Bioinformatics

A mixture normal model has been developed to partition genotypes in predicting quantitative phenotypes. Its estimation and inference are performed through an EM algorithm. This approach can conduct simultaneous genotype clustering and hypothesis testing. It is a valuable method for predicting the distribution of quantitative phenotypes among multi-locus genotypes across...

Background: Genetic and epigenetic alterations in colorectal cancer are numerous. However, it is difficult to judge whether such changes are primary or secondary to the appearance and progression of tumors. Therefore, the aim of the present study was to identify altered DNA regions with significant covariation to transcription alterations...

An efficient computing procedure for estimating the age-specific hazard functions by the log-linear age-period-cohort (LLAPC) model is proposed. This procedure accounts for the influence of time period and birth cohort effects on the distribution of age-specific cancer incidence rates and estimates the hazard function for populations with different exposures to...

Conjugated linoleic acids (CLA) are of important nutritional and health benefit to human. Food products of animal origin are their major dietary source and their concentration increases with high concentrate diets fed to animals. To examine the effects of diet supplementation on the expression of genes related to lipid...

Cancer is a disease associated with the deregulation of multiple gene networks. Microarray data has permitted researchers to identify gene panel markers for diagnosis or prognosis of cancer but these are not sufficient to make specific mechanistic assertions about phenotype switches. We propose a strategy to identify putative mechanisms of...

When confronted with a small sample, feature-selection algorithms often fail to find good feature sets, a problem exacerbated for high-dimensional data and large feature sets. The problem is compounded by the fact that, if one obtains a feature set with a low error estimate, the estimate is unreliable because training-data-based...

When confronted with a small sample, feature-selection algorithms often fail to find good feature sets, a problem exacerbated for high-dimensional data and large feature sets. The problem is compounded by the fact that, if one obtains a feature set with a low error estimate, the estimate is unreliable because training-data-based...

Multi-class cancer classification based on microarray data is described. A generalized output-coding scheme based on One Versus One (OVO) combined with Latent Variable Model (LVM) is used. Results from the proposed One Versus One (OVO) output- coding strategy is compared with the results obtained from the generalized One Versus All...

Motivation: Whole genome microarrays are increasingly becoming the method of choice to study responses in model organisms to disease, stressors or other stimuli. However, whole genome sequences are available for only some model organisms, and there are still many species whose genome sequences are not yet available. Cross-species studies, where...

The recently published article by Wang, and commented on by Ertel in this journal, describes development of a method, named the Bimodality Index, to objectively identify and rank meaningful and reliable bimodal patterns from large-scale gene expression datasets. This is an important step because genes with a bimodal distribution may...

The discovery of effective cancer treatments is a key goal for pharmaceutical companies. However, the current costs of bringing a cancer drug to the market in the USA is now estimated at $1 billion per FDA approved drug, with many months of research at the bench and costly clinical trials....

Many proteins contain a large number of NXS/T sequences (where X is any amino acid except proline) which are the potential sites of asparagine (N) linked glycosylation. However, the patterns of occurrence of these N-glycosylation sequons in related proteins or groups of proteins and their underlying causes have largely been...

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our...

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our...

With insights gained through molecular profiling, cancer is recognized as a heterogeneous disease with distinct subtypes and outcomes that can be predicted by a limited number of biomarkers. Statistical methods such as supervised classification and machine learning identify distinguishing features associated with disease subtype but are not necessarily clear or...

A pseudo-testcross pedigree is widely used for mapping quantitative trait loci (QTL) in outcrossing species, but the model for analyzing pseudo-testcross data borrowed from the inbred backcross design can only detect those QTLs that are heterozygous only in one parent. In this study, an intercross model that incorporates the high...

Automated disease surveillance systems are becoming widely used by the public health community. However, communication among non-collocated and widely dispersed users still needs improvement. A web-based software tool for enhancing user communications was completely integrated into an existing automated disease surveillance system and was tested during two simulated exercises and...

