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11 papers found.
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Prediction of protein self-interactions using stacked long short-term memory from protein sequences information

study is the first to build a deep learning model for SIP prediction using protein sequence, and the results demonstrate our method is strong and practical. Notes Yan-Bin Wang and Zhu-Hong You

Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding

of Mining and Technology , Xuzhou, Jiangsu 221116 , China 2 Yu-An Huang and Zhu-Hong You are joint First Authors 3 College of Computer Science and Software Engineering, Shenzhen University , Shenzhen

Predicting Protein-Protein Interactions from Primary Protein Sequences Using a Novel Multi-Scale Local Feature Representation Scheme and the Random Forest

The study of protein-protein interactions (PPIs) can be very important for the understanding of biological cellular functions. However, detecting PPIs in the laboratories are both time-consuming and expensive. For this reason, there has been much recent effort to develop techniques for computational prediction of PPIs as this can complement laboratory procedures and provide an...

WBSMDA: Within and Between Score for MiRNA-Disease Association prediction

ScholarSearch for Xu Zhang in:Nature Research journals • PubMed • Google ScholarSearch for Zhu-Hong You in:Nature Research journals • PubMed • Google ScholarSearch for Lixi Deng in:Nature Research journals

Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set

Background Identifying protein-protein interactions (PPIs) is essential for elucidating protein functions and understanding the molecular mechanisms inside the cell. However, the experimental methods for detecting PPIs are both time-consuming and expensive. Therefore, computational prediction of protein interactions are becoming increasingly popular, which can provide an...

t-LSE: A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks

Protein-protein interaction (PPI) networks provide insights into understanding of biological processes, function and the underlying complex evolutionary mechanisms of the cell. Modeling PPI network is an important and fundamental problem in system biology, where it is still of major concern to find a better fitting model that requires less structural assumptions and is more...

Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis

Background Protein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Although large amount of PPIs data for different species has been generated by high-throughput experimental techniques, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks...

Assessing and predicting protein interactions by combining manifold embedding with multiple information integration

Background Protein-protein interactions (PPIs) play crucial roles in virtually every aspect of cellular function within an organism. Over the last decade, the development of novel high-throughput techniques has resulted in enormous amounts of data and provided valuable resources for studying protein interactions. However, these high-throughput protein interaction data are often...

Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data

Motivation: High-throughput protein interaction data, with ever-increasing volume, are becoming the foundation of many biological discoveries, and thus high-quality protein–protein interaction (PPI) maps are critical for a deeper understanding of cellular processes. However, the unreliability and paucity of current available PPI data are key obstacles to the subsequent...

A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

Background Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level...