Guilt-by-association feature selection: identifying biomarkers from proteomic profiles.
In recent years, proteomic profiling by mass spectrometry has opened up a new realm of methods for identifying potential biomarkers. Mass spectrometry data, like other proteomic and genomic data, are challenging to analyze because of their high dimensionality and the availability of few samples. Hence, feature selection is extremely important because it directly provides a list of potential biomarkers by choosing a subset of effective features that separate diseased samples from healthy ones. The rule of thumb for feature selection is that features must be discriminant and independent for the best separation of the two groups. However, in general, existing feature selection algorithms only take into account the discrimination ability of features. In this paper, we present a novel method for feature selection, guilt-by-association feature selection (GBA-FS). The algorithm makes it possible to select features that are independent as well as discriminant. After measuring similarities between features, the algorithm groups together similar features using a clustering algorithm, and selects the best representative feature from each group. As a result, it produces a list of discriminant and independent features. The efficacy of GBA-FS was extensively tested on two real-world SELDI TOF data sets. The experimental results demonstrate that GBA-FS assists in selecting more independent features as compared to a common filter type feature selection method, the t test. The results also show that GBA-FS can be used to deconvolve multiply charged states of the same protein molecules. As GBA-FS successfully identifies feature groups with similar mass values, it can also be employed as an alternative to peak detection for preprocessing the mass spectrometry data.
Shin
Hyunjin
H
Department of Electrical and Computer Engineering, The University of Texas at Austin, USA.
Sheu
Bryan
B
Joseph
Maria
M
Markey
Mia K
MK
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D015415:Biomarkers / Q000032:analysis*; D020869:Gene Expression Profiling / Q000379:methods*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D010449:Peptide Mapping / Q000379:methods*; D020543:Proteome / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
D015415:Biomarkers; D020543:Proteome