Biomedical subjects

Romdhane Rekaya

Publications and source records attributed to Romdhane Rekaya.

6 recordsLinked to original sources

PubMed2005

A method for predicting disease subtypes in presence of misclassification among training samples using gene expression: application to human breast cancer.

MOTIVATION: An accurate diagnostic and prediction will not be achieved unless the disease subtype status for every training sample used in the supervised learning step is accurately known. Such an assumption requires the existence of a perfect tool for disease diagnostic and classification, which is seldom available in the majority of the cases. Thus, the supervised learning step has to be conducted with a statistical model that contemplates and handles potential mislabeling in the input data. RESULTS: A procedure for handling potential mislabeling among training samples in the prediction of disease subtypes using gene expression data was proposed. A real data-based simulation study about the estrogen receptor status (ER+/ER-) of breast cancer patients was conducted. The results demonstrated that when 1-4 training samples (N = 30) were artificially mislabeled, the proposed method was able not only in correcting the ER status of mislabeled training samples but also more importantly in predicting the ER status of validation samples as well as using 'true' training data.

Algorithms↗

PubMed2004

Comparative transcriptional profiling of two human embryonic stem cell lines.

Human embryonic stem cells (ESCs) have generated enormous interest due to their ability to self-renew and produce many different cell types. In conjunction with microarray technology, human ESCs provide a powerful tool for employing a systems-based approach to deciphering the molecular mechanisms that control pluripotency and early development. Recent work has focused on defining "stemness" and pluripotency based on different experimental and analytical approaches in both mouse and human ESCs. Using a mixed linear model statistical approach, we report a stringent direct comparison between data sets obtained from two human ESCs (BG01 and H1) in order to obtain a list of genes that are enriched in ESCs. In addition, we used another pluripotent population derived from BG01 ESCs to obtain a list of genes that we consider important to the maintenance of pluripotency. A total of 133 genes overlapped between the three pluripotent populations. A majority of the 133 genes were classified under the key functional categories of cell-cycle regulation, signaling, and regulation of transcription. Key genes expressed were Oct4, Sox2, LeftyA, and Fgf2. Also found to be enriched in all three populations is FLJ10713, a gene encoding a hypothetical protein of unknown function that has been shown in earlier studies to possess a homolog in mouse ESCs and also to cluster tightly with Oct4 in human ESCs. Although there were many genes unique to each pluripotent population, they shared similarities based on functional ontologies that define pluripotency. The significance of our studies underscores the need for direct comparison of stem cell populations that share biological similarities using uniform stringent analytical approaches, in order to better define pluripotency. Our findings have important implications for the maintenance of pluripotency and in developing directed differentiation strategies for various regenerative applications.

Algorithms↗

PubMed2004

Transcriptional profiling of initial differentiation events in human embryonic stem cells.

Currently, there are no differentiation strategies for human embryonic stem cells (hESCs) that efficiently produce one specific cell type, possibly because of lack of understanding of the genes that control signaling events prior to overt differentiation. sed HepG2 cell conditioned medium (MEDII), which induces early differentiation in mouse ES cells while retaining pluripotent markers, to query gene expression in hESCs. Treatment of adherent hESCs with 50% MEDII medium effected differentiation to a cell type with gene expression similar to primitive streak stage cells of mouse embryos. MEDII treatment up-regulates TDGF1 (Cripto), a gene essential for anterior-posterior axis and mesoderm formation in mouse embryos and a key component of the TGFB1/NODAL signaling pathway. LEFTYA, an antagonist of NODAL/TDGF1 signaling expressed in anterior visceral endoderm, is down-regulated with MEDII treatment, as is FST, an inhibitor of mesoderm induction via the related INHBE1 pathway. In summary, the TGFB1/NODAL pathway is important for primitive-streak and mesoderm formation and in using MEDII, we present a means for generating an in vitro cell population that maintains pluripotent gene expression (POU5F1, NANOG) and SSEA-4 markers while regulating genes in the TGFB1/NODAL pathway, which may lead to more uniform formation of mesoderm in vitro.

Carcinoma, Hepatocellular↗

PubMed2002

National evaluation for calving ease, gestation length and birth weight by linear and threshold model methodologies.

Data included 393,097 calving ease, 129,520 gestation length, and 412,484 birth weight records on 412,484 Gelbvieh cattle. Additionally, pedigrees were available on 72,123 animals. Included in the models were effects of sex and age of dam, treated as fixed, as well as direct, maternal genetic and permanent environmental effects and effects of contemporary group (herd-year-season), treated as random. In all analyses, birth weight and gestation length were treated as continuous traits. Calving ease (CE) was treated either as a continuous trait in a mixed linear model (LM), or as a categorical trait in linear-threshold models (LTM). Solutions in TM obtained by empirical Bayes (TMEB) and Monte Carlo (TMMC) methodologies were compared with those by LM. Due to the computational cost, only 10,000 samples were obtained for TMMC. For calving ease, correlations between LM and TMEB were 0.86 and 0.78 for direct and maternal genetic effects, respectively. The same correlations but between TMEB and TMMC were 1.00 and 0.98, respectively. The correlations between LM and TMMC were 0.85 and 0.75, respectively. The correlations for the linear traits were above.97 between LM and TMEB but as low as 0.91 between LM and TMMC, suggesting insufficient convergence of TMMC. Computing time required was about 2 hrs, 5 hrs, and 6 days for LM, TMEB and TMMC, respectively, and memory requirements were 169, 171, and 445 megabytes, respectively. Bayesian implementation of threshold model is simple, can be extended to multiple categorical traits, and allows easy calculation of accuracies; however, computing time is prohibitively long for large models.

Animals↗

PubMedDate not supplied

Longitudinal random effects models for genetic analysis of binary data with application to mastitis in dairy cattle.

A Bayesian analysis of longitudinal mastitis records obtained in the course of lactation was undertaken. Data were 3341 test-day binary records from 329 first lactation Holstein cows scored for mastitis at 14 and 30 days of lactation and every 30 days thereafter. First, the conditional probability of a sequence for a given cow was the product of the probabilities at each test-day. The probability of infection at time t for a cow was a normal integral, with its argument being a function of "fixed" and "random" effects and of time. Models for the latent normal variable included effects of: (1) year-month of test + a five-parameter linear regression function ("fixed", within age-season of calving) + genetic value of the cow + environmental effect peculiar to all records of the same cow + residual. (2) As in (1), but with five parameter random genetic regressions for each cow. (3) A hierarchical structure, where each of three parameters of the regression function for each cow followed a mixed effects linear model. Model 1 posterior mean of heritability was 0.05. Model 2 heritabilities were: 0.27, 0.05, 0.03 and 0.07 at days 14, 60, 120 and 305, respectively. Model 3 heritabilities were 0.57, 0.16, 0.06 and 0.18 at days 14, 60, 120 and 305, respectively. Bayes factors were: 0.011 (Model 1/Model 2), 0.017 (Model 1/Model 3) and 1.535 (Model 2/Model 3). The probability of mastitis for an "average" cow, using Model 2, was: 0.06, 0.05, 0.06 and 0.07 at days 14, 60, 120 and 305, respectively. Relaxing the conditional independence assumption via an autoregressive process (Model 2) improved the results slightly.

Animals↗

PubMedDate not supplied

A simulation study for the analysis of uncertain binary responses: application to first insemination success in beef cattle.

A simulation was carried out to investigate the methods of analyzing uncertain binary responses for success or failure at first insemination. A linear mixed model that included, herd, year, and month of mating as fixed effects; and unrelated service sire, sire and residual as random effects was used to generate binary data. Binary responses were assigned using the difference between days to calving and average gestation length. Females deviating from average gestation length lead to uncertain binary responses. Thus, the methods investigated were the following: (1) a threshold model fitted to certain (no uncertainty) binary data (M1); (2) a threshold model fitted to uncertain binary data ignoring uncertainty (M2); and (3) analysis of uncertain binary data, accounting for uncertainty from day 16 to 26 (M3) or from day 14 to 28 (M4) after introduction of the bull, using a threshold model with fuzzy logic classification. There was virtually no difference between point estimates obtained from M1, M3, and M4 with true values. When uncertain binary data were analyzed ignoring uncertainty (M2), sire variance and heritability were underestimated by 22 and 24%, respectively. Thus, for noisy binary data, a threshold model contemplating uncertainty is needed to avoid bias when estimating genetic parameters.

Analysis of Variance↗