Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques

CAG and GGC repeat polymorphisms in the androgen receptor gene and breast cancer susceptibility in BRCA1/2 carriers and non-carriers

Variation in the penetrance estimates for BRCA1 and BRCA2 mutations carriers suggests that other genetic polymorphisms may modify the cancer risk in carriers. A previous study has suggested that BRCA1 carriers with longer lengths of the CAG repeat in the androgen receptor (AR) gene are at increased risk of breast cancer (BC). We genotyped 188 BRCA1/2 carriers (122 affected and 66 unaffected with breast cancer), 158 of them of Ashkenazi origin, 166 BC cases without BRCA1/2 mutations and 156 Ashkenazi control individuals aged over 56 for the AR CAG and GGC repeats. In carriers, risk analyses were conducted using a variant of the log-rank test, assuming two sets of risk estimates in carriers: penetrance estimates based on the Breast Cancer Linkage Consortium (BCLC) studies of multiple case families, and lower estimates as suggested by population-based studies. We found no association of the CAG and GGC repeats with BC risk in either BRCA1/2 carriers or in the general population. Assuming BRCA1/2 penetrance estimates appropriate to the Ashkenazi population, the estimated RR per repeat adjusted for ethnic group (Ashkenazi and non-Ashkenazi) was 1.05 (95%CI 0.97–1.17) for BC and 1.00 (95%CI 0.83–1.20) for ovarian cancer (OC) for CAG repeats and 0.96 (95%CI 0.80–1.15) and 0.90 (95%CI 0.60–1.22) respectively for GGC repeats. The corresponding RR estimates for the unselected case–control series were 1.00 (95%CI 0.91–1.10) for the CAG and 1.05 (95%CI 0.90–1.22) for the GGC repeats. The estimated relative risk of BC in carriers associated with ≥28 CAG repeats was 1.08 (95%CI 0.45–2.61). Furthermore, no significant association was found if attention was restricted to the Ashkenazi carriers, or only to BRCA1 or BRCA2 carriers. We conclude that, in contrast to previous observations, if there is any effect of the AR repeat length on BRCA1 penetrance, it is likely to be weak. © 2001 Cancer Research Campaign http://www.bjcancer.co

Control of bovine mastitis: old and recent therapeutic approaches

Mastitis is defined as the inflammatory response resulting of the infection of the udder tissue and it is reported in numerous species, namely in domestic dairy animals. This pathology is the most frequent disease of dairy cattle and can be potentially fatal. Mastitis is an economically important pathology associated with reduced milk production, changes in milk composition and quality, being considered one of the most costly to dairy industry. Therefore, the majority of research in the field has focused on control of bovine mastitis and many efforts are being made for the development of new and effective anti-mastitis drugs. Antibiotic treatment is an established component of mastitis control programs; however, the continuous search for new therapeutic alternatives, effective in the control and treatment of bovine mastitis, is urgent. This review will provide an overview of some conventional and emerging approaches in the management of bovine mastitis infections.F. Gomes acknowledge the financial support of the Portuguese Foundation for Science and Technology through the Grant SFRH/BPD/84488/2012 and for financial support to the CEB research center

Perlecan Maintains microvessel integrity in vivo and modulates their formation in vitro

Perlecan is a heparan sulfate proteoglycan assembled into the vascular basement membranes (BMs) during vasculogenesis. In the present study we have investigated vessel formation in mice, teratomas and embryoid bodies (EBs) in the absence of perlecan. We found that perlecan was dispensable for blood vessel formation and maturation until embryonic day (E) 12.5. At later stages of development 40% of mutant embryos showed dilated microvessels in brain and skin, which ruptured and led to severe bleedings. Surprisingly, teratomas derived from perlecan-null ES cells showed efficient contribution of perlecan-deficient endothelial cells to an apparently normal tumor vasculature. However, in perlecan-deficient EBs the area occupied by an endothelial network and the number of vessel branches were significantly diminished. Addition of FGF-2 but not VEGF165 rescued the in vitro deficiency of the mutant ES cells. Furthermore, in the absence of perlecan in the EB matrix lower levels of FGFs are bound, stored and available for cell surface presentation. Altogether these findings suggest that perlecan supports the maintenance of brain and skin subendothelial BMs and promotes vasculo- and angiogenesis by modulating FGF-2 function

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

<p>Abstract</p> <p>Background</p> <p>RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities.</p> <p>Results</p> <p>Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (<it>N</it>-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative.</p> <p>Conclusion</p> <p>The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall <it>t</it>-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at the following site: <url>ftp://scitoolsftp.idtdna.com/SEQ2SVM/</url>.</p

Coronavirus Gene 7 Counteracts Host Defenses and Modulates Virus Virulence

Transmissible gastroenteritis virus (TGEV) genome contains three accessory genes: 3a, 3b and 7. Gene 7 is only present in members of coronavirus genus a1, and encodes a hydrophobic protein of 78 aa. To study gene 7 function, a recombinant TGEV virus lacking gene 7 was engineered (rTGEV-Δ7). Both the mutant and the parental (rTGEV-wt) viruses showed the same growth and viral RNA accumulation kinetics in tissue cultures. Nevertheless, cells infected with rTGEV-Δ7 virus showed an increased cytopathic effect caused by an enhanced apoptosis mediated by caspase activation. Macromolecular synthesis analysis showed that rTGEV-Δ7 virus infection led to host translational shut-off and increased cellular RNA degradation compared with rTGEV-wt infection. An increase of eukaryotic translation initiation factor 2 (eIF2α) phosphorylation and an enhanced nuclease, most likely RNase L, activity were observed in rTGEV-Δ7 virus infected cells. These results suggested that the removal of gene 7 promoted an intensified dsRNA-activated host antiviral response. In protein 7 a conserved sequence motif that potentially mediates binding to protein phosphatase 1 catalytic subunit (PP1c), a key regulator of the cell antiviral defenses, was identified. We postulated that TGEV protein 7 may counteract host antiviral response by its association with PP1c. In fact, pull-down assays demonstrated the interaction between TGEV protein 7, but not a protein 7 mutant lacking PP1c binding motif, with PP1. Moreover, the interaction between protein 7 and PP1 was required, during the infection, for eIF2α dephosphorylation and inhibition of cell RNA degradation. Inoculation of newborn piglets with rTGEV-Δ7 and rTGEV-wt viruses showed that rTGEV-Δ7 virus presented accelerated growth kinetics and pathology compared with the parental virus. Overall, the results indicated that gene 7 counteracted host cell defenses, and modified TGEV persistence increasing TGEV survival. Therefore, the acquisition of gene 7 by the TGEV genome most likely has provided a selective advantage to the virus

PROPER: global protein interaction network alignment through percolation matching

Background The alignment of protein-protein interaction (PPI) networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging. Results In this paper, we introduce a new global network alignment algorithm for PPI networks called PROPER. Compared to other global network alignment methods, our algorithm shows higher accuracy and speed over real PPI datasets and synthetic networks. We show that the PROPER algorithm can detect large portions of conserved biological pathways between species. Also, using a simple parsimonious evolutionary model, we explain why PROPER performs well based on several different comparison criteria. Conclusions We highlight that PROPER has high potential in further applications such as detecting biological pathways, finding protein complexes and PPI prediction. The PROPER algorithm is available at http://proper.epfl.ch