authors); Integrating AI with Sequence Analysis. pg. 210. Edwards, P., Sleeman, D., Roberts, G, Lian, L.,; An AI approach to the Interpretation of the NMR Spectra of Proteins. pg. 396. --- A final chapter by Joshua Lederberg titled: The Anti-Expert System - Thirteen Hypothesis an AI Program Should Have Seen Through. --- is an excellent discussion of how we know if our system is working correctly and completely, something the AI community (myself included) ignores all too frequently. ------------------------------------------------------------------------------------ Here are a few more papers I've come across in my literature search: Andreassen, H. et. al. (12 authors); Analysis of the secondary structure of the human immunodeficiency virus (HIV) proteins p 17, gp 120, and gp 41 by computer modeling based on neural network methods. J. of Aquired Immune Deficiency Syndromes 3 (1990); 615-622. Arrigo, P. et. al. (5 authors); Identification of a new motif on nucleic acid sequence data using Kohonen's self-organizing map. CABIOS, Vol. 7, no. 3 (1991). pp 353-357. Bengio, Y., Pouliot, Y.; Effecient recognition of immunoglubulin domains from amino acid sequences using a neural network. CABIOS, 6(4) (1990): 319-324. Bohr, H. et. al. (8 authors); Protein secondary structure and homology by neural networks. The alpha-helices in rhodopsin. FEBS Letters 241(1,2) (1988): 223-228. Bohr, H. et. al. (7 authors); A novel approach to prediction of the 3-dimensional sturctures of protein backbones by neural networks. FEBS Letters 261(1) (1990): 43-46. Brunak, S., Engelbrecht, J., Knudsen, S.; Neural network detects errors in the assignment of mRNA splice sites. Nucleic Acids Research 18(16) (1990): 4797-4801. Brunak, S., Englebrecht, J., Knudsen, S.; Prediction of human mRNA donor and acceptor sites from the DNA sequence. J. Mol. Bio. 220 (1991): pp. 49-65. Burks, C. et. al. (5 authors); Stochastic optimization tools for genomic sequence assembly. In Automated DNA Sequencing and Analysis Techniques, Venter, J. editor. In Press. Churchill, G. et. al. (5 authors) Assembling DNA sequence fragments by shuffling and simulated annealing. (1993) Manuscript in Prep. Farber, R., Lapedes, A., Sirotkin, K.; Determination of eukaryotic protein coding regions using neural networks and information theory. J. Mol. Bio. 226 (1992): pp. 471-479. Ferran, E., Ferrara, P.; Clustering proteins into families using artificial neural networks. CABIOS, 8(1) (1992): 39-44. Ferran, E., Ferrara, P.; A neural network dynamics that resembles protein evolution. Physica A 185 (1992): 395-401. Fickett, J., Cinkosky, M.; A genetic algorithm for assembling chromosome physical maps. In Proc. of 2nd Int. Conf. on Bioinformatics, Supercomputing, and Complete Genome Analysis. Cantor, C. and Robbins, R. editors. World Scientific. Frishman, D., Argos, P.; Recognition of distantly related protein sequences using conserved motifs and neural networks. J. Mol. Bio. 228 (1992): 951-962. Holley, L., Karplus, M.; Protein secondary structure prediction with a neural network. Proc. Natl. Acad. Sci. USA, Vol. 86 (1989) pp. 152-156. Kneller, D., Cohen, F., Langridge, R.; Improvement in Protein secondary structure prediction by an enhanced neural network. J. Mol. Bio. (1990). 214(1), pp. 171-182. Ladunga, I., Czako, F., Csabai, I., Geszti, T.; Improving signal peptide prediction accuracy by simulated neural network. CABIOS, 7(4) (1991): 485-487. Lapedes, A. et. al. (5 authors); Application of neural networks and other machine learning algorithms to DNA sequence analysis. In Computers and DNA, 157-182. SFI Studies in the Sciences of Complexity, vol VII: Addison-Wesley (1990). Muskal, S., Kim, S-H.; Predicting protein secondary structure content: a tandem neural network approach. J. Mol. Bio. 225, pp. 713-727. Qian, N., Sejnowski, T.; Predicting the Secondary Structure of Globular Proteins Using Neural Network Models. J. Mol. Bio. (1988) 202, pp. 865-884. Uberbacher, E., Mural, R.; Locating protein coding regions in human DNA sequences using a multiple sensor-neural network approach. Proc. Natl. Acad. Sci. USA 88 (1991): 11261-11265. Wade, R., Bohr, H., Wolynes, P.; Prediction of water binding sites on proteins by neural networks. J. of the Amer. Chem. Soc. 114 (1992): 8284-8285. Zhang, X., Mesirov, J., Waltz, D.; A hybrid system for protein secondary structure prediction. J. Mol. Bio. (1992) 225, pp. 1049-1063.