Browsing by Subject "Computational science"

Now showing 1 - 3 of 3
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    The International Conference on Intelligent Biology and Medicine (ICIBM) 2019: bioinformatics methods and applications for human diseases
    (BMC, 2019-12-20) Zhao, Zhongming; Dai, Yulin; Zhang, Chi; Mathé, Ewy; Wei, Lai; Wang, Kai; Medical and Molecular Genetics, School of Medicine
    Between June 9–11, 2019, the International Conference on Intelligent Biology and Medicine (ICIBM 2019) was held in Columbus, Ohio, USA. The conference included 12 scientific sessions, five tutorials or workshops, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics in bioinformatics, medical informatics, systems biology and intelligent computing. Here, we describe 13 high quality research articles selected for publishing in BMC Bioinformatics.
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    A Metabolomics Approach for Early Prediction of Vincristine-Induced Peripheral Neuropathy
    (Springer, 2020-04-15) Verma, Parul; Devaraj, Jayachandran; Skiles, Jodi L.; Sajdyk, Tammy; Ho, Richard H.; Hutchinson, Raymond; Wells, Elizabeth; Li, Lang; Renbarger, Jamie; Cooper, Bruce; Ramkrishna, Doraiswami; Pediatrics, School of Medicine
    Vincristine is a core chemotherapeutic drug administered to pediatric acute lymphoblastic leukemia patients. Despite its efficacy in treating leukemia, it can lead to severe peripheral neuropathy in a subgroup of the patients. Peripheral neuropathy is a debilitating and painful side-effect that can severely impact an individual’s quality of life. Currently, there are no established predictors of peripheral neuropathy incidence during the early stage of chemotherapeutic treatment. As a result, patients who are not susceptible to peripheral neuropathy may receive sub-therapeutic treatment due to an empirical upper cap on the dose, while others may experience severe neuropathy at the same dose. Contrary to previous genomics based approaches, we employed a metabolomics approach to identify small sets of metabolites that can be used to predict a patient’s susceptibility to peripheral neuropathy at different time points during the treatment. Using those identified metabolites, we developed a novel strategy to predict peripheral neuropathy and subsequently adjust the vincristine dose accordingly. In accordance with this novel strategy, we created a free user-friendly tool, VIPNp, for physicians to easily implement our prediction strategy. Our results showed that focusing on metabolites, which encompasses both genotypic and phenotypic variations, can enable early prediction of peripheral neuropathy in pediatric leukemia patients.
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    The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services
    (Springer Nature, 2019-05-23) Avesani, Paolo; McPherson, Brent; Hayashi, Soichi; Caiafa, Cesar F.; Henschel, Robert; Garyfallidis, Eleftherios; Kitchell, Lindsey; Bullock, Daniel; Patterson, Andrew; Olivetti, Emanuele; Sporns, Olaf; Saykin, Andrew J.; Wang, Lei; Dinov, Ivo; Hancock, David; Caron, Bradley; Qian, Yiming; Pestilli, Franco; Radiology and Imaging Sciences, School of Medicine
    We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.