- author = "Viktoria Dorfer and Sergey Maltsev and Stephan Dreiseitl and Karl Mechtler and Stephan M. Winkler",
- title = "A Symbolic Regression Based Scoring System Improving Peptide Identifications for MS Amanda",
- booktitle = "GECCO 2015 Medical Applications of Genetic and Evolutionary Computation (MedGEC'15) Workshop",
- year = "2015",
- editor = "Stephen L. Smith and Stefano Cagnoni and Robert M. Patton",
- isbn13 = "978-1-4503-3488-4",
- keywords = "genetic algorithms, genetic programming",
- pages = "1335--1341",
- month = "11-15 " # jul,
- organisation = "SIGEVO",
- address = "Madrid, Spain",
-
URL = "
http://doi.acm.org/10.1145/2739482.2768509",
-
DOI = "
doi:10.1145/2739482.2768509",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "Peptide search engines are algorithms that are able to
identify peptides (i.e., short proteins or parts of
proteins) from mass spectra of biological samples.
These identification algorithms report the best
matching peptide for a given spectrum and a score that
represents the quality of the match; usually, the
higher this score, the higher is the reliability of the
respective match. In order to estimate the specificity
and sensitivity of search engines, sets of target
sequences are given to the identification algorithm as
well as so-called decoy sequences that are randomly
created or scrambled versions of real sequences; decoy
sequences should be assigned low scores whereas target
sequences should be assigned high scores.
In this paper we present an approach based on symbolic regression (using genetic programming) that helps to distinguish between target and decoy matches. On the basis of features calculated for matched sequences and using the information on the original sequence set (target or decoy) we learn mathematical models that calculate updated scores. As an alternative to this white box modelling approach we also use a black box modelling method, namely random forests.
As we show in the empirical section of this paper, this approach leads to scores that increase the number of reliably identified samples that are originally scored using the MS Amanda identification algorithm for high resolution as well as for low resolution mass spectra.",
- notes = "Also known as \cite{2768509} Distributed at GECCO-2015.",
