Biomedical subjects

David W Kane

Publications and source records attributed to David W Kane.

7 recordsLinked to original sources

PubMed2006

Cancers as wounds that do not heal: differences and similarities between renal regeneration/repair and renal cell carcinoma.

Cancers have been described as wounds that do not heal, suggesting that the two share common features. By comparing microarray data from a model of renal regeneration and repair (RRR) with reported gene expression in renal cell carcinoma (RCC), we asked whether those two processes do, in fact, share molecular features and regulatory mechanisms. The majority (77%) of the genes expressed in RRR and RCC were concordantly regulated, whereas only 23% were discordant (i.e., changed in opposite directions). The orchestrated processes of regeneration, involving cell proliferation and immune response, were reflected in the concordant genes. The discordant gene signature revealed processes (e.g., morphogenesis and glycolysis) and pathways (e.g., hypoxia-inducible factor and insulin-like growth factor-I) that reflect the intrinsic pathologic nature of RCC. This is the first study that compares gene expression patterns in RCC and RRR. It does so, in particular, with relation to the hypothesis that RCC resembles the wound healing processes seen in RRR. However, careful attention to the genes that are regulated in the discordant direction provides new insights into the critical differences between renal carcinogenesis and wound healing. The observations reported here provide a conceptual framework for further efforts to understand the biology and to develop more effective diagnostic biomarkers and therapeutic strategies for renal tumors and renal ischemia.

Animals↗

PubMed2006

Agile methods in biomedical software development: a multi-site experience report.

BACKGROUND: Agile is an iterative approach to software development that relies on strong collaboration and automation to keep pace with dynamic environments. We have successfully used agile development approaches to create and maintain biomedical software, including software for bioinformatics. This paper reports on a qualitative study of our experiences using these methods. RESULTS: We have found that agile methods are well suited to the exploratory and iterative nature of scientific inquiry. They provide a robust framework for reproducing scientific results and for developing clinical support systems. The agile development approach also provides a model for collaboration between software engineers and researchers. We present our experience using agile methodologies in projects at six different biomedical software development organizations. The organizations include academic, commercial and government development teams, and included both bioinformatics and clinical support applications. We found that agile practices were a match for the needs of our biomedical projects and contributed to the success of our organizations. CONCLUSION: We found that the agile development approach was a good fit for our organizations, and that these practices should be applicable and valuable to other biomedical software development efforts. Although we found differences in how agile methods were used, we were also able to identify a set of core practices that were common to all of the groups, and that could be a focus for others seeking to adopt these methods.

Algorithms↗

PubMed2005

High-Throughput GoMiner, an 'industrial-strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID).

BACKGROUND: We previously developed GoMiner, an application that organizes lists of 'interesting' genes (for example, under-and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. The original version of GoMiner was oriented toward visualization and interpretation of the results from a single microarray (or other high-throughput experimental platform), using a graphical user interface. Although that version can be used to examine the results from a number of microarrays one at a time, that is a rather tedious task, and original GoMiner includes no apparatus for obtaining a global picture of results from an experiment that consists of multiple microarrays. We wanted to provide a computational resource that automates the analysis of multiple microarrays and then integrates the results across all of them in useful exportable output files and visualizations. RESULTS: We now introduce a new tool, High-Throughput GoMiner, that has those capabilities and a number of others: It (i) efficiently performs the computationally-intensive task of automated batch processing of an arbitrary number of microarrays, (ii) produces a human-or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories, (iii) integrates the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories, (iv) provides a fast form of 'false discovery rate' multiple comparisons calculation, and (v) provides annotations and visualizations for relating transcription factor binding sites to genes and GO categories. CONCLUSION: High-Throughput GoMiner achieves the desired goal of providing a computational resource that automates the analysis of multiple microarrays and integrates results across all of the microarrays. For illustration, we show an application of this new tool to the interpretation of altered gene expression patterns in Common Variable Immune Deficiency (CVID). High-Throughput GoMiner will be useful in a wide range of applications, including the study of time-courses, evaluation of multiple drug treatments, comparison of multiple gene knock-outs or knock-downs, and screening of large numbers of chemical derivatives generated from a promising lead compound.

Binding Sites↗

PubMed2005

Practical applications of usability theory to electronic data collection for clinical trials.

Pharmaceutical and device companies are more frequently considering and using electronic data collection (EDC) to collect patient-reported outcomes such as satisfaction and quality of life for clinical trials. The transition from paper-and-pencil data collection to EDC is not without risks. The unique context of clinical trials presents challenges that, if not addressed, can lead to expensive mistakes. The advantages inherent to EDC can easily be cancelled out without careful attention to the characteristics of the clinical setting. This paper provides an overview of EDC issues specific to clinical trials and health care settings. In particular, it evaluates usability issues associated with methods of EDC and suggests strategies to minimize potential problems. Lessons learned from usability testing in the unique setting of the clinical trial can be applied to other projects to decrease costs, enhance the quality of the data, and minimize time to analysis.

Clinical Trials as Topic↗

PubMed2004

Mistaken identifiers: gene name errors can be introduced inadvertently when using Excel in bioinformatics.

BACKGROUND: When processing microarray data sets, we recently noticed that some gene names were being changed inadvertently to non-gene names. RESULTS: A little detective work traced the problem to default date format conversions and floating-point format conversions in the very useful Excel program package. The date conversions affect at least 30 gene names; the floating-point conversions affect at least 2,000 if Riken identifiers are included. These conversions are irreversible; the original gene names cannot be recovered. CONCLUSIONS: Users of Excel for analyses involving gene names should be aware of this problem, which can cause genes, including medically important ones, to be lost from view and which has contaminated even carefully curated public databases. We provide work-arounds and scripts for circumventing the problem.

Animals↗

PubMed2003

GoMiner: a resource for biological interpretation of genomic and proteomic data.

We have developed GoMiner, a program package that organizes lists of 'interesting' genes (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. GoMiner provides quantitative and statistical output files and two useful visualizations. The first is a tree-like structure analogous to that in the AmiGO browser and the second is a compact, dynamically interactive 'directed acyclic graph'. Genes displayed in GoMiner are linked to major public bioinformatics resources.

Computer Graphics↗

PubMed2003

Development of gene ontology tool for biological interpretation of genomic and proteomic data.

We have designed and developed a Gene Ontology based navigation tool, GoMiner, which organizes lists of interesting genes from a microarray or a protein array experiment for biological interpretation. It provides quantitative and statistical output files and useful visualization (e.g., a tree-like structure) to map the list of genes to its biological functional categories. It also provides links to other resources such as pubmed, locuslink, and biological molecular interaction map and signaling pathway packages.

Computational Biology↗