In recent years, increasing associations between microRNAs (miRNAs) and human diseases have been identified. Based on accumulating biological data, many computational models for potential ...miRNA-disease associations inference have been developed, which saves time and expenditure on experimental studies, making great contributions to researching molecular mechanism of human diseases and developing new drugs for disease treatment. In this paper, we proposed a novel computational method named Ensemble of Decision Tree based MiRNA-Disease Association prediction (EDTMDA), which innovatively built a computational framework integrating ensemble learning and dimensionality reduction. For each miRNA-disease pair, the feature vector was extracted by calculating the statistical measures, graph theoretical measures, and matrix factorization results for the miRNA and disease, respectively. Then multiple base learnings were built to yield many decision trees (DTs) based on random selection of negative samples and miRNA/disease features. Particularly, Principal Components Analysis was applied to each base learning to reduce feature dimensionality and hence remove the noise or redundancy. Average strategy was adopted for these DTs to get final association scores between miRNAs and diseases. In model performance evaluation, EDTMDA showed AUC of 0.9309 in global leave-one-out cross validation (LOOCV) and AUC of 0.8524 in local LOOCV. Additionally, AUC of 0.9192+/-0.0009 in 5-fold cross validation proved the model's reliability and stability. Furthermore, three types of case studies for four human diseases were implemented. As a result, 94% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 96% (Breast Neoplasms) and 88% (Carcinoma Hepatocellular) of top 50 predicted miRNAs were confirmed by experimental evidences in literature.
Abstract
MicroRNAs (miRNAs) play crucial roles in human disease and can be targeted by small molecule (SM) drugs according to numerous studies, which shows that identifying SM–miRNA associations in ...human disease is important for drug development and disease treatment. We proposed the method of Ensemble of Kernel Ridge Regression-based Small Molecule–MiRNA Association prediction (EKRRSMMA) to uncover potential SM–miRNA associations by combing feature dimensionality reduction and ensemble learning. First, we constructed different feature subsets for both SMs and miRNAs. Then, we trained homogeneous base learners based on distinct feature subsets and took the average of scores obtained from these base learners as SM–miRNA association score. In EKRRSMMA, feature dimensionality reduction technology was employed in the process of construction of feature subsets to reduce the influence of noisy data. Besides, the base learner, namely KRR_avg, was the combination of two classifiers constructed under SM space and miRNA space, which could make full use of the information of SM and miRNA. To assess the prediction performance of EKRRSMMA, we conducted Leave-One-Out Cross-Validation (LOOCV), SM-fixed local LOOCV, miRNA-fixed local LOOCV and 5-fold CV based on two datasets. For Dataset 1 (Dataset 2), EKRRSMMA got the Area Under receiver operating characteristic Curves (AUCs) of 0.9793 (0.8871), 0.8071 (0.7705), 0.9732 (0.8586) and 0.9767 ± 0.0014 (0.8560 ± 0.0027). Besides, we conducted four case studies. As a result, 32 (5-Fluorouracil), 19 (17β-Estradiol), 26 (5-Aza-2′-deoxycytidine) and 11 (cyclophosphamide) out of top 50 predicted potentially associated miRNAs were confirmed by database or experimental literature. Above evaluation results demonstrated that EKRRSMMA is reliable for predicting SM–miRNA associations.
Abstract
MicroRNA (miRNA) plays an important role in the occurrence, development, diagnosis and treatment of diseases. More and more researchers begin to pay attention to the relationship between ...miRNA and disease. Compared with traditional biological experiments, computational method of integrating heterogeneous biological data to predict potential associations can effectively save time and cost. Considering the limitations of the previous computational models, we developed the model of deep-belief network for miRNA-disease association prediction (DBNMDA). We constructed feature vectors to pre-train restricted Boltzmann machines for all miRNA-disease pairs and applied positive samples and the same number of selected negative samples to fine-tune DBN to obtain the final predicted scores. Compared with the previous supervised models that only use pairs with known label for training, DBNMDA innovatively utilizes the information of all miRNA-disease pairs during the pre-training process. This step could reduce the impact of too few known associations on prediction accuracy to some extent. DBNMDA achieves the AUC of 0.9104 based on global leave-one-out cross validation (LOOCV), the AUC of 0.8232 based on local LOOCV and the average AUC of 0.9048 ± 0.0026 based on 5-fold cross validation. These AUCs are better than other previous models. In addition, three different types of case studies for three diseases were implemented to demonstrate the accuracy of DBNMDA. As a result, 84% (breast neoplasms), 100% (lung neoplasms) and 88% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by recent literature. Therefore, we could conclude that DBNMDA is an effective method to predict potential miRNA-disease associations.
