SBOLDesigner 2: An Intuitive Tool for Structural Genetic Design

Zhang, Michael; McLaughlin, James Alastair; Wipat, Anil; Myers, Chris J.

doi:10.1021/acssynbio.6b00275

Cited by 41 publications

(49 citation statements)

References 16 publications

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“…To increase the accuracy, ELM is combined with sparse representation. This hybrid algorithm performs classification in two fundamental steps [38–40]. In the first stage, the ELM network is trained with the conventional training approach.…”

Section: Proposed Methods: Classification Of Stages Of Ad Progressionmentioning

confidence: 99%

Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

Lama

Gwak

Park

et al. 2017

Journal of Healthcare Engineering

117

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Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer's disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

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Section: Proposed Methods: Classification Of Stages Of Ad Progressionmentioning

confidence: 99%

Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

Lama

Gwak

Park

et al. 2017

Journal of Healthcare Engineering

117

View full text Add to dashboard Cite

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“…The use of the libSBOLj SynBioHub integration has been demonstrated in SBOLDesigner 12 and iBioSim 13 tools to query to find existing parts in SynBioHub and upload new designs to SynBioHub.…”

Section: Availabilitymentioning

confidence: 99%

SynBioHub: A Standards-Enabled Design Repository for Synthetic Biology

et al. 2018

Self Cite

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The SynBioHub repository ( https://synbiohub.org ) is an open-source software project that facilitates the sharing of information about engineered biological systems. SynBioHub provides computational access for software and data integration, and a graphical user interface that enables users to search for and share designs in a Web browser. By connecting to relevant repositories (e.g., the iGEM repository, JBEI ICE, and other instances of SynBioHub), the software allows users to browse, upload, and download data in various standard formats, regardless of their location or representation. SynBioHub also provides a central reference point for other resources to link to, delivering design information in a standardized format using the Synthetic Biology Open Language (SBOL). The adoption and use of SynBioHub, a community-driven effort, has the potential to overcome the reproducibility challenge across laboratories by helping to address the current lack of information about published designs.

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“…One of the aspects of these tools that is most critical to the field of biosensor development is the ability to share and reproduce the design, parameterization, and measurement of biosensors between groups and study systems. Common standards for the description of genetic designs and models have been established (Hucka et al, 2015; Martínez-García et al, 2015; Cox et al, 2018), alongside tools for developing and parameterizing (Harris et al, 2017;Zhang et al, 2017;Choi et al, 2018;Shockley et al, 2018;Wandy et al, 2018;Watanabe et al, 2018), as well as visualizing and communicating these designs and models (Merchant et al, 2016;Cox et al, 2017;Der et al, 2017;Medley et al, 2018). Laboratory inventory management and electronic laboratory notebook systems have also been developed to provide a higher degree of organization and reproducibility in the wet lab (List et al, 2014(List et al, , 2015Barillari et al, 2016;Craig et al, 2017;Klavins, 2017).…”

Section: Buildmentioning

confidence: 99%

Plant Synthetic Biology: Quantifying the “Known Unknowns” and Discovering the “Unknown Unknowns”

Wright

Nemhauser

2019

Plant Physiol.

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Our knowledge of plant biology has reached the point where we can begin to rationally engineer plant form and function to meet our needs. From a bioengineer's or synthetic biologist's point of view, the goal of studying developmental biology is to generate a predictive model that specifies the molecular circuitry required to move a cell from one state to another. This model could then serve as a guide for harvesting the most useful parts and logic to enable the engineering of novel states and multicell behaviors. Among the most critical parts to understand from this perspective are the signaling molecules that enable intra-and intercellular communication. Several biosensors have been developed in recent years to detect plant-specific signals and secondary messengers. Many other general biosensors have been successfully implemented in plant systems. These biosensors, in combination with single cell "omics" techniques and predictive statistical frameworks, are providing the type of high resolution, quantitative descriptions of cell state that will ultimately make it possible to decode and re-engineer traits associated with higher yields and stress tolerance. Being a plant developmental biologist today can feel like a lot like being a cryptographer piecing together fragmented messages with only a partial knowledge of the cipher. Biological signaling is rife with redundancy, feedback, and feedforward motifs acting to dampen or amplify each signal, and modulate outputs depending on position and cell identity. To crack the code of these complex genetic signal processors, it is important to be able to measure, as well as manipulate, both signals and responses. Recent advances in synthetic biology have provided a means to access such tools. Sensitive, genetically encoded reporters (biosensors), in combination with emerging single-cell transcriptomics

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SBOLDesigner 2: An Intuitive Tool for Structural Genetic Design

Cited by 41 publications

References 16 publications

Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

SynBioHub: A Standards-Enabled Design Repository for Synthetic Biology

Plant Synthetic Biology: Quantifying the “Known Unknowns” and Discovering the “Unknown Unknowns”

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