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      The Computational Drug Repositioning Without Negative Sampling

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules

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              What is a support vector machine?

              Support vector machines (SVMs) are becoming popular in a wide variety of biological applications. But, what exactly are SVMs and how do they work? And what are their most promising applications in the life sciences?
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                Author and article information

                Contributors
                Journal
                IEEE/ACM Transactions on Computational Biology and Bioinformatics
                IEEE/ACM Trans. Comput. Biol. and Bioinf.
                Institute of Electrical and Electronics Engineers (IEEE)
                1545-5963
                1557-9964
                2374-0043
                March 1 2023
                March 1 2023
                : 20
                : 2
                : 1506-1517
                Affiliations
                [1 ]Department of Automation, Ningbo Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai, China
                Article
                10.1109/TCBB.2022.3212051
                68d8471a-38e7-40ca-9cb5-65227e9456d9
                © 2023

                https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-037

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