A Pilot Study of Multi-Input Recurrent Neural Networks for Drug-Kinase Binding Prediction
نویسندگان
چکیده
منابع مشابه
Multi-Step-Ahead Prediction of Stock Price Using a New Architecture of Neural Networks
Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...
متن کاملInput output stability of recurrent neural networks
i Foreword Recurrent neural networks are an attractive tool for both practical applications and for the modeling of biological nerve nets, but their successful application requires an understanding of their dynamical properties, in particular, their stability. The present work provides an in-depth study of this challenging issue and contributes a number of new results that are also important fo...
متن کاملRecurrent neural networks for time-series prediction
Recurrent neural networks have been used for time-series prediction with good results. In this dissertation we compare recurrent neural networks with time-delayed feed forward networks, feed forward networks and linear regression models to see which architecture that can make the most accurate predictions. The data used in all experiments is real-world sales data containing two kinds of segment...
متن کاملA constrained regularization approach for input-driven recurrent neural networks
We introduce a novel regularization approach for a class of inputdriven recurrent neural networks. The regularization of network parameters is constrained to reimplement a previously recorded state trajectory. We derive a closed-form solution for network regularization and show that the method is capable of reimplementing harvested dynamics. We investigate important properties of the method and...
متن کاملMulti-dimensional Recurrent Neural Networks
Recurrent neural networks (RNNs) have proved effective at one dimensional sequence learning tasks, such as speech and online handwriting recognition. Some of the properties that make RNNs suitable for such tasks, for example robustness to input warping, and the ability to access contextual information, are also desirable in multidimensional domains. However, there has so far been no direct way ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Molecules
سال: 2020
ISSN: 1420-3049
DOI: 10.3390/molecules25153372