eesnyder@boulder.Colorado.EDU (Eric E. Snyder) (09/26/90)
I am currently trying to develop a NN model to determine a quantitative relationship between nucleotide sequences and functional activities. The data takes the following form: sequence activity ATTTCT 2.3 ACCTCC 3.0 AAATCG 2.5 etc.... I would like to develop a general model which can predict activity given a new sequence. This is my first AI project. I would like to hear about similar work that has already been done, as well as possible approaches to this problem. Thanks! --------------------------------------------------------------------------- TTGATTGCTAAACACTGGGCGGCGAATCAGGGTTGGGATCTGAACAAAGACGGTCAGATTCAGTTCGTACTGCTG Eric E. Snyder Department of MCD Biology Proctoscopy recapitulates University of Colorado, Boulder hagiography. Boulder, Colorado 80309-0347 LeuIleAlaLysHisTrpAlaAlaAsnGlnGlyTrpAspLeuAsnLysAspGlyGlnIleGlnPheValLeuLeu ---------------------------------------------------------------------------
usenet@nlm.nih.gov (usenet news poster) (09/27/90)
In article <26964@boulder.Colorado.EDU> eesnyder@boulder.Colorado.EDU (Eric E. Snyder) writes: >I am currently trying to develop a NN model to determine >a quantitative relationship between nucleotide sequences and >functional activities. > >The data takes the following form: > >sequence activity > >ATTTCT 2.3 >ACCTCC 3.0 >AAATCG 2.5 > >etc.... > >I would like to develop a general model which can predict activity >given a new sequence. What specifically did you have in mind? The use of neural networks in molecular sequence has been explored by a number of workers. A couple recent examples include: Holley and Karplus, PNAS 86:152-6 (1989) title: Protein secondary structure prediction with a neural network. Qian N; Sejnowski TJ, J Mol Biol 202: 865-84 (1988) title: Predicting the secondary structure of globular proteins using neural network models. Lukashin AV; Anshelevich VV; Amirikyan BR; Gragerov AI; Frank-Kamenetskii MD J Biomol Struct Dyn 6: 1123-33 (1989) title: Neural network models for promoter recognition. Bohr H; Bohr J; Brunak S; Cotterill RM; Lautrup B; Norskov L; Olsen OH; Petersen SB, FEBS Lett 241: 223-8 (1988) title: Protein secondary structure and homology by neural networks. The alpha-helices in rhodopsin. Kneller DG; Cohen FE; Langridge R, J Mol Biol 214: 171-82 (1990) title: Improvements in protein secondary structure prediction by an enhanced neural network. >Eric E. Snyder David States