A new electrochemical three-component annulation-iodosulfonylation of 1,5-enyne-containing
-quinone methides (
-QMs) has been established by using available arylsulfonyl hydrazides and potassium ...iodide under environmentally benign conditions. The electrosynthesis offers sustainable and efficient access to construct spirocyclohexadienone-containing (
)-indenes without any additional catalyst or oxidant through a sulfonyl-radical-triggered 1,6-addition and an I
-mediated
-cyclization cascade. Notably, potassium iodide plays the triple role of an electrolyte, a redox catalyst, as well as an iodination reagent.
Surface electromyogram (sEMG) signals can be applied in medical, rehabilitation, robotic, and industrial fields. As a typical application, a myoelectric prosthetic hand is controlled by the sEMG ...signals of the amputee's residual muscles. To improve the dexterity of the myoelectric prosthetic hand, additional hand motion commands need to be classified. The more sEMG sensors are used, the more hand motion commands can be classified. However, the amputee's residual muscles are limited. In order to improve the practicability of the myoelectric prosthetic hand, it is critical to investigate the effective pattern recognition algorithms to deal with the sEMG signals detected by fewer sensors, while identifying as many hand motion commands as possible. Current pattern recognition algorithms for sEMG signals are challenged by limited recognition patterns and unsteady classification accuracy rates. To solve these dilemmas, we employed discrete wavelet transform (DWT) and wavelet neural network (WNN) algorithms to improve the pattern recognition effects of sEMG signals. In addition, the back propagation and gradient descent algorithms were utilized to train WNN. In this work, we only used three sEMG sensors to classify and recognize six kinds of hand motion commands. The maximum identification accuracy rate is 100%, and an average classification accuracy rate of the proposed WNN is 94.67%, which is substantially better than the artificial neural network (ANN) algorithm.
In recent years, increasing microRNA (miRNA)-disease associations were identified through traditionally biological experiments. These associations contribute to revealing molecular mechanism of ...diseases and preventing and curing diseases. To improve the efficiency of miRNA-disease association discovery, some calculation methods were developed as auxiliary tools for researchers. In the current study, we raised a novel model named Bayesian Ranking for MiRNA-Disease Association prediction (BRMDA) by improving Bayesian Personalized Ranking from three aspects: (i) taking advantage of similarity of diseases and miRNAs; (ii) incorporating miRNA bias for miRNAs associated with different number of diseases; and (iii) implementing neighborhood-based approach for new miRNAs and diseases. For each investigated disease, BRMDA used the set of triples (i.e. disease, labeled miRNA, unlabeled miRNA) that reflected association preference of the disease to miRNAs as training set, which made full use of unknown samples rather than simply considering them as negative samples. To investigate the predictive performance of BRMDA, we employed leave-one-out cross-validation and obtained Area Under the Curve of 0.8697, which outperformed many classical methods. Besides, we further implemented three distinct classes of case studies for three common Neoplasms. As a result, there are 44 (Colon Neoplasms), 49 (Esophageal Neoplasms) and 49 (Lung Neoplasms) among the top 50 predicted miRNAs validated through experiments. In short, BRMDA would be a trustable tool for inferring valuable associations.
