Abstract Proteomics is nowadays one of the most important and relevant fields from computational ... more Abstract Proteomics is nowadays one of the most important and relevant fields from computational biology, raising a lot of challenging and provocative questions. Gaining an understanding of protein dynamic and function as well as obtaining additional insights into the protein folding process is still of great interest in bionformatics and medicine. This paper introduces a new approach A n o m a l P for detecting anomalous protein conformational transitions using deep autoencoders for encoding information about the structural similarity between proteins belonging to the same superfamily. Experiments are conducted on real protein data and the obtained results emphasize the potential of autoencoders to learn biological relevant patterns, such as proteins’ structural characteristics and that they are useful for detecting conformations or proteins which are likely to be anomalous with respect to a superfamily. The study performed in this paper is aimed to provide better insights of proteins structural similarity, with the broader goal of learning to predict proteins conformational transitions.
This paper investigates the problem of supervisedly classifying proteins according to their struc... more This paper investigates the problem of supervisedly classifying proteins according to their structural similarity, based on the information enclosed within their conformational transitions. We are proposing AutoSimP approach consisting of an ensemble of autoencoders for predicting the similarity class of a certain protein, considering the similarity predicted for its conformational transitions. Experiments performed on real protein data reveal the effectiveness of our proposal compared with similar existing approaches.
Abstract Proteomics is nowadays one of the most important and relevant fields from computational ... more Abstract Proteomics is nowadays one of the most important and relevant fields from computational biology, raising a lot of challenging and provocative questions. Gaining an understanding of protein dynamic and function as well as obtaining additional insights into the protein folding process is still of great interest in bionformatics and medicine. This paper introduces a new approach A n o m a l P for detecting anomalous protein conformational transitions using deep autoencoders for encoding information about the structural similarity between proteins belonging to the same superfamily. Experiments are conducted on real protein data and the obtained results emphasize the potential of autoencoders to learn biological relevant patterns, such as proteins’ structural characteristics and that they are useful for detecting conformations or proteins which are likely to be anomalous with respect to a superfamily. The study performed in this paper is aimed to provide better insights of proteins structural similarity, with the broader goal of learning to predict proteins conformational transitions.
This paper investigates the problem of supervisedly classifying proteins according to their struc... more This paper investigates the problem of supervisedly classifying proteins according to their structural similarity, based on the information enclosed within their conformational transitions. We are proposing AutoSimP approach consisting of an ensemble of autoencoders for predicting the similarity class of a certain protein, considering the similarity predicted for its conformational transitions. Experiments performed on real protein data reveal the effectiveness of our proposal compared with similar existing approaches.
Uploads
Papers by Carmina Codre