Adaptive multi-source multi-view latent feature learning for inferring potential disease-associated miRNAs

2020 ◽  
Author(s):  
Qiu Xiao ◽  
Ning Zhang ◽  
Jiawei Luo ◽  
Jianhua Dai ◽  
Xiwei Tang
Keyword(s):  
Computational Models ◽  
Geometric Characteristic ◽  
Feature Learning ◽  
Cost Effective ◽  
Disease Diagnosis ◽  
Computational Method ◽  
Data Sources ◽  
Superior Performance ◽  
Graph Regularization ◽  
Latent Features

Abstract Accumulating evidence has shown that microRNAs (miRNAs) play crucial roles in different biological processes, and their mutations and dysregulations have been proved to contribute to tumorigenesis. In silico identification of disease-associated miRNAs is a cost-effective strategy to discover those most promising biomarkers for disease diagnosis and treatment. The increasing available omics data sources provide unprecedented opportunities to decipher the underlying relationships between miRNAs and diseases by computational models. However, most existing methods are biased towards a single representation of miRNAs or diseases and are also not capable of discovering unobserved associations for new miRNAs or diseases without association information. In this study, we present a novel computational method with adaptive multi-source multi-view latent feature learning (M2LFL) to infer potential disease-associated miRNAs. First, we adopt multiple data sources to obtain similarity profiles and capture different latent features according to the geometric characteristic of miRNA and disease spaces. Then, the multi-modal latent features are projected to a common subspace to discover unobserved miRNA-disease associations in both miRNA and disease views, and an adaptive joint graph regularization term is developed to preserve the intrinsic manifold structures of multiple similarity profiles. Meanwhile, the Lp,q-norms are imposed into the projection matrices to ensure the sparsity and improve interpretability. The experimental results confirm the superior performance of our proposed method in screening reliable candidate disease miRNAs, which suggests that M2LFL could be an efficient tool to discover diagnostic biomarkers for guiding laborious clinical trials.

scholarly journals Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Han-Jing Jiang ◽  
Yu-An Huang ◽  
Zhu-Hong You
Keyword(s):  
Case Studies ◽  
Computational Models ◽  
Cross Validation ◽  
Drug Repositioning ◽  
Feature Learning ◽  
Superior Performance ◽  
Reliable Model ◽  
Disease Associations ◽  
The Cost ◽  
Fold Cross Validation

Computational drug repositioning, designed to identify new indications for existing drugs, significantly reduced the cost and time involved in drug development. Prediction of drug-disease associations is promising for drug repositioning. Recent years have witnessed an increasing number of machine learning-based methods for calculating drug repositioning. In this paper, a novel feature learning method based on Gaussian interaction profile kernel and autoencoder (GIPAE) is proposed for drug-disease association. In order to further reduce the computation cost, both batch normalization layer and the full-connected layer are introduced to reduce training complexity. The experimental results of 10-fold cross validation indicate that the proposed method achieves superior performance on Fdataset and Cdataset with the AUCs of 93.30% and 96.03%, respectively, which were higher than many previous computational models. To further assess the accuracy of GIPAE, we conducted case studies on two complex human diseases. The top 20 drugs predicted, 14 obesity-related drugs, and 11 drugs related to Alzheimer's disease were validated in the CTD database. The results of cross validation and case studies indicated that GIPAE is a reliable model for predicting drug-disease associations.

scholarly journals Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy

2021 ◽  
pp. 1-5
Author(s):  
Marco Angrisani ◽  
Anya Samek ◽  
Arie Kapteyn
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Cost Effective ◽  
Research Data ◽  
Data Sources ◽  
Special Issue ◽  
Administrative Records ◽  
The Past ◽  
Survey Questionnaires ◽  
Internet Panels

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.

scholarly journals Multi-source hydrological soil moisture state estimation using data fusion optimisation

2017 ◽  
Vol 21 (7) ◽  
pp. 3267-3285 ◽  
Author(s):  
Lu Zhuo ◽  
Dawei Han
Keyword(s):  
Soil Moisture ◽  
Data Fusion ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Local Linear Regression ◽  
Data Sources ◽  
Superior Performance ◽  
Flood Prediction ◽  
Time To Build ◽  
Moisture State

