Complex diseases are often the downstream event of a number of risk factors, including both environmental and genetic variables. To better understand the mechanism of disease onset, it is of great interest to systemat...
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Complex diseases are often the downstream event of a number of risk factors, including both environmental and genetic variables. To better understand the mechanism of disease onset, it is of great interest to systematically investigate the crosstalk among various risk factors. Bayesian networks provide an intuitive graphical interface that captures not only the association but also the conditional independence and dependence structures among the variables, resulting in sparser relationships between risk factors and the disease phenotype than traditional correlation-based methods. In this paper, we apply a Bayesian network to dissect the complex regulatory relationships among disease traits and various risk factors for the Genetic Analysis Workshop 17 simulated data. We use the Bayesian network as a tool for the risk prediction of disease outcome.
Human epidermal growth factor receptor 2 amplified (HER2+) breast cancer is common and aggressive. Trastuzumab is a targeted therapy to the HER2 cell surface receptor and has greatly improved prognosis for HER2+ breas...
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Identifying transcription factor binding sites (TFBSs) is crucial for understanding the mechanism of transcriptional regulation. It is known that transcription factors (TFs) often cooperate to regulate genes. While tr...
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Error correction is often an important first step prior to analyzing reads from next-generation DNA sequencers. This talk will be focused on a flexible read decomposition method developed to improve the accuracy of er...
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Error correction is often an important first step prior to analyzing reads from next-generation DNA sequencers. This talk will be focused on a flexible read decomposition method developed to improve the accuracy of error correction and make it more computationally efficient. The method relies on decomposing a read by overlapping tiles, each containing two or more kmers that serve as the basis for error correction. While the value of k is chosen so that the kmer occurs with sufficient frequency, the surrounding kmers within the tile provide the context for improving specificity and resolve ambiguity. The method adopts a flexible tile decomposition strategy to make swift progress in regions with sparse occurrence of errors and thorough exploration in regions of clustered errors. Space usage is reduced by avoiding explicit construction and storage of relationships among kmers, instead relying on the creation of space-efficient data structures that can compute such information on the fly. Experimental verification on benchmark data sets from Illumina Genome Analyzer confirms that the method achieves high error correction accuracy, while the improvements in run-time and memory usage enable scaling to large data sets.
computational protein-protein docking is a valuable tool for determining the conformation of complexes formed by interacting proteins. Selecting near-native conformations from the large number of possible models gener...
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We propose a novel method of calculation of free energy for coarse grained models of proteins by combining our newly developed multibody potentials with entropies computed from elastic network models of proteins. Mult...
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Evolution and Medicine is a curriculum supplement designed by the National Institutes of Health (NIH) and the Biological Sciences Curriculum Study (BSCS) for high school students. The supplement is freely available fr...
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Background. Convenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice...
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Background. Convenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice that offers "live" data. However, the functionality that a database offers cannot be represented in a static data download file, and webservices may consume considerable computational resources from the host server. Results. MetNetAPI is a versatile Application programming Interface (API) to the MetNetDB database. It abstracts, captures and retains operations away from a biological network repository and website. A range of database functions, previously only available online, can be immediately (and independently from the website) applied to a dataset of interest. Data is available in four layers: molecular entities, localized entities (linked to a specific organelle), interactions, and pathways. Navigation between these layers is intuitive (e.g. one can request the molecular entities in a pathway, as well as request in what pathways a specific entity participates). Data retrieval can be customized: Network objects allow the construction of new and integration of existing pathways and interactions, which can be uploaded back to our server. In contrast to webservices, the computational demand on the host server is limited to processing data-related queries only. Conclusions. An API provides several advantages to a systems biology software platform. MetNetAPI illustrates an interface with a central repository of data that represents the complex interrelationships of a metabolic and regulatory network. As an alternative to data-dumps and webservices, it allows access to a current and "live" database and exposes analytical functions to application developers. Yet it only requires limited resources on the server-side (thin server/fat client setup). The API is available for Java, *** and R programming environments and offers flexible query and bro
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