title: DNA fragment assembly using neural prediction techniques.
abstract: The paper describes an alternative approach to the fragment assembly problem. The key idea is to train a recurrent neural network to tracking the sequence of bases constituting a given fragment and to assign to a same cluster all the sequences which are well tracked by this network. We make use of a 3-layer Recurrent Perceptron and examine both edited sequences from a ftp site and artificial fragments from a common simulation software: the clusters we obtain exhibit interesting properties in terms of error filtering, stability and self consistency; we define as well, with a certain degree of approximation, a metric on the fragment set. The proposed assembly algorithm is susceptible to becoming an alternative method with the following properties: (i) high quality of the rebuilt genomic sequences, (ii) high parallelizability of the computing process with consequent drastic reduction of the running time.
authors.affiliation: Dipartimento di Scienze dell'Informazione, University of Milan, Italy. angeleri@dsi.unimi.it
authors.forename: E
authors.initials: E
authors.lastname: Angeleri
authors.forename: B
authors.initials: B
authors.lastname: Apolloni
authors.forename: D
authors.initials: D
authors.lastname: de Falco
authors.forename: L
authors.initials: L
authors.lastname: Grandi
mesh_terms: D053938:DNA Fragmentation; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D017422:Sequence Analysis, DNA