Co-evolutionary models such as direct coupling analysis (DCA) in combination with machine learning (ML) techniques based on deep neural networks are able to predict accurate protein contact or distance maps. Such information can be used as constraint...
RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning...
Optical microrobotics is an emerging field that has the potential to improve upon current optical tweezer studies through avenues such as limiting the exposure of biological molecules of interest to laser radiation and overcoming the current limitati...
Non-coding RNAs (ncRNAs) play crucial roles in multiple biological processes. However, only a few ncRNAs' functions have been well studied. Given the significance of ncRNAs classification for understanding ncRNAs' functions, more and more computation...
Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling...
The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to det...
A good scoring function is necessary for ab inito prediction of RNA tertiary structures. In this study, we explored the power of a machine learning based approach as a scoring function. Compared with the traditional scoring functions, the present app...
MOTIVATION: The convolutional neural network (CNN) has been applied to the classification problem of DNA sequences, with the additional purpose of motif discovery. The training of CNNs with distributed representations of four nucleotides has successf...
MOTIVATION: Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative ex...
MOTIVATION: Transcription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of ...
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