Abstract
Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of ...heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.
An efficient and selective Cu catalyst for CO2 electroreduction is highly desirable since current catalysts suffer from poor selectivity towards a series of products, such as alkenes, alcohols, and ...carboxylic acids. Here, we used copper(ii) paddle wheel cluster-based porphyrinic metal–organic framework (MOF) nanosheets for electrocatalytic CO2 reduction and compared them with CuO, Cu2O, Cu, a porphyrin–Cu(ii) complex and a CuO/complex composite. Among them, the cathodized Cu-MOF nanosheets exhibit significant activity for formate production with a faradaic efficiency (FE) of 68.4% at a potential of −1.55 V versus Ag/Ag+. Moreover, the C–C coupling product acetate is generated from the same catalyst together with formate at a wide voltage range of −1.40 V to −1.65 V with the total liquid product FE from 38.8% to 85.2%. High selectivity and activity are closely related to the cathodized restructuring of Cu-MOF nanosheets. With the combination of X-ray diffraction, X-ray photoelectron spectroscopy, high resolution transmission electron microscopy and Fourier transform infrared spectroscopy, we find that Cu(ii) carboxylate nodes possibly change to CuO, Cu2O and Cu4O3, which significantly catalyze CO2 to formate and acetate with synergistic enhancement from the porphyrin–Cu(ii) complex. This intriguing phenomenon provides a new opportunity for the rational design of high-performance Cu catalysts from pre-designed MOFs.
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.
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).
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.
Abstract
Drug repositioning can drastically decrease the cost and duration taken by traditional drug research and development while avoiding the occurrence of unforeseen adverse events. With the ...rapid advancement of high-throughput technologies and the explosion of various biological data and medical data, computational drug repositioning methods have been appealing and powerful techniques to systematically identify potential drug-target interactions and drug-disease interactions. In this review, we first summarize the available biomedical data and public databases related to drugs, diseases and targets. Then, we discuss existing drug repositioning approaches and group them based on their underlying computational models consisting of classical machine learning, network propagation, matrix factorization and completion, and deep learning based models. We also comprehensively analyze common standard data sets and evaluation metrics used in drug repositioning, and give a brief comparison of various prediction methods on the gold standard data sets. Finally, we conclude our review with a brief discussion on challenges in computational drug repositioning, which includes the problem of reducing the noise and incompleteness of biomedical data, the ensemble of various computation drug repositioning methods, the importance of designing reliable negative samples selection methods, new techniques dealing with the data sparseness problem, the construction of large-scale and comprehensive benchmark data sets and the analysis and explanation of the underlying mechanisms of predicted interactions.
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.
Ultrathin superhydrophobic–superoleophilic fibrous poly (vinylidene fluoride) (PVDF) membranes were prepared by means of electrospinning technique for the purpose of low-cost, high-efficiency ...water–oil separation. The ultrathin electrospun fibrous PVDF membranes exhibited a high water contact angle up to 153° and nearly zero oil (diesel) contact angle. The surface morphology and hydrophobicity of the membranes can be conveniently tuned by adjusting the PVDF concentration of the electrospinning solutions. The obtained ultrathin fibrous PVDF membranes showed excellent performance in separation of water-in-oil emulsions.
•Superhydrophobic–superoleophilic PVDF nanofibers were produced by electrospinning.•Surface morphology and structure of PVDF nanofibers were characterized by SEM.•Correlation of contact angle of PVDF nanofibers to fiber diameter was made.•High-efficiency of oil–water separation of PVDF nanofibers was demonstrated.
Commercial lithium-ion batteries (LIBs) have been widely used in various energy storage systems. However, many unfavorable factors of LIBs have prompted researchers to turn their attention to the ...development of emerging secondary batteries. Aqueous zinc ion batteries (AZIBs) present some prominent advantages with environmental friendliness, low cost and convenient operation feature. MnO2 electrode is the first to be discovered as promising cathode material. So far, manganese-based oxides have made significant progresses in improving the inherent capacity and energy density. Herein, we summarize comprehensively recent advances of Mn-based compounds as electrode materials for ZIBs. Especially, this review focuses on the design strategies of electrode structures, optimization of the electrochemical performance and the clarification of energy storage mechanisms. Finally, their future research directions and perspective are also proposed.
A plenty of work has focused on polymorphic Mn-based compounds due to their non-toxicity, low cost and rich crystal structure. In fact, the connection mode of MnO6 octahedrons determines MnO2 crystal structure, including α-, β-, γ-, λ-, R-, δ-, ε- and T-MnO2. These structures can be mutually transformed and seriously affect their electrochemical performance Display omitted .
Cancer-secreted exosomal miRNAs are emerging mediators of cancer-stromal cross-talk in the tumor environment. Our previous miRNAs array of cervical squamous cell carcinoma (CSCC) clinical specimens ...identified upregulation of miR-221-3p. Here, we show that miR-221-3p is closely correlated with peritumoral lymphangiogenesis and lymph node (LN) metastasis in CSCC. More importantly, miR-221-3p is characteristically enriched in and transferred by CSCC-secreted exosomes into human lymphatic endothelial cells (HLECs) to promote HLECs migration and tube formation in vitro, and facilitate lymphangiogenesis and LN metastasis in vivo according to both gain-of-function and loss-of-function experiments. Furthermore, we identify vasohibin-1 (VASH1) as a novel direct target of miR-221-3p through bioinformatic target prediction and luciferase reporter assay. Re-expression and knockdown of VASH1 could respectively rescue and simulate the effects induced by exosomal miR-221-3p. Importantly, the miR-221-3p-VASH1 axis activates the ERK/AKT pathway in HLECs independent of VEGF-C. Finally, circulating exosomal miR-221-3p levels also have biological function in promoting HLECs sprouting in vitro and are closely associated with tumor miR-221-3p expression, lymphatic VASH1 expression, lymphangiogenesis, and LN metastasis in CSCC patients. In conclusion, CSCC-secreted exosomal miR-221-3p transfers into HLECs to promote lymphangiogenesis and lymphatic metastasis via downregulation of VASH1 and may represent a novel diagnostic biomarker and therapeutic target for metastatic CSCC patients in early stages.
The novel theranostic nanosystems based on two‐photon fluorescence can achieve higher spatial resolution of deep tissue imaging for simultaneous diagnosis and therapy of a variety of cancers. Herein, ...we have designed and prepared FRET‐based two‐photon mesoporous silica nanoparticles (MTP–MSNs) for single‐excitation multiplexed intracellular imaging and targeted cancer therapy for the first time. This nanosystem includes two constituents, containing (1) multicolor two‐photon mesoporous silica nanoparticles and (2) cancer cell‐targeting aptamers that act as gatekeepers for MTP–MSNs. After incubation with cancer cells, the Dox‐loaded and aptamer‐capped MTP–MSNs could be internalized into the cells, opening the pores and releasing the drug. Furthermore, using two‐photon multicolor fluorescence, MTP–MSNs could serve as good contrast agents for multicolor two‐photon intracellular imaging with increased imaging depth and improved spatial localization of tissue. In sum, these multicolor MTP–MSNs provide a promising system for traceable targeted cancer therapy with further applications in multiplex intracellular imaging and the screening of drug.
Herein, we have designed and prepared FRET‐based two‐photon mesoporous silica nanoparticles (MTP–MSNs) for multiplexed intracellular imaging and targeted cancer therapy for the first time. After incubation with cancer cells, MTP–MSNs could release drugs and performed multicolor two‐photon cellular imaging. These MTP–MSNs provide a promising system for traceable targeted cancer therapy with further applications in multiplex intracellular imaging.
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.
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.
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.
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