title: Kalman filtering for disease-state estimation from microarray data.
abstract: BACKGROUND
  In this paper, we propose using the Kalman filter (KF) as a pre-processing step in microarray-based molecular diagnosis. Incorporating the expression covariance between genes is important in such classification problems, since this represents the functional relationships that govern tissue state. Failing to fulfil such requirements may result in biologically implausible class prediction models. Here, we show that employing the KF to remove noise (while retaining meaningful covariance and thus being able to estimate the underlying biological state from microarray measurements) yields linearly separable data suitable for most classification algorithms.
  
  
  RESULTS
  We demonstrate the utility and performance of the KF as a robust disease-state estimator on publicly available binary and multi-class microarray datasets in combination with the most widely used classification methods to date. Moreover, using popular graphical representation schemes we show that our filtered datasets also have an improved visualization capability.
authors.affiliation: Laboratory of Functional Genomics, Biological Research Centre, Hungarian Academy of Sciences, Szeged Temesvári krt. 62, H-6726, Hungary. kelli@nucleus.szbk.u-szeged.hu
authors.forename: János Z
authors.initials: JZ
authors.lastname: Kelemen
authors.forename: Attila
authors.initials: A
authors.lastname: Kertész-Farkas
authors.forename: András
authors.initials: A
authors.lastname: Kocsor
authors.forename: László G
authors.initials: LG
authors.lastname: Puskás
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis*; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013598:Systems Theory
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins