Biomedical subjects

Chindo Hicks

Publications and source records attributed to Chindo Hicks.

4 recordsLinked to original sources

PubMed2006

Utilization of lymphoblastoid cell lines as a system for the molecular modeling of autism.

In order to provide an alternative approach for understanding the biology and genetics of autism, we performed statistical analysis of gene expression profiles of lymphoblastoid cell lines derived from children with autism and their families. The goal was to assess the feasibility of using this model in identifying autism-associated genes. Replicate microarray experiments demonstrated that expression data from the cell lines were consistent and highly reproducible. Further analyses identified differentially expressed genes between cell lines derived from children with autism and those derived from their normally developing siblings. These genes were then used to identify biochemical pathways potentially involved in autism. This study suggests that lymphoblastoid cell lines may be a viable tool for identifying genes associated with autism.

Autistic Disorder↗

PubMed2006

Gene expression data analysis in subtypes of ovarian cancer using covariance analysis.

Many studies have used microarray technology to identify the molecular signatures of human cancer, yet the critical features of these often unmanageably large set of signatures remain elusive. We have investigated co-expression pattern in four subtypes of ovarian cancer from 104 cancer patients using covariance analysis, treating each subtype of ovarian cancer as a distinct disease entity. We sought gene pairs that were transcriptionally co-expressed in one or multiple subtypes of ovarian cancer, establishing a high confidence network of 87 genes interconnected by significantly high co-expression links that were observed in at least two subtypes of ovarian cancer. We have shown that certain groups of co-expressed gene pairs are cancer subtype specific, through demonstrating significant differences in co-expression patterns of gene pairs between subtypes of ovarian cancer. In addition, we identified a set of 24 genes that classified patients into specific cancer subtypes with a misclassification error rate of less than 5%. Our findings illustrate how large public microarray gene expression datasets could be exploited for identification of cancer subtype specific molecular signatures, and how to classify cancer patients into specific subtypes of cancer using gene expression profiles.

Biomarkers, Tumor↗

PubMed2005

Structural genomics analysis of alternative splicing and application to isoform structure modeling.

Alternative splicing is a sophisticated nuclear process that regulates gene expression. It represents an important mechanism for enhancing the functional diversity of proteins. Our current knowledge of alternatively spliced variants is derived mainly from mRNA transcripts, and very little is known about their protein tertiary structures. We carried out a large-scale analysis of known alternatively spliced variants at both protein sequence and structure levels and have shown that threading is, in general, a viable approach for modeling structures of alternatively spliced variants. An examination of alternative splicing at the protein sequence level revealed that the size of splicing events follows the power law distribution and the majority of splicing isoforms harbor only one or two alternations. We examined alternative splicing in the context of protein 3D structures and found that the boundaries of alternative splicing events generally happen in coil regions of secondary structures and exposed residues and the majority of the sequences involved in splicing are located on the surface of proteins. In light of these findings, we then proceeded to demonstrate that threading represents a useful tool for structure prediction of alternative splicing isoforms and addressed the fold stability issue of threading-based structure prediction by molecular dynamics simulation. Our analysis and the insights gained have helped to establish a viable method for structure prediction of alternatively spliced isoforms at the genome scale.

Alternative Splicing↗

PubMed2002

Role of gene expression microarray analysis in finding complex disease genes.

The promise of gene expression studies using microarray technology has inspired much new hope for finding complex diseases genes. It has become clear that complex diseases result from collective actions of many genetic and nongenetic factors. Therefore, genetic dissection of complex diseases should be carried out in a global context. The technology of gene expression microarray analysis (GEMA) can provide such global information on transcription activities of essentially all genes simultaneously. It is hoped that this promising technology can be applied to samples drawn from large-scale, well-defined genetic epidemiological studies and help us untangle the web of pathways leading to complex diseases. However, extremely noisy GEMA data pose serious challenges in terms of the statistical methodologies needed. Extensive work is needed in order to respond to the challenges before one can fully utilize the potential power provided by GEMA. We begin in this paper by identifying several statistical problems related to the application of GEMA to genetic epidemiological analysis, and consider study designs that might benefit from this promising new technology. While it is still too early to tell how much of the enormous potential of GEMA will be realized ultimately, its success will probably depend most critically on the ability of statistical genetics to rise to the challenge of mining information from a sea of noise.

Gene Expression↗