Metabolic network prediction through pairwise rational kernels

BACKGROUND: Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. RESULTS: We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times. CONCLUSIONS: The power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations

Biochemical Parameters and Histopathological Findings in the Forced Molt Laying Hens

© 2016, Fundacao APINCO de Ciencia e Tecnologia Avicolas. All rights reserved.The aim of this study was to evaluate the effects of forced molting using biochemical parameters and histopathological findings in laying hens. 36 Hyline W36 strain laying hens, 90 weeks old were chosen for this research. Eight of these chickens were randomly selected and placed in a cage as the control group before the molting program began. All the others 28 chickens were used for the forced molting program. Eight laying hens were slaughtered at the end of the molting program named as molting group. The remaining 20 hens were fed for 37 days, weighted and slaughtered when they reached the maximum egg production (80%) as postmolting group. Then, blood was analyzed for malondialdehyde, glutathione, catalase, glucose, calcium, phosphorus, albumin, globulin, total protein, triiodothyronine, thyroxine and Vitamin C. The malondialdehyde and glutathione levels of the thyroid and liver tissues were also analyzed along with an examination of the histopathological changes of the liver, ovarium and thyroid glands; and live body, liver, ovarium, thyroid weights and thyroid lengths. In conclusion, it was found that forced molting produces stress and notable side effects in hens, like the oxidant and antioxidant status of the organs, tissue weights and sizes, hormon profiles, blood biochemical and histopathological parameter changes. The activities of thyroid malondialdehyde (p<0.05), liver glutathione (p<0.01), plasma catalase (p<0.001) were significantly decreased in molting group compared to control values, while liver malondialdehyde levels were significantly increased (p<0.001) and thyroid glutathione levels had nonsignificant effect. These levels in molting hens were the first study for veterinary science