| Paper: | ITT-P2.2 | ||
| Session: | Emerging DSP Applications | ||
| Time: | Thursday, May 18, 10:00 - 12:00 | ||
| Presentation: | Poster | ||
| Topic: | Industry Technology Track: Biomedical | ||
| Title: | A New Specification to Gene Signals Sensors by Neural Self Organizing Feature Map (SOFM) | ||
| Authors: | Mariusz Zoltowski, Collegium Medicum of Nicolaus Copernicus University of Torun, Poland | ||
| Abstract: | Two different paradigms by the goals in gene-finding research have been recognized: 1) to offer computational aid in the annotation of the large volume of genomic data and 2) to provide a computational model helpful in elucidating the mechanisms involved in transcription, splicing, polyadynalation and other important processes on the pathway from genome to proteome [1]. New findings in gene regulation appear to focus a new interest in the latter paradigm approaches [1, 2, 3 and 4]. Therefore, a single weight matrix for the genomic patterns consensus scoring [5, 6] can be substituted by SOFM clusters matrices. This should result in the detection improvement of gene functional sites or signals, and therefore a gain evaluation across the known Burset’s and Guigó’s collection of the genes of 570 vertebrates is provided by a percentile measure on an exemplary site detection statistics. Such an improvement is important in both the “extrinsic” and “intrinsic” approaches [14]. In the “signal” case of the latter [14], a demand is addressed for a neural approach which translates into likelihood scoring. This includes, but is not limited to, applications with HMM (Hidden Markov Model) derived gene finders [7]. The approach is also scalable into a clusters-based solution to genes recognition with the capability of integrating DNA-string-contained knowledge in a novel way. | ||
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