Background: Gene set enrichment analysis (GSEA) is an analytic approach which simultaneously reduces the dimensionality of microarray data and enables ready inference of the biological meaning of observed gene expression patterns. Here we invert the GSEA process to identify class-specific gene signatures. Because our approach uses the Kolmogorov-Smirnov approach...

Background: The accurate prognosis for patients with resectable pancreatic adenocarcinomas requires the incorporation of more factors than those included in AJCC TNM system. Methods: We identified 218 patients diagnosed with stage I and II pancreatic adenocarcinoma at NewYork-Presbyterian Hospital/ Columbia University Medical Center (1999 to 2009). Tumor and clinical...

Bacterial autotransporters represent a diverse family of proteins that autonomously translocate across the inner membrane of Gram-negative bacteria via the Sec complex and across the outer bacterial membrane. They often possess exceptionally long N-terminal signal sequences. We analyzed 90 long signal sequences of bacterial autotransporters and members of the two-partner...

A simple, computationally efficient procedure for analyses of the time period and birth cohort effects on the distribution of the age-specific incidence rates of cancers is proposed. Assuming that cohort effects for neighboring cohorts are almost equal and using the Log-Linear Age-Period-Cohort Model, this procedure allows one to evaluate temporal...

Melanoma antigen family (MAGE) genes are widely expressed in various tumor types but silent in normal cells except germ-line cells lacking human leukocyte antigen (HLA) expression. Over 25 MAGE genes have been identified in different tissues, mostly located in Xq28 of human chromosome and some of them in chromosome 3...

Melanoma antigen family (MAGE) genes are widely expressed in various tumor types but silent in normal cells except germ-line cells lacking human leukocyte antigen (HLA) expression. Over 25 MAGE genes have been identified in different tissues, mostly located in Xq28 of human chromosome and some of them in chromosome 3...

A signal transduction pathway (STP) is a cascade composed of a series of signal transferring steps, which often activate one or more transcription factors (TFs) to control the transcription of target genes. Understanding signaling pathways is important to our understanding of the molecular mechanisms of disease. Many condition-annotated pathways have...

This study aimed at discriminating carcinogens on the basis of hepatic transcript profiling in the rats administrated with a variety of carcinogens and non-carcinogens. We conducted 28-day toxicity tests in male F344 rats with 47 carcinogens and 26 non- carcinogens, and then investigated periodically the hepatic gene expression profiles using...

The high sequence divergence within the small subunit ribosomal RNA gene (SSU rDNA) of foraminifera makes it difficult to establish the homology of individual nucleotides across taxa. Alignment-based approaches so far relied on time-consuming manual alignments and discarded up to 50% of the sequenced nucleotides prior to phylogenetic inference. Here,...

The high sequence divergence within the small subunit ribosomal RNA gene (SSU rDNA) of foraminifera makes it difficult to establish the homology of individual nucleotides across taxa. Alignment-based approaches so far relied on time-consuming manual alignments and discarded up to 50% of the sequenced nucleotides prior to phylogenetic inference. Here,...

In this report, we compared the success rate of classification of coding sequences (CDS) vs. introns by Codon Structure Factor (CSF) and by a method that we called Universal Feature Method (UFM). UFM is based on the scoring of purine bias (Rrr) and stop codon frequency. We show that the...

The tremendous rate of accumulation of experimental and clinical knowledge pertaining to cancer dictates the development of a theoretical framework for the meaningful integration of such knowledge at all levels of biocomplexity. In this context our research group has developed and partly validated a number of spatiotemporal simulation models of...

How to combine heterogeneous data sources for reliable prediction of transcriptional regulation is a challenge. Here we present an easy but powerful method to integrate Chromatin immunoprecipitation (ChIP)-chip and knock-out data. Since these two types of data provide complementary (physical and functional) information about transcription, the method combining them is...