A simple and robust method for connecting small-molecule drugs using gene-expression signatures

Interaction of a drug or chemical with a biological system can result in a gene-expression profile or signature characteristic of the event. Using a suitably robust algorithm these signatures can potentially be used to connect molecules with similar pharmacological or toxicological properties. The Connectivity Map was a novel concept and innovative tool first introduced by Lamb et al to connect small molecules, genes, and diseases using genomic signatures [Lamb et al (2006), Science 313, 1929-1935]. However, the Connectivity Map had some limitations, particularly there was no effective safeguard against false connections if the observed connections were considered on an individual-by-individual basis. Further when several connections to the same small-molecule compound were viewed as a set, the implicit null hypothesis tested was not the most relevant one for the discovery of real connections. Here we propose a simple and robust method for constructing the reference gene-expression profiles and a new connection scoring scheme, which importantly allows the valuation of statistical significance of all the connections observed. We tested the new method with the two example gene-signatures (HDAC inhibitors and Estrogens) used by Lamb et al and also a new gene signature of immunosuppressive drugs. Our testing with this new method shows that it achieves a higher level of specificity and sensitivity than the original method. For example, our method successfully identified raloxifene and tamoxifen as having significant anti-estrogen effects, while Lamb et al's Connectivity Map failed to identify these. With these properties our new method has potential use in drug development for the recognition of pharmacological and toxicological properties in new drug candidates.Comment: 8 pages, 2 figures, and 2 tables; supplementary data supplied as a ZIP fil

Altered serological and cellular reactivity to H-2 antigens after target cell infection with vaccinia virus

MICE generate cytotoxic T lymphocytes (CTL) which are able to lyse virus infected target cells in vitro after infection with lymphocytic choriomeningitis virus (LCMV) and pox-viruses1−3. CTL kill syngeneic and semiallogenic infected cells but not allogenic infected targets. Target cell lysis in these systems seems to be restricted by H-2 antigens, especially by the K or D end of the major histocompatibility complex (MHC). In experiments where virus specific sensitised lymphocytes kill virus infected allogenic target cells4 the effector lymphocytes have not been characterised exactly. Recent investigations suggest that the active cell in this assay, at least in the measles infection, is a non-thymus derived cell (H. Kreth, personal communication). An H-2 restriction of cell mediated cytolysis (CMC) to trinitrophenol (TNP)-modified lymphocytes has also been described5. Zinkernagel and Doherty6 postulated that the CTL is directed against syngeneic H-2 antigens and viral antigens and they suggested an alteration of H-2 induced by the LCMV infection. Earlier7 we found a close topological relationship between H-2 antigens and the target antigen(s) responsible for CMC in the vaccinia system. Here we report experiments which were carried out to prove alteration of H-2 after infection of L-929 fibroblasts with vaccinia virus

Data mining in biomedicine : current applications and further directions for research

Author name used in this manuscript: S. K. KwokAuthor name used in this manuscript: A. H. C. Tsang2009-2010 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

Copyright @ 2013 Tseng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background - Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared. Methods - The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests. Results - In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively). Conclusions - The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.National Health Research Institutes in Taiwa

Enriching for correct prediction of biological processes using a combination of diverse classifiers

<p>Abstract</p> <p>Background</p> <p>Machine learning models (classifiers) for classifying genes to biological processes each have their own unique characteristics in what genes can be classified and to what biological processes. No single learning model is qualitatively superior to any other model and overall precision for each model tends to be low. The classification results for each classifier can be complementary and synergistic suggesting the benefit of a combination of algorithms, but often the prediction probability outputs of various learning models are neither comparable nor compatible for combining. A means to compare outputs regardless of the model and data used and combine the results into an improved comprehensive model is needed.</p> <p>Results</p> <p>Gene expression patterns from NCI's panel of 60 cell lines were used to train a Random Forest, a Support Vector Machine and a Neural Network model, plus two over-sampled models for classifying genes to biological processes. Each model produced unique characteristics in the classification results. We introduce the Precision Index measure (PIN) from the maximum posterior probability that allows assessing, comparing and combining multiple classifiers. The class specific precision measure (PIC) is introduced and used to select a subset of predictions across all classes and all classifiers with high precision. We developed a single classifier that combines the PINs from these five models in prediction and found that the PIN Combined Classifier (PINCom) significantly increased the number of correctly predicted genes over any single classifier. The PINCom applied to test genes that were not used in training also showed substantial improvement over any single model.</p> <p>Conclusions</p> <p>This paper introduces novel and effective ways of assessing predictions by their precision and recall plus a method that combines several machine learning models and capitalizes on synergy and complementation in class selection, resulting in higher precision and recall. Different machine learning models yielded incongruent results each of which were successfully combined into one superior model using the PIN measure we developed. Validation of the boosted predictions for gene functions showed the genes to be accurately predicted.</p

