Machine learning methods for predictive proteomics.
The search for predictive biomarkers of disease from high-throughput mass spectrometry (MS) data requires a complex analysis path. Preprocessing and machine-learning modules are pipelined, starting from raw spectra, to set up a predictive classifier based on a shortlist of candidate features. As a machine-learning problem, proteomic profiling on MS data needs caution like the microarray case. The risk of overfitting and of selection bias effects is pervasive: not only potential features easily outnumber samples by 10(3) times, but it is easy to neglect information-leakage effects during preprocessing from spectra to peaks. The aim of this review is to explain how to build a general purpose design analysis protocol (DAP) for predictive proteomic profiling: we show how to limit leakage due to parameter tuning and how to organize classification and ranking on large numbers of replicate versions of the original data to avoid selection bias. The DAP can be used with alternative components, i.e. with different preprocessing methods (peak clustering or wavelet based), classifiers e.g. Support Vector Machine (SVM) or feature ranking methods (recursive feature elimination or I-Relief). A procedure for assessing stability and predictive value of the resulting biomarkers' list is also provided. The approach is exemplified with experiments on synthetic datasets (from the Cromwell MS simulator) and with publicly available datasets from cancer studies.
Barla
Annalisa
A
FBK, via Sommarive 18, I-38100 Povo (Trento), Italy.
Jurman
Giuseppe
G
Riccadonna
Samantha
S
Merler
Stefano
S
Chierici
Marco
M
Furlanello
Cesare
C
D000465:Algorithms; D000818:Animals; D019540:Area Under Curve; D001185:Artificial Intelligence; D015415:Biomarkers / Q000032:analysis*; D020869:Gene Expression Profiling; D006801:Humans; D013058:Mass Spectrometry / Q000295:instrumentation / Q000379:methods; D046228:Microarray Analysis; D010363:Pattern Recognition, Automated / Q000379:methods; D040901:Proteomics / Q000295:instrumentation / Q000379:methods; D012815:Signal Processing, Computer-Assisted
D015415:Biomarkers