DNA expression microarrays may be the wrong tool to identify biological pathways

DNA microarray expression signatures are expected to provide new insights into patho- physiological pathways. Numerous variant statistical methods have been described for each step of the signal analysis. We employed five similar statistical tests on the same data set at the level of gene selection. Inter-test agreement for the identification of biological pathways in BioCarta, KEGG and Reactome was calculated using Cohen’s k- score. The identification of specific biological pathways showed only moderate agreement (0.30 < k < 0.79) between the analysis methods used. Pathways identified by microarrays must be treated cautiously as they vary according to the statistical method used

DNA expression microarrays may be the wrong tool to identify biological pathways

DNA microarray expression signatures are expected to provide new insights into patho- physiological pathways. Numerous variant statistical methods have been described for each step of the signal analysis. We employed five similar statistical tests on the same data set at the level of gene selection. Inter-test agreement for the identification of biological pathways in BioCarta, KEGG and Reactome was calculated using Cohen's k- score. The identification of specific biological pathways showed only moderate agreement (0.30 < k < 0.79) between the analysis methods used. Pathways identified by microarrays must be treated cautiously as they vary according to the statistical method used

Volume-based non-continuum modeling of bone functional adaptation

BACKGROUND: Bone adapts to mechanical strain by rearranging the trabecular geometry and bone density. The common finite element methods used to simulate this adaptation have inconsistencies regarding material properties at each node and are computationally demanding. Here, a volume-based, non-continuum formulation is proposed as an alternative. Adaptive processes corresponding to various external mechanical loading conditions are simulated for the femur. RESULTS: Bone adaptations were modeled for one-legged stance, abduction and adduction. One-legged stance generally results in higher bone densities than the other two loading cases. The femoral head and neck are the regions where densities change most drastically under different loading conditions while the distal area always contains the lowest densities regardless of the loading conditions. In the proposed formulation, the inconsistency of material densities or strain energy densities, which is a common problem to finite element based approaches, is eliminated. The computational task is alleviated through introduction of the quasi-binary connectivity matrix and linearization operations in the Jacobian matrix and is therefore computationally less demanding. CONCLUSION: The results demonstrated the viability of the proposed formulation to study bone functional adaptation under mechanical loading

An Entropy-based gene selection method for cancer classification using microarray data

BACKGROUND: Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of non-redundant but relevant genes is difficult. The selected gene set should be small enough to allow diagnosis even in regular clinical laboratories and ideally identify genes involved in cancer-specific regulatory pathways. Here an entropy-based method is proposed that selects genes related to the different cancer classes while at the same time reducing the redundancy among the genes. RESULTS: The present study identifies a subset of features by maximizing the relevance and minimizing the redundancy of the selected genes. A merit called normalized mutual information is employed to measure the relevance and the redundancy of the genes. In order to find a more representative subset of features, an iterative procedure is adopted that incorporates an initial clustering followed by data partitioning and the application of the algorithm to each of the partitions. A leave-one-out approach then selects the most commonly selected genes across all the different runs and the gene selection algorithm is applied again to pare down the list of selected genes until a minimal subset is obtained that gives a satisfactory accuracy of classification. The algorithm was applied to three different data sets and the results obtained were compared to work done by others using the same data sets CONCLUSION: This study presents an entropy-based iterative algorithm for selecting genes from microarray data that are able to classify various cancer sub-types with high accuracy. In addition, the feature set obtained is very compact, that is, the redundancy between genes is reduced to a large extent. This implies that classifiers can be built with a smaller subset of genes

Quantitative evaluation of recall and precision of CAT Crawler, a search engine specialized on retrieval of Critically Appraised Topics

BACKGROUND: Critically Appraised Topics (CATs) are a useful tool that helps physicians to make clinical decisions as the healthcare moves towards the practice of Evidence-Based Medicine (EBM). The fast growing World Wide Web has provided a place for physicians to share their appraised topics online, but an increasing amount of time is needed to find a particular topic within such a rich repository. METHODS: A web-based application, namely the CAT Crawler, was developed by Singapore's Bioinformatics Institute to allow physicians to adequately access available appraised topics on the Internet. A meta-search engine, as the core component of the application, finds relevant topics following keyword input. The primary objective of the work presented here is to evaluate the quantity and quality of search results obtained from the meta-search engine of the CAT Crawler by comparing them with those obtained from two individual CAT search engines. From the CAT libraries at these two sites, all possible keywords were extracted using a keyword extractor. Of those common to both libraries, ten were randomly chosen for evaluation. All ten were submitted to the two search engines individually, and through the meta-search engine of the CAT Crawler. Search results were evaluated for relevance both by medical amateurs and professionals, and the respective recall and precision were calculated. RESULTS: While achieving an identical recall, the meta-search engine showed a precision of 77.26% (±14.45) compared to the individual search engines' 52.65% (±12.0) (p < 0.001). CONCLUSION: The results demonstrate the validity of the CAT Crawler meta-search engine approach. The improved precision due to inherent filters underlines the practical usefulness of this tool for clinicians

Relevance similarity: an alternative means to monitor information retrieval systems

BACKGROUND: Relevance assessment is a major problem in the evaluation of information retrieval systems. The work presented here introduces a new parameter, "Relevance Similarity", for the measurement of the variation of relevance assessment. In a situation where individual assessment can be compared with a gold standard, this parameter is used to study the effect of such variation on the performance of a medical information retrieval system. In such a setting, Relevance Similarity is the ratio of assessors who rank a given document same as the gold standard over the total number of assessors in the group. METHODS: The study was carried out on a collection of Critically Appraised Topics (CATs). Twelve volunteers were divided into two groups of people according to their domain knowledge. They assessed the relevance of retrieved topics obtained by querying a meta-search engine with ten keywords related to medical science. Their assessments were compared to the gold standard assessment, and Relevance Similarities were calculated as the ratio of positive concordance with the gold standard for each topic. RESULTS: The similarity comparison among groups showed that a higher degree of agreements exists among evaluators with more subject knowledge. The performance of the retrieval system was not significantly different as a result of the variations in relevance assessment in this particular query set. CONCLUSION: In assessment situations where evaluators can be compared to a gold standard, Relevance Similarity provides an alternative evaluation technique to the commonly used kappa scores, which may give paradoxically low scores in highly biased situations such as document repositories containing large quantities of relevant data