Multi-scale parametric spectral analysis for exon detection in DNA sequences based on forward-backward linear prediction and singular value decomposition of the double-base curves

This paper presents a new method for exon detection in DNA sequences based on multi-scale parametric spectral analysis. A forward-backward linear prediction (FBLP) with the singular value decomposition (SVD) algorithm FBLP-SVD is applied to the double-base curves (DB-curves) of a DNA sequence using a variable moving window sizes to estimate the signal spectrum at multiple scales. Simulations are done on short human genes in the range of 11bp to 2032bp and the results show that our proposed method out-performs the classical Fourier transform method. The multi-scale approach is shown to be more effective than using a single scale with a fixed window size. In addition, our method is flexible as it requires no training data

Digitisation, Lossy Compression and Visualisation in Cost-Effective Telehealth Environment

Digital technology in medical imaging has become increasingly important. In recent years, more and more investment have been made in this field. With the emergence of paperless hospitals and the advent of telehealth and telemedicine applications, the importance of digital manipulations has become even greater. Undoubtedly, digital technology greatly benefits the radiology department. However, these benefits come at a price. The cost of the equipment remains an expensive proposition for now. Thus, action must be taken to lower the financial burden, especially to clinics or hospitals in rural and less affluent areas. This thesis concentrates on the aspects of digitisation, compression, and visualisation of medical images

Genetic and epigenetic biomarkers of colorectal cancer

Cancer is a heterogeneous disease caused, in part, by genetic and epigenetic alterations. These changes have been explored in studies of the pathogenesis of colorectal cancer (CRC) and have led to the identification of many biomarkers of disease progression. However, the number of biomarkers that have been incorporated into clinical practice is surprisingly small. We review the genetic and epigenetic mechanisms of colorectal cancer and discuss molecular markers recommended for use in early detection, screening, diagnosis, determination of prognosis, and prediction of treatment outcomes. We also review important areas for future research.7 page(s

The Implications of biomarker evidence for systematic reviews

Background: In Evidence-Based Medicine, clinical practice guidelines and systematic reviews are crucial devices for medical practitioners in making clinical decision. Clinical practice guidelines are systematically developed statements to support health care decisions for specific circumstances whereas systematic reviews are summaries of evidence on clearly formulated clinical questions. Biomarkers are biological measurements (primarily molecular) that are used to diagnose, predict treatment outcomes and prognosticate disease and are increasingly used in randomized controlled trials (RCT). Methods. We search PubMed for systematic reviews, RCTs, case reports and non-systematic reviews with and without mentions of biomarkers between years 1990-2011. We compared the frequency and growth rate of biomarkers and non-biomarkers publications. We also compared the growth of the proportion of biomarker-based RCTs with the growth of the proportion of biomarker-based systematic reviews. Results: With 147,774 systematic reviews indexed in PubMed from 1990 to 2011 (accessed on 18/10/2012), only 4,431 (3%) are dedicated to biomarkers. The annual growth rate of biomarkers publications is consistently higher than non-biomarkers publications, showing the growth in biomarkers research. From 20 years of systematic review publications indexed in PubMed, we identified a bias in systematic reviews against the inclusion of biomarker-based RCTs. Conclusions: With the realisation of genome-based personalised medicine, biomarkers are becoming important for clinical decision making. The bias against the inclusion of biomarkers in systematic reviews leads to medical practitioners deprive of important information they require to address clinical questions. Sparse or weak evidence and lack of genetic training for systematic reviewers may contribute to this trend.7 page(s

Role of citation tracking in updating of systematic reviews

We proposed to use automatic citation tracking to enhance the retrieval of new evidence for updating Systematic Reviews (SR). We tested on a Cochrane review from 2003 (updated 2010) and retrieved 12 of the papers to be added (recall 85.7%). Citation tracking yields a high proportion of the required literature.1 page(s

Clustering of DNA microarray temporal data based on the autoregressive model

In this paper, we propose to combine linear prediction coefficients from the autoregressive model (AR) and the time series itself as features for the clustering algorithm. The purpose of the use of the AR model is to realize the importance of dynamic modeling of microarray time series data. We define the distance among the time series profiles using the autoregressive model and use the hierarchical clustering and the k-means clustering methods for comparison. The results show that the performance of the clustering DNA microarray time course profile is increased with the linear prediction coefficients in addition to the time series itself used as features.5 page(s

Citation enrichment improves deduplication of primary evidence

Objective: To automatically detect duplicate citations in a bibliographical database. Background: Citations retrieved from multiple search databases have different forms making manual and automatic detection of duplicates difficult. Existing methods rely on fuzzy-similarity measures which are error-prone. Methods: We analysed four pairs of original search results from MEDLINE and EMBASE that were used to create systematic reviews. An automatic tool deduplicated citations by first enriching citations with Digital Object Identifiers (DOI), and/or other unique identifiers. Duplication of records was then determined by comparing these unique identifiers. We compared our method with the duplicate detection function of a popular citation management desktop application in several configurations. Results: Citation Enrichment identified 93 % (range 86 %–100 %) of the duplicates indexed online and erroneously marked 3 % (range 0 %–6 %) documents as duplicates. The citation management application found 68 % (range 64 %–72 %) without error using default setting. When set for highest deduplication, the citation management application found 94 % of duplicates (range 77 %–100 %) and 4 % error (range 0 %–8 %). Conclusion: Citation enrichment using unique identifiers enhances automatic deduplication. On its own, the approach seems slightly superior to tools that compare citations without enrichment. Methods that combine citation enrichment with existing fuzzy-matching may substantially reduce resource requirements of evidence synthesis.8 page(s