Cross-validation pitfalls when selecting and assessing regression and classification models.

Background: We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe and evaluate best practices which improve reliability and increase confidence in selected models.

A gene expression profile test to resolve head & neck squamous versus lung squamous cancers.

Background: The differential diagnosis between metastatic head & neck squamous cell carcinomas (HNSCC) and lung squamous cell carcinomas (lung SCC) is often unresolved because the histologic appearance of these two tumor types is similar. We have developed and validated a gene expression profile test (GEP-HN-LS) that distinguishes HNSCC and lung SCC in formalin-fixed, paraffin-embedded (FFPE) specimens using a 2160-gene classification model.

Methods: The test was validated in a blinded study using a pre-specified algorithm and microarray data files for 76 metastatic or poorly-differentiated primary tumors with a known HNSCC or lung SCC diagnosis.

A gene expression profile test for the differential diagnosis of ovarian versus endometrial cancers.

We have developed a gene expression profile test (Pathwork Tissue of Origin Endometrial Test) that distinguishes primary epithelial ovarian and endometrial cancers in formalin-fixed, paraffin-embedded (FFPE) specimens using a 316-gene classification model. The test was validated in a blinded study using a pre-specified algorithm and microarray files for 75 metastatic, poorly differentiated or undifferentiated specimens with a known ovarian or endometrial cancer diagnosis. Measures of test performance include a 94.

Multicenter validation of a 1,550-gene expression profile for identification of tumor tissue of origin.

Purpose: Malignancies found in unexpected locations or with poorly differentiated morphologies can pose a significant challenge for tissue of origin determination. Current histologic and imaging techniques fail to yield definitive identification of the tissue of origin in a significant number of cases. The aim of this study was to validate a predefined 1,550-gene expression profile for this purpose.

Interlaboratory performance of a microarray-based gene expression test to determine tissue of origin in poorly differentiated and undifferentiated cancers.

Clinical workup of metastatic malignancies of unknown origin is often arduous and expensive and is reported to be unsuccessful in 30 to 60% of cases. Accurate classification of uncertain primary cancers may improve with microarray-based gene expression testing. We evaluated the analytical performance characteristics of the Pathwork tissue of origin test, which uses expression signals from 1668 probe sets in a gene expression microarray, to quantify the similarity of tumor specimens to 15 known tissues of origin.

PCP: a program for supervised classification of gene expression profiles.

Unlabelled: PCP (Pattern Classification Program) is an open-source machine learning program for supervised classification of patterns (vectors of measurements). The principal use of PCP in bioinformatics is design and evaluation of classifiers for use in clinical diagnostic tests based on measurements of gene expression. PCP implements leading pattern classification and gene selection algorithms and incorporates cross-validation estimation of classifier performance.