Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
6,760
result(s) for
"Fang, Xiang"
Sort by:
Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep Learning
Circular RNAs (circRNAs) are extensively expressed in cells and tissues, and play crucial roles in human diseases and biological processes. Recent studies have reported that circRNAs could function as RNA binding protein (RBP) sponges, meanwhile RBPs can also be involved in back-splicing. The interaction with RBPs is also considered an important factor for investigating the function of circRNAs. Hence, it is necessary to understand the interaction mechanisms of circRNAs and RBPs, especially in human cancers. Here, we present a novel method based on deep learning to identify cancer-specific circRNA–RBP binding sites (CSCRSites), only using the nucleotide sequences as the input. In CSCRSites, an architecture with multiple convolution layers is utilized to detect the features of the raw circRNA sequence fragments, and further identify the binding sites through a fully connected layer with the softmax output. The experimental results show that CSCRSites outperform the conventional machine learning classifiers and some representative deep learning methods on the benchmark data. In addition, the features learnt by CSCRSites are converted to sequence motifs, some of which can match to human known RNA motifs involved in human diseases, especially cancer. Therefore, as a deep learning-based tool, CSCRSites could significantly contribute to the function analysis of cancer-associated circRNAs.
Journal Article
اتجاه حركة الشبيبة
هذا الكتاب الذي بعنوان \"اتجاه حركة الشبيبة\" الحديث الذي ألقاه الرفيق ماو تسي تونغ في الرابع من أيار (مايو) 1939 في يينآن، في حشد الجماهير الشبيبة على شرف الذكرى العشرين لحركة الرابع من أيار (مايو)، وقد طور الرفيق ماوتسي تونغ في هذا الحديث عددا من المبادىء النظرية الخاصة بالثورة في الصين.
Book
DWNN-RLS: regularized least squares method for predicting circRNA-disease associations
Background
Many evidences have demonstrated that circRNAs (circular RNA) play important roles in controlling gene expression of human, mouse and nematode. More importantly, circRNAs are also involved in many diseases through fine tuning of post-transcriptional gene expression by sequestering the miRNAs which associate with diseases. Therefore, identifying the circRNA-disease associations is very appealing to comprehensively understand the mechanism, treatment and diagnose of diseases, yet challenging. As the complex mechanism between circRNAs and diseases, wet-lab experiments are expensive and time-consuming to discover novel circRNA-disease associations. Therefore, it is of dire need to employ the computational methods to discover novel circRNA-disease associations.
Result
In this study, we develop a method (DWNN-RLS) to predict circRNA-disease associations based on Regularized Least Squares of Kronecker product kernel. The similarity of circRNAs is computed from the Gaussian Interaction Profile(GIP) based on known circRNA-disease associations. In addition, the similarity of diseases is integrated by the mean of GIP similarity and sematic similarity which is computed by the direct acyclic graph (DAG) representation of diseases. The kernels of circRNA-disease pairs are constructed from the Kronecker product of the kernels of circRNAs and diseases. DWNN (decreasing weight k-nearest neighbor) method is adopted to calculate the initial relational score for new circRNAs and diseases. The Kronecker product kernel based regularised least squares approach is used to predict new circRNA-disease associations. We adopt 5-fold cross validation (5CV), 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) to assess the prediction performance of our method, and compare it with other six competing methods (RLS-avg, RLS-Kron, NetLapRLS, KATZ, NBI, WP).
Conlusion
The experiment results show that DWNN-RLS reaches the AUC values of 0.8854, 0.9205 and 0.9701 in 5CV, 10CV and LOOCV, respectively, which illustrates that DWNN-RLS is superior to the competing methods RLS-avg, RLS-Kron, NetLapRLS, KATZ, NBI, WP. In addition, case studies also show that DWNN-RLS is an effective method to predict new circRNA-disease associations.
Journal Article
DeepEP: a deep learning framework for identifying essential proteins
Background
Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics.
Results
We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins.
Conclusion
We demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods.
Journal Article
PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations
CircRNAs have particular biological structure and have proven to play important roles in diseases. It is time-consuming and costly to identify circRNA-disease associations by biological experiments. Therefore, it is appealing to develop computational methods for predicting circRNA-disease associations. In this study, we propose a new computational path weighted method for predicting circRNA-disease associations. Firstly, we calculate the functional similarity scores of diseases based on disease-related gene annotations and the semantic similarity scores of circRNAs based on circRNA-related gene ontology, respectively. To address missing similarity scores of diseases and circRNAs, we calculate the Gaussian Interaction Profile (GIP) kernel similarity scores for diseases and circRNAs, respectively, based on the circRNA-disease associations downloaded from circR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). Then, we integrate disease functional similarity scores and circRNA semantic similarity scores with their related GIP kernel similarity scores to construct a heterogeneous network made up of three sub-networks: disease similarity network, circRNA similarity network and circRNA-disease association network. Finally, we compute an association score for each circRNA-disease pair based on paths connecting them in the heterogeneous network to determine whether this circRNA-disease pair is associated. We adopt leave one out cross validation (LOOCV) and five-fold cross validations to evaluate the performance of our proposed method. In addition, three common diseases, Breast Cancer, Gastric Cancer and Colorectal Cancer, are used for case studies. Experimental results illustrate the reliability and usefulness of our computational method in terms of different validation measures, which indicates PWCDA can effectively predict potential circRNA-disease associations.
