Constructing query-driven dynamic machine learning model with application to protein-ligand binding sites prediction

IEEE Transactions on Nanobioscience
Dong-Jun YuHong-Bin Shen

Abstract

We are facing an era with annotated biological data rapidly and continuously generated. How to effectively incorporate new annotated data into the learning step is crucial for enhancing the performance of a bioinformatics prediction model. Although machine-learning-based methods have been extensively used for dealing with various biological problems, existing approaches usually train static prediction models based on fixed training datasets. The static approaches are found having several disadvantages such as low scalability and impractical when training dataset is huge. In view of this, we propose a dynamic learning framework for constructing query-driven prediction models. The key difference between the proposed framework and the existing approaches is that the training set for the machine learning algorithm of the proposed framework is dynamically generated according to the query input, as opposed to training a general model regardless of queries in traditional static methods. Accordingly, a query-driven predictor based on the smaller set of data specifically selected from the entire annotated base dataset will be applied on the query. The new way for constructing the dynamic model enables us capable of updating the annotate...Continue Reading

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Citations

Nov 6, 2015·International Journal of Molecular Sciences·Mayumi EgusaShinsuke Ifuku
Dec 24, 2015·International Journal of Molecular Sciences·Daniel Barry RocheLiam James McGuffin
Jan 31, 2018·Cell Biochemistry and Biophysics·Rituraj PurohitVipin Hallan
Mar 2, 2018·Current Drug Discovery Technologies·Elaheh Kashani-AminAzadeh Ebrahim-Habibi
Mar 7, 2020·Computational and Structural Biotechnology Journal·Jingtian ZhaoLe Zhang

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