Mutation spectrum of 122 hemophilia A families from Taiwanese population by LD-PCR, DHPLC, multiplex PCR and evaluating the clinical application of HRM

<p>Abstract</p> <p>Background</p> <p>Hemophilia A represents the most common and severe inherited hemorrhagic disorder. It is caused by mutations in the F8 gene, which leads to a deficiency or dysfunctional factor VIII protein, an essential cofactor in the factor X activation complex.</p> <p>Methods</p> <p>We used long-distance polymerase chain reaction and denaturing high performance liquid chromatography for mutation scanning of the F8 gene. We designed the competitive multiplex PCR to identify the carrier with exonal deletions. In order to facilitate throughput and minimize the cost of mutation scanning, we also evaluated a new mutation scanning technique, high resolution melting analysis (HRM), as an alternative screening method.</p> <p>Results</p> <p>We presented the results of detailed screening of 122 Taiwanese families with hemophilia A and reported twenty-nine novel mutations. There was one family identified with whole exons deletion, and the carriers were successfully recognized by multiplex PCR. By HRM, the different melting curve patterns were easily identified in 25 out of 28 cases (89%) and 15 out of 15 (100%) carriers. The sensitivity was 93 % (40/43). The overall mutation detection rate of hemophilia A was 100% in this study.</p> <p>Conclusion</p> <p>We proposed a diagnostic strategy for hemophilia A genetic diagnosis. We consider HRM as a powerful screening tool that would provide us with a more cost-effective protocol for hemophilia A mutation identification.</p

STK295900, a Dual Inhibitor of Topoisomerase 1 and 2, Induces G<inf>2</inf> Arrest in the Absence of DNA Damage

STK295900, a small synthetic molecule belonging to a class of symmetric bibenzimidazoles, exhibits antiproliferative activity against various human cancer cell lines from different origins. Examining the effect of STK295900 in HeLa cells indicates that it induces G2 phase arrest without invoking DNA damage. Further analysis shows that STK295900 inhibits DNA relaxation that is mediated by topoisomerase 1 (Top 1) and topoisomerase 2 (Top 2) in vitro. In addition, STK295900 also exhibits protective effect against DNA damage induced by camptothecin. However, STK295900 does not affect etoposide-induced DNA damage. Moreover, STK295900 preferentially exerts cytotoxic effect on cancer cell lines while camptothecin, etoposide, and Hoechst 33342 affected both cancer and normal cells. Therefore, STK295900 has a potential to be developed as an anticancer chemotherapeutic agent. © 2013 Kim et al

Effective Interventions and Decline of Antituberculosis Drug Resistance in Eastern Taiwan, 2004–2008

BACKGROUND: The Taiwan health authority recently launched several tuberculosis (TB) control interventions, which may have an impact on the epidemic of drug-resistant TB. We conducted a population-based antituberculosis drug resistance surveillance program in Eastern Taiwan to measure the proportions of notified TB patients with anti-TB drug resistance and the trend from 2004 to 2008. METHODS AND FINDINGS: All culture-positive TB patients were enrolled. Drug susceptibility testing results of the first isolate of each TB patient in each treatment course were analyzed. In total, 2688 patients were included, of which 2176 (81.0%) were new TB cases and 512 (19.0%) were previously treated cases. Among the 2176 new TB cases, 97 (4.5%) were retreated after the first episode of TB treatment within the study period. The proportion of new patients with any resistance, isoniazid resistance but not multidrug-resistant TB (resistant to at least isoniazid and rifampin, MDR-TB), and MDR-TB was 16.4%, 7.5%, and 4.0%, respectively, and that among previously treated cases was 30.9%, 7.9%, and 17.6%, respectively. The combined proportion of any resistance decreased from 23.3% in 2004 to 14.3% in 2008, and that of MDR-TB from 11.5% to 2.4%. CONCLUSIONS: The proportion of TB patients with drug-resistant TB in Eastern Taiwan remains substantial. However, an effective TB control program has successfully driven the proportion of drug resistance among TB patients downward

Notch signaling during human T cell development

Notch signaling is critical during multiple stages of T cell development in both mouse and human. Evidence has emerged in recent years that this pathway might regulate T-lineage differentiation differently between both species. Here, we review our current understanding of how Notch signaling is activated and used during human T cell development. First, we set the stage by describing the developmental steps that make up human T cell development before describing the expression profiles of Notch receptors, ligands, and target genes during this process. To delineate stage-specific roles for Notch signaling during human T cell development, we subsequently try to interpret the functional Notch studies that have been performed in light of these expression profiles and compare this to its suggested role in the mouse