Type Identifier

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The Experts below are selected from a list of 72 Experts worldwide ranked by ideXlab platform

Zemin Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Author Correction: SciBet as a portable and fast single cell Type Identifier.
    Nature communications, 2021
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22248-3

  • SciBet as a portable and fast single cell Type Identifier
    Nature communications, 2020
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    Fast, robust and technology-independent computational methods are needed for supervised cell Type annotation of single-cell RNA sequencing data. We present SciBet, a supervised cell Type Identifier that accurately predicts cell identity for newly sequenced cells with order-of-magnitude speed advantage. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. Facing the exponential growth in the size of single cell RNA datasets, this user-friendly and cross-platform tool can be widely useful for single cell Type identification.

  • SciBet: a portable and fast single cell Type Identifier
    2019
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    ABSTRACT Fast, robust and technology-independent computational methods are needed for supervised cell Type annotation of single-cell RNA sequencing data. We present SciBet, a Bayesian classifier that accurately predicts cell identity for newly sequenced cells or cell clusters. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. This user-friendly and cross-platform tool can be widely useful for single cell Type identification.

Baolin Liu - One of the best experts on this subject based on the ideXlab platform.

  • Author Correction: SciBet as a portable and fast single cell Type Identifier.
    Nature communications, 2021
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22248-3

  • SciBet as a portable and fast single cell Type Identifier
    Nature communications, 2020
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    Fast, robust and technology-independent computational methods are needed for supervised cell Type annotation of single-cell RNA sequencing data. We present SciBet, a supervised cell Type Identifier that accurately predicts cell identity for newly sequenced cells with order-of-magnitude speed advantage. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. Facing the exponential growth in the size of single cell RNA datasets, this user-friendly and cross-platform tool can be widely useful for single cell Type identification.

  • SciBet: a portable and fast single cell Type Identifier
    2019
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    ABSTRACT Fast, robust and technology-independent computational methods are needed for supervised cell Type annotation of single-cell RNA sequencing data. We present SciBet, a Bayesian classifier that accurately predicts cell identity for newly sequenced cells or cell clusters. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. This user-friendly and cross-platform tool can be widely useful for single cell Type identification.

Yedan Liu - One of the best experts on this subject based on the ideXlab platform.

  • Author Correction: SciBet as a portable and fast single cell Type Identifier.
    Nature communications, 2021
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22248-3

  • SciBet as a portable and fast single cell Type Identifier
    Nature communications, 2020
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    Fast, robust and technology-independent computational methods are needed for supervised cell Type annotation of single-cell RNA sequencing data. We present SciBet, a supervised cell Type Identifier that accurately predicts cell identity for newly sequenced cells with order-of-magnitude speed advantage. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. Facing the exponential growth in the size of single cell RNA datasets, this user-friendly and cross-platform tool can be widely useful for single cell Type identification.

  • SciBet: a portable and fast single cell Type Identifier
    2019
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    ABSTRACT Fast, robust and technology-independent computational methods are needed for supervised cell Type annotation of single-cell RNA sequencing data. We present SciBet, a Bayesian classifier that accurately predicts cell identity for newly sequenced cells or cell clusters. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. This user-friendly and cross-platform tool can be widely useful for single cell Type identification.

Changya Chen - One of the best experts on this subject based on the ideXlab platform.

  • Author Correction: SciBet as a portable and fast single cell Type Identifier.
    Nature communications, 2021
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22248-3

  • SciBet as a portable and fast single cell Type Identifier
    Nature communications, 2020
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    Fast, robust and technology-independent computational methods are needed for supervised cell Type annotation of single-cell RNA sequencing data. We present SciBet, a supervised cell Type Identifier that accurately predicts cell identity for newly sequenced cells with order-of-magnitude speed advantage. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. Facing the exponential growth in the size of single cell RNA datasets, this user-friendly and cross-platform tool can be widely useful for single cell Type identification.

  • SciBet: a portable and fast single cell Type Identifier
    2019
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    ABSTRACT Fast, robust and technology-independent computational methods are needed for supervised cell Type annotation of single-cell RNA sequencing data. We present SciBet, a Bayesian classifier that accurately predicts cell identity for newly sequenced cells or cell clusters. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. This user-friendly and cross-platform tool can be widely useful for single cell Type identification.

Zedao Liu - One of the best experts on this subject based on the ideXlab platform.

  • Author Correction: SciBet as a portable and fast single cell Type Identifier.
    Nature communications, 2021
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22248-3

  • SciBet as a portable and fast single cell Type Identifier
    Nature communications, 2020
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    Fast, robust and technology-independent computational methods are needed for supervised cell Type annotation of single-cell RNA sequencing data. We present SciBet, a supervised cell Type Identifier that accurately predicts cell identity for newly sequenced cells with order-of-magnitude speed advantage. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. Facing the exponential growth in the size of single cell RNA datasets, this user-friendly and cross-platform tool can be widely useful for single cell Type identification.

  • SciBet: a portable and fast single cell Type Identifier
    2019
    Co-Authors: Baolin Liu, Boxi Kang, Zedao Liu, Yedan Liu, Changya Chen, Xianwen Ren, Zemin Zhang
    Abstract:

    ABSTRACT Fast, robust and technology-independent computational methods are needed for supervised cell Type annotation of single-cell RNA sequencing data. We present SciBet, a Bayesian classifier that accurately predicts cell identity for newly sequenced cells or cell clusters. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. This user-friendly and cross-platform tool can be widely useful for single cell Type identification.

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