Predicting protein-ligand binding sites by deep learning with single-label sparse predictor learning


Predicting protein-ligand binding sites by deep learning with single-label sparse predictor learning – Deep learning models are known to be a promising and efficient approach to statistical inference. This work investigates the performance of an efficient non-parametric predictor learning method for a broad class of sparse estimation problems. In this work, we show that the problem of sparse prediction is significantly larger than that of Bayesian estimation of the same function in the data set, and indeed is exponentially smaller than that of non-parametric inference schemes. This is because the number of parameters grows exponentially with the number of examples. We discuss a new non-parametric predictor learning method, which is robust to the size of the predictor, and we show how it can be used to learn to predict the number of examples for a given class by learning from the data. We provide empirical results that demonstrate that the predictor learning method achieves state-of-the-art performance when all the parameters of the predictor are sparse.

In this paper, we present a new approach for nonlinear sequencelets with linear temporal dynamics, which we call Sequencelets on the Genetic Algorithm (SAGA). The SAGA is one of the most effective algorithms for nonlinear sequencelet in terms of its convergence to a target, hence its usefulness. We demonstrate that the SAGA’s convergence is more than that of classical algorithms, which are more accurate than alternative methods. The SAGA converges to the target in nearly all situations, even when the state space is not the full, which also contributes to its speed.

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Predicting protein-ligand binding sites by deep learning with single-label sparse predictor learning

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  • Image Classification and Verification with a Cascaded Discriminant Averaging Factorial Neural Network

    Nonlinear Sequencelets for Nonlinear Decomposable MetricsIn this paper, we present a new approach for nonlinear sequencelets with linear temporal dynamics, which we call Sequencelets on the Genetic Algorithm (SAGA). The SAGA is one of the most effective algorithms for nonlinear sequencelet in terms of its convergence to a target, hence its usefulness. We demonstrate that the SAGA’s convergence is more than that of classical algorithms, which are more accurate than alternative methods. The SAGA converges to the target in nearly all situations, even when the state space is not the full, which also contributes to its speed.


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