IDEAS home Printed from https://ideas.repec.org/a/adm/journl/v9y2020i1p27-34.html
   My bibliography  Save this article

The pLoc_bal-mGneg Predictor is a Powerful Web-Server for Identifying the Subcellular Localization of Gram-Negative Bacterial Proteins based on their Sequences Information Alone

Author

Listed:
  • Kuo-Chen Chou

Abstract

Recently a very powerful web-server has been developed for predicting the subcellular localization of Gram-negative bacterial proteins purely according to their sequences information for the multi-label systems, in which a same protein may appear or move between two or more location sites and hence its ID (identification) needs two or more labels for distinction, namely the “multi-label mark†. The web-server is called as “pLoc_bal-mGneg†, where “bal†means that the predictor has been treated by balancing or quasi-balancing out the training dataset [3-9], and “m†means that the predictor is with the capacity to study the multi-label systems.

Suggested Citation

  • Kuo-Chen Chou, 2020. "The pLoc_bal-mGneg Predictor is a Powerful Web-Server for Identifying the Subcellular Localization of Gram-Negative Bacterial Proteins based on their Sequences Information Alone," International Journal of Sciences, Office ijSciences, vol. 9(01), pages 27-34, January.
  • Handle: RePEc:adm:journl:v:9:y:2020:i:1:p:27-34
    DOI: 10.18483/ijSci.2248
    as

    Download full text from publisher

    File URL: https://www.ijsciences.com/pub/article/2248
    Download Restriction: no

    File URL: https://www.ijsciences.com/pub/pdf/V92020012248.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.18483/ijSci.2248?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sharaf Jameel Malebary & Muhammad Safi ur Rehman & Yaser Daanial Khan, 2019. "iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou’s 5-step rule," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-15, November.
    2. Yan Xu & Jun Ding & Ling-Yun Wu & Kuo-Chen Chou, 2013. "iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    3. Yan Xu & Xin Wen & Li-Shu Wen & Ling-Yun Wu & Nai-Yang Deng & Kuo-Chen Chou, 2014. "iNitro-Tyr: Prediction of Nitrotyrosine Sites in Proteins with General Pseudo Amino Acid Composition," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kuo-Chen Chou, 2020. "Showcase to Illustrate How the Web-Server iKcr-PseEns is Working," International Journal of Sciences, Office ijSciences, vol. 9(01), pages 85-95, January.
    2. Kuo Chen Chou, 2020. "How the Artificial Intelligence Tool iRNA-PseU is Working in Predicting the RNA Pseudouridine Sites?," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 24(2), pages 18055-18064, January.
    3. Abdollah Dehzangi & Yosvany López & Sunil Pranit Lal & Ghazaleh Taherzadeh & Abdul Sattar & Tatsuhiko Tsunoda & Alok Sharma, 2018. "Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-16, February.
    4. Bin Liu & Longyun Fang & Fule Liu & Xiaolong Wang & Junjie Chen & Kuo-Chen Chou, 2015. "Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-20, March.
    5. Wenzheng Bao & Bin Yang & Rong Bao & Yuehui Chen, 2019. "LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree," Complexity, Hindawi, vol. 2019, pages 1-9, July.
    6. Bandana Kumari & Ravindra Kumar & Manish Kumar, 2014. "PalmPred: An SVM Based Palmitoylation Prediction Method Using Sequence Profile Information," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
    7. Sabit Ahmed & Afrida Rahman & Md Al Mehedi Hasan & Md Khaled Ben Islam & Julia Rahman & Shamim Ahmad, 2021. "predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-17, April.

    More about this item

    Keywords

    pLoc_bal-mGneg; Web-Server;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:adm:journl:v:9:y:2020:i:1:p:27-34. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Staff ijSciences (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.