We propose a ratiometric electrochemical assay for detecting microRNA (miRNA) on the basis of dual-amplification mechanism by using distinguishable electrochemical signals from thionine (Thi) and ...ferrocene (Fc). The thiol-modified and ferrocene-labeled hairpin capture probes (CP) are first immobilized on an Au electrode via Au-S reaction. The target miRNA hybridizes with CP and unfolding the hairpin structure of CP to form miRNA-DNA duplexes. Then, kamchatka crab duplex specific nuclease (DSN) specifically cleaves the DNA in miRNA-DNA duplexes, leading to the release of miRNA and another cleaves cycle, meanwhile, numerous Fc leaves away from the electrode surface and leads to the signal-off of Fc. The residual fragment on electrode surface acts as a HCR primer to form dsDNA polymers through in situ HCR with the presence of the primer and two probes (HDNA and HDNA’), resulting in the capture of numerous DNA/Au NPs/Thi and the signal-on of Thi. The dual-amplification mechanism significantly amplifies the decrease of Fc signal and the increase of Thi signal for ratiometric readout (IThi/IFc), thus providing a sensitive method for the selective detection of miR-141 with a detection limit down to 11aM. The dual-signal ratiometric outputs have an intrinsic self-calibration to the effects from system, which is promising to be applied in biosensing and clinical diagnosis.
•The miRNA-initiated cleavage of DNA by DSN was introduced for a signal-off of Fc.•HCR and the nanoprobes can masterly amplify the signal-on of Thi.•The dual-amplification mechanism further amplified the ratiometric readout of IThi/IFc.•This ratiometric miRNA nanosensor was constructed based on DSN, HCR and nanoprobes.
Two pairs of new enantiomeric hydroxyphenylacetic acid derivatives, (±)‐corylophenols A and B ((±)‐1 and (±)‐2), a new α‐pyrone analogue, corylopyrone A (3), and six andrastin‐type meroterpenoids ...(4–9) were isolated and identified from the deep‐sea cold‐seep sediment‐derived fungus Penicillium corylophilum CS‐682. Their structures and stereo configurations were determined by detailed spectroscopic analysis of NMR and MS data, chiral HPLC analysis, J‐based configuration analysis, and quantum chemical calculations of ECD, specific rotation, and NMR (with DP4+ probability analysis). Compound 3 showed inhibitory activity against some strains of pathogenic bacteria.
A novel radical‐induced annulation/1,8‐halosulfonylation of β‐alkynyl ketones with haloaryl diazonium tetrafluoroborates and DABCO.bis(sulfur dioxide) was first achieved via the ...cleavage/recombination of C(sp3)−C(sp3) and C(sp2)‐halogen bonds, from which 47 examples of sulfone‐containing 1,3‐dimethylene‐substituted (Z,Z)‐isobenzofurans as single stereoisomers were synthesized in generally good yields. This multicomponent pathway is proposed to proceed through the in‐situ generation of arylsulfonyl radicals, followed by selective radical addition‐cyclization and ring‐opening of the cyclopropyl unit as well as C(sp2)‐halogen bond cleavage, resulting in the consecutive construction of three new chemical bonds, including C−S, C−O and C‐halogen bonds.
Amyloid-beta (Aβ) plays a pivotal role in the pathogenesis of Alzheimer’s disease (AD). The physiological capacity of peripheral tissues and organs in clearing brain-derived Aβ and its therapeutic ...potential for AD remains largely unknown. Here, we measured blood Aβ levels in different locations of the circulation in humans and mice, and used a parabiosis model to investigate the effect of peripheral Aβ catabolism on AD pathogenesis. We found that blood Aβ levels in the inferior/posterior vena cava were lower than that in the superior vena cava in both humans and mice. In addition, injected
125
I labeled Aβ40 was located mostly in the liver, kidney, gastrointestinal tract, and skin but very little in the brain; suggesting that Aβ derived from the brain can be cleared in the periphery. Parabiosis before and after Aβ deposition in the brain significantly reduced brain Aβ burden without alterations in the expression of amyloid precursor protein, Aβ generating and degrading enzymes, Aβ transport receptors, and AD-type pathologies including hyperphosphorylated tau, neuroinflammation, as well as neuronal degeneration and loss in the brains of parabiotic AD mice. Our study revealed that the peripheral system is potent in clearing brain Aβ and preventing AD pathogenesis. The present work suggests that peripheral Aβ clearance is a valid therapeutic approach for AD, and implies that deficits in the Aβ clearance in the periphery might also contribute to AD pathogenesis.
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