CINdex: A Bioconductor Package for Analysis of Chromosome Instability in DNA Copy Number Data.

The CINdex Bioconductor package addresses an important area of high-throughput genomic analysis. It calculates the chromosome instability (CIN) index, a novel measurement that quantitatively characterizes genome-wide copy number alterations (CNAs) as a measure of CIN. The advantage of this package is an ability to compare CIN index values between several groups for patients (case and control groups), which is a typical use case in translational research.

DNA copy numbers profiles in affinity-purified ovarian clear cell carcinoma.

Purpose: Advanced ovarian clear cell carcinoma (CCC) is one of the most aggressive ovarian malignancies, in part because it tends to be resistant to platinum-based chemotherapy. At present, little is known about the molecular genetic alterations in CCCs except that there are frequent activating mutations in PIK3CA. The purpose of this study is to comprehensively define the genomic changes in CCC based on DNA copy number alterations.

Analysis of DNA copy number alterations in ovarian serous tumors identifies new molecular genetic changes in low-grade and high-grade carcinomas.

Ovarian serous carcinoma, the most common and lethal type of ovarian cancer, is thought to develop from two distinct molecular pathways. High-grade (HG) serous carcinomas contain frequent TP53 mutations, whereas low-grade (LG) carcinomas arise from serous borderline tumors (SBT) and harbor mutations in KRAS/BRAF/ERBB2 pathway. However, the molecular alterations involved in the progression from SBT to LG carcinoma remain unknown.

Genetic and clinical heterogeneity in eIF2B-related disorder.

Eukaryotic initiation factor 2B (eIF2B)-related disorders are heritable white matter disorders with a variable clinical phenotype (including vanishing white matter disease and ovarioleukodystrophy) and an equally heterogeneous genotype. We report 9 novel mutations in the EIF2B genes in our subject population, increasing the number of known mutations to more than 120. Using homology modeling, we have analyzed the impact of novel mutations on the 5 subunits of the eIF2B protein.

Phenotypic-specific gene module discovery using a diagnostic tree and caBIG VISDA.

For the critical task of gene module discovery in genomic research, we present a model-based hierarchical data clustering and visualization algorithm, visual statistical data analyzer (VISDA), which effectively exploits human-data interaction to improve the clustering outcome. Guided by a diagnostic tree, we apply VISDA to a muscular dystrophy dataset that contains a number of different phenotypic conditions. We then superimpose existing knowledge of gene regulation and gene function (ingenuity pathway analysis) to analyze the clustering results and generate novel hypotheses for further research on muscular dystrophies.

Gene selection for multiclass prediction by weighted Fisher criterion.

Gene expression profiling has been widely used to study molecular signatures of many diseases and to develop molecular diagnostics for disease prediction. Gene selection, as an important step for improved diagnostics, screens tens of thousands of genes and identifies a small subset that discriminates between disease types. A two-step gene selection method is proposed to identify informative gene subsets for accurate classification of multiclass phenotypes.

Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data.

Motivation: Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-fitting, and may not provide acceptable prediction accuracy.

Results: Based on Fisher linear discriminant analysis, we designed and implemented an MLP optimization scheme for a two-layer MLP that effectively optimizes the initialization of MLP parameters and MLP architecture.