Abstract. Reliable estimation of hydrological soil moisture state is of critical importance in operational hydrology to improve the flood prediction and hydrological cycle description. Although there have been a number of soil moisture products, they cannot be directly used in hydrological modelling. This paper attempts for the first time to build a soil moisture product directly applicable to hydrology using multiple data sources retrieved from SAC-SMA (soil moisture), MODIS (land surface temperature), and SMOS (multi-angle brightness temperatures in H–V polarisations). The simple yet effective local linear regression model is applied for the data fusion purpose in the Pontiac catchment. Four schemes according to temporal availabilities of the data sources are developed, which are pre-assessed and best selected by using the well-proven feature selection algorithm gamma test. The hydrological accuracy of the produced soil moisture data is evaluated against the Xinanjiang hydrological model's soil moisture deficit simulation. The result shows that a superior performance is obtained from the scheme with the data inputs from all sources (NSE = 0.912, r = 0.960, RMSE = 0.007 m). Additionally, the final daily-available hydrological soil moisture product significantly increases the Nash–Sutcliffe efficiency by almost 50 % in comparison with the two most popular soil moisture products. The proposed method could be easily applied to other catchments and fields with high confidence. The misconception between the hydrological soil moisture state variable and the real-world soil moisture content, and the potential to build a global routine hydrological soil moisture product are discussed.

Computational Methods to Support Brittle Fracture Assessment of PSVs Involving Autorefrigeration

2015 ◽  
Author(s):  
Kannan Subramanian ◽  
Jorge Penso ◽  
Harbi Pordal
Keyword(s):  
Brittle Fracture ◽  
Computational Methods ◽  
Cost Effective ◽  
Liquid Hydrocarbon ◽  
Computational Method ◽  
Metal Temperature ◽  
Relief Valve ◽  
Sensitivity Studies ◽  
Fracture Assessment ◽  
Cfd Method

Pressure safety relief valve (PSV) operation generally leads to cooling of the valve itself and the piping connected to the PSV. The temperatures may reach values below the minimum design metal temperature (MDMT) of the valve, and therefore the valve needs to be assessed for brittle fracture susceptibility. Simplistic determination of the minimum metal temperature in the valve may disqualify these valves during the brittle fracture assessments (BFA). Replacement may be time consuming and may not be cost effective. In such circumstances, a sophisticated and more representative BFA approach involving the use of computational fluid dynamics (CFD) followed by finite element method (FEM) based stress analysis which may be further followed by fracture mechanics can be adopted based on the concepts defined in ASME/API 579. The accuracy of the BFA depends on the accuracy of each of the computational method involved in the assessment. Among all the computational methods, CFD poses significant challenge. The low temperature may have been caused due to Joule-Thompson effect or auto-refrigeration. While Joule-Thompson effect can be best captured with easy to implement and robust CFD methods, auto-refrigeration involving adiabatic flashing which causes additional complexity and requires multiple sensitivity studies performed to determine the accuracy of the CFD approach. In this paper, an overview of the computational methods used in the brittle fracture assessment of PSVs is presented. Specific CFD method details are provided for PSV involving the flashing of liquid hydrocarbon to vapor is presented in the form of a case study derived from downstream industry application.

Journey Toward Process Convergence in TSV Technology

2010 ◽  
Vol 2010 (DPC) ◽  
pp. 001282-001321
Author(s):  
Sesh Ramaswami ◽  
John Dukovic
Keyword(s):  
Process Integration ◽  
Semiconductor Industry ◽  
Rapid Development ◽  
Chemical Mechanical Planarization ◽  
Cost Effective ◽  
Superior Performance ◽  
Wafer Level ◽  
Unit Process ◽  
Integration Schemes ◽  
Process Convergence

Continuous demand for more advanced electronic devices with higher functionality and superior performance in smaller packages is driving the semiconductor industry to develop new and more advanced 3D wafer-level interconnect technologies involving TSVs (through-silicon vias). The TSVs are created either on full-thickness wafer from the wafer front-side ¡V as part of wafer-fab processing during Middle-Of-Line (¡§via middle¡¨) or Back-End-Of-Line (¡§via last BEOL¡¨) ¡V or from the wafer backside after wafer thinning (¡§via last backside¡¨). Independent of the specific approach, the main steps include via etching, lining with insulator, copper barrier/seed deposition, via fill, and chemical mechanical planarization (CMP). Over the past year, the industry has been converging toward some primary unit processes and integration schemes for creating the TSVs. A common cost-of-ownership framework has also begun to emerge. Active collaboration underway among equipment suppliers, materials providers and end users is bringing about rapid development and validation of cost-effective TSV technology in end products. This presentation will address unit-process and integration challenges of TSV fabrication in the context of 20x100ƒÝm and 5x50ƒÝm baseline process flows at Applied Materials. Highlights of wafer-backside process integration involving wafers bonded to silicon or glass carriers will also be discussed.

scholarly journals Quality of Experience in Cyber-Physical Social Systems Based on Reinforcement Learning and Game Theory

2018 ◽  
Vol 10 (11) ◽  
pp. 108 ◽  
Author(s):  
Eirini Tsiropoulou ◽  
George Kousis ◽  
Athina Thanou ◽  
Ioanna Lykourentzou ◽  
Symeon Papavassiliou