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Machine learning for protein folding and dynamics
Many aspects of the study of protein folding and dynamics have been affe...
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Deep Learning in Protein Structural Modeling and Design
Deep learning is catalyzing a scientific revolution fueled by big data, ...
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Boosting Combinatorial Problem Modeling with Machine Learning
In the past few years, the area of Machine Learning (ML) has witnessed t...
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Machine Learning Force Fields
In recent years, the use of Machine Learning (ML) in computational chemi...
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Intelligence plays dice: Stochasticity is essential for machine learning
Many fields view stochasticity as a way to gain computational efficiency...
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Field Label Prediction for Autofill in Web Browsers
Automatic form fill is an important productivity related feature present...
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Feature vector regularization in machine learning
Problems in machine learning (ML) can involve noisy input data, and ML c...
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Computational prediction of RNA tertiary structures using machine learning methods
RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.
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