Analysis of transcriptomic data using similarity based modeling

Abstract

An analytic apparatus and method is provided for diagnosis, prognosis and biomarker discovery using transcriptome data such as mRNA expression levels from microarrays, proteomic data, and metabolomic data. The invention provides for model-based analysis, especially using kernel-based models, and more particularly similarity-based models. Model-derived residuals advantageously provide a unique new tool for insights into disease mechanisms. Localization of models provides for improved model efficacy. The invention is capable of extracting useful information heretofore unavailable by other methods, relating to dynamics in cellular gene regulation, regulatory networks, biological pathways and metabolism.

Description

CROSS REFERENCE TO RELATED APPLICATIONS [0001] This application claims the benefit of priority under 35 U.S.C. § 119(e) to U.S. Provisional application Ser. No. 60 / 670,950 filed Apr. 13, 2005.FIELD OF THE INVENTION [0002] The invention relates to biomarker discovery and disease diagnosis and prognosis, especially based on transcriptomic data such as gene expression levels and proteomics. BACKGROUND OF THE INVENTION [0003] Recent advances in the biological sciences have made it possible to measure thousands of gene expression levels in cells using microarrays, and to quantify the cellular content of thousands of types of proteins using e.g., mass spectrometry, in single experiments with one sample of tissue or serum. Gene expression molecules, i.e., messenger RNA, and the proteins encoded by the mRNA comprise the “transcriptome” of the cell, the components of the cell that are the direct products of the transcription (the genome) and translation (the proteome) of DNA. Analysis of the...

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Benefits of technology

[0012] In a first embodiment of the invention, a model is trained with multivariate transcriptome profiles representative of normal health, and the model is then used to detect deviations in transcriptome patterns and dynamics representative of a diseased state, which deviations point to underlying disease mechanisms. An autoassociative model of gene expression data for normal tissue is trained from normal tissue data. A new input vector representing expression level data from diseased tissue is modeled with this model to provide an autoassociative estimate of the expression levels, which is then differenced with the actual measurements to provide residuals. A residual threshold test detects gene expression levels that are abnormal, identifying these genes as potential markers. Furthermore, the residual pattern can be pattern matched against stored patterns for known disease types to classify the input vector vis-à-vis disease.

[0013] In a second embodiment of the invention, a diagnostic classification is made for a dynamic multivariate genomic or proteomic profile. An inferential model is trained to recognize normal and diseased state transcriptome profiles, and new samples are analyzed and classified accordingly.

[0014] The classification capability of the invention can advantageously be extended t

Problems solved by technology

Disease most often occurs when these delicately

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