Journal Article
Twin-field quantum key distribution over 830-km fibre
Quantum key distribution (QKD) provides a promising solution for sharing information-theoretic secure keys between remote peers with physics-based protocols. According to the law of quantum physics, the photons carrying signals cannot be amplified or relayed via classical optical techniques to maintain quantum security. As a result, the transmission loss of the channel limits its achievable distance, and this has been a huge barrier towards building large-scale quantum-secure networks. Here we present an experimental QKD system that could tolerate a channel loss beyond 140 dB and obtain a secure distance of 833.8 km, setting a new record for fibre-based QKD. Furthermore, the optimized four-phase twin-field protocol and high-quality set-up make its secure key rate more than two orders of magnitude greater than previous records over similar distances. Our results mark a breakthrough towards building reliable and efficient terrestrial quantum-secure networks over a scale of 1,000 km.Twin-field (TF) quantum key distribution (QKD) over a secure distance of 833.8 km is demonstrated even in the finite-size regime. To this end, an optimized four-phase TF-QKD protocol and a high-speed low-noise TF-QKD system are developed.
Journal Article
Evolution of stomatal closure to optimize water-use efficiency in response to dehydration in ferns and seed plants
• Plants control water-use efficiency (WUE) by regulating water loss and CO₂ diffusion through stomata. Variation in stomatal control has been reported among lineages of vascular plants, thus giving rise to the possibility that different lineages may show distinct WUE dynamics in response to water stress.
• Here, we compared the response of gas exchange to decreasing leaf water potential among four ferns and nine seed plant species exposed to a gradually intensifying water deficit. The data collected were combined with those from 339 phylogenetically diverse species obtained from previous studies.
• In well-watered angiosperms, the maximum stomatal conductance was high and greater than that required for maximum WUE, but drought stress caused a rapid reduction in stomatal conductance and an increase in WUE in response to elevated concentrations of abscisic acid. However, in ferns, stomata did not open beyond the optimum point corresponding to maximum WUE and actually exhibited a steady WUE in response to dehydration. Thus, seed plants showed improved photosynthetic WUE under water stress.
• The ability of seed plants to increase WUE could provide them with an advantage over ferns under drought conditions, thereby presumably increasing their fitness under selection pressure by drought.
Journal Article
Combined high leaf hydraulic safety and efficiency provides drought tolerance in Caragana species adapted to low mean annual precipitation
• Clarifying the coordination of leaf hydraulic traits with gas exchange across closely-related species adapted to varying rainfall can provide insights into plant habitat distribution and drought adaptation.
• The leaf hydraulic conductance (K
leaf), stomatal conductance (g
s), net assimilation (A), vein embolism and abscisic acid (ABA) concentration during dehydration were quantified, as well as pressure–volume curve traits and vein anatomy in 10 Caragana species adapted to a range of mean annual precipitation (MAP) conditions and growing in a common garden.
• We found a positive correlation between Ψleaf at 50% loss of K
leaf (K
leaf
P
50) and maximum K
leaf (K
leaf-max) across species. Species from low-MAP environments exhibited more negative K
leaf
P
50 and turgor loss point, and higher K
leaf-max and leaf-specific capacity at full turgor, along with higher vein density and midrib xylem per leaf area, and a higher ratio of K
leaf-max : maximum g
s. Tighter stomatal control mediated by higher ABA accumulation during dehydration in these species resulted in an increase in hydraulic safety and intrinsic water use efficiency (WUEi) during drought.
• Our results suggest that high hydraulic safety and efficiency combined with greater stomatal sensitivity triggered by ABA production and leading to greater WUEi provides drought tolerance in Caragana species adapted to low-MAP environments.
Journal Article
Overlap matrix completion for predicting drug-associated indications
Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated indications, respectively. OMC is able to efficiently exploit the underlying low-rank structures of the drug-disease association matrices. In OMC2, first of all, we construct one bilayer network from drug-side aspect and one from disease-side aspect, and then obtain their corresponding block adjacency matrices. We then propose the OMC2 algorithm to fill out the values of the missing entries in these two adjacency matrices, and predict the scores of unknown drug-disease pairs. Moreover, we further extend OMC2 to OMC3 to handle tri-layer networks. Computational experiments on various datasets indicate that our OMC methods can effectively predict the potential drug-disease associations. Compared with the other state-of-the-art approaches, our methods yield higher prediction accuracy in 10-fold cross-validation and de novo experiments. In addition, case studies also confirm the effectiveness of our methods in identifying promising indications for existing drugs in practical applications.
Journal Article