Large biomedical model

Ebook "Java for bioinformatics and biomedical applications" represents an important development, giving the reader an opportunity to discover how the use of open and reusable Java code can solve large bioinformatics problems in a software engineered and robust way. This has lead to an explosion of interest in the subject, and a similar explosion in tools and data resources for researchers to learn and use in their work.

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Recent completion of swine genome may simplify the production of swine as a large biomedical model. Here we studied sequence and location of known swine miRNA genes, key regulators of protein-coding genes at the level of RNA, and compared them to human and mouse data to prioritize future molecular studies.

12p vinarcissa 21-03-2023 2 1   Download

Genome-wide association studies (GWAS) interrogate large-scale whole genome to characterize the complex genetic architecture for biomedical traits. When the number of SNPs dramatically increases to half million but the sample size is still limited to thousands, the traditional p-value based statistical approaches suffer from unprecedented limitations.

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Deconvolution analyses have been widely used to track compositional alterations of cell types in gene expression data. Although a large number of novel methods have been developed, due to a lack of understanding of the effects of modeling assumptions and tuning parameters, it is challenging for researchers to select an optimal deconvolution method suitable for the targeted biological conditions.

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Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects.

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With more and more protein sequences produced in the genomic era, predicting protein structures from sequences becomes very important for elucidating the molecular details and functions of these proteins for biomedical research.

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Biomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully used for this task. However, these state-of-the-art BNER systems largely depend on hand-crafted features.

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Structural modeling of protein-protein interactions produces a large number of putative configurations of the protein complexes. Identification of the near-native models among them is a serious challenge. Publicly available results of biomedical research may provide constraints on the binding mode, which can be essential for the docking.

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The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling.

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Bioinformatics is at a crossroads. We work in a field that is changing every day, increasingly moving from specific solutions created by single researchers working alone or in small groups to larger, often geographically dispersed programs enabled by collaborative computing and open software. This book represents an important development, giving the reader an opportunity to discover how the use of open and reusable Java code can solve large bioinformatics problems in a software engineered and robust way.

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The goals of experimental neuroscience research are fundamentally to gain mechanistic understanding of the pathology of disease in order to identify appropriate targets for potential pharmacological intervention and evaluation of putative therapies. With the advances in gene manipulation and transgene technologies, we have unprecedented ability to generate animal models of disease that more closely mimic the clinical conditions. Noninvasive techniques capable of investigating altered pathophysiologies are now of paramount importance.

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The appearance of digital computers and the development of modern theories of learning and neural processing both occurred at about the same time, during the late 1940s. Since that time, the digital computer has been used as a tool to model individual neurons as well as clusters of neurons, which are called neural networks. A large body of neurophysiological research has accumulated since then. For a good review of this research, see Neural and Brain Modeling by Ronald J. MacGregor [21].

411p tailieuvip13 25-07-2012 47 8   Download