Adaptive learning of immunosignaturing features for multi-disease pathologies

Previously, adaptive learning algorithms have been used with immunosignaturing in order to identify disease states in patients. However, in these algorithms the presence of a single disease state is assumed, although in a clinical setting this may not be the case. We propose a novel algorithm based on latent feature identification using beta process factor analysis, in which the binary feature sharing matrix is modified and key comparisons are applied to identify multiple possible underlying disease states. The algorithm is verified using combinations of actual patient immunosignaturing data. The proposed method has a variety of applications, including multi-disease state diagnosis in the clinical setting, multi-biothreat detection in the field, and separation of co-contaminated biological samples.

[1]  Lawrence Carin,et al.  Nonparametric factor analysis with beta process priors , 2009, ICML '09.

[2]  S. Takada,et al.  Dynamic energy landscape view of coupled binding and protein conformational change: Induced-fit versus population-shift mechanisms , 2008, Proceedings of the National Academy of Sciences.

[3]  P. Brown,et al.  Exploring the metabolic and genetic control of gene expression on a genomic scale. , 1997, Science.

[4]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[5]  Kenji Usui,et al.  A designed peptide chip: protein fingerprinting technology with a dry peptide array and statistical data mining. , 2009, Methods in molecular biology.

[6]  Michael I. Jordan,et al.  Bayesian Nonparametric Latent Feature Models , 2011 .

[7]  Michael I. Jordan,et al.  Joint Modeling of Multiple Related Time Series via the Beta Process , 2011, 1111.4226.

[8]  Michael I. Jordan,et al.  Hierarchical Beta Processes and the Indian Buffet Process , 2007, AISTATS.

[9]  Lawrence Carin,et al.  A Stick-Breaking Construction of the Beta Process , 2010, ICML.

[10]  T. Griffiths,et al.  Bayesian nonparametric latent feature models , 2007 .

[11]  Pierre R. Bushel,et al.  Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models , 2001, J. Comput. Biol..

[12]  Jun Jason Zhang,et al.  Beta process based adaptive learning for immunosignature microarray feature identification , 2012, 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[13]  G. Church,et al.  Systematic determination of genetic network architecture , 1999, Nature Genetics.

[14]  Jun Jason Zhang,et al.  Adaptive learning of immunosignaturing peptide array features for biothreat detection and classification , 2011, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[15]  Phillip Stafford,et al.  A general method for characterization of humoral immunity induced by a vaccine or infection. , 2010, Vaccine.

[16]  Phillip Stafford,et al.  Immunosignaturing microarrays distinguish antibody profiles of related pancreatic diseases , 2013 .

[17]  Phillip Stafford,et al.  Comparative study of classification algorithms for immunosignaturing data , 2012, BMC Bioinformatics.

[18]  Oded Maimon,et al.  Evaluation of gene-expression clustering via mutual information distance measure , 2007, BMC Bioinformatics.

[19]  John R. Crowther,et al.  The ELISA Guidebook , 2000, Methods in Molecular Biology™.

[20]  Phillip Stafford,et al.  Application of immunosignatures to the assessment of Alzheimer's disease , 2011, Annals of neurology.

[21]  Joseph Barten Legutki,et al.  Evaluation of Biological Sample Preparation for Immunosignature-Based Diagnostics , 2012, Clinical and Vaccine Immunology.

[22]  Rebecca F. Halperin,et al.  Physical Characterization of the “Immunosignaturing Effect” , 2012, Molecular & Cellular Proteomics.

[23]  Lancelot F. James,et al.  Gibbs Sampling Methods for Stick-Breaking Priors , 2001 .

[24]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

[25]  N. Hjort.  Nonparametric Bayes Estimators Based on Beta Processes in Models for Life History Data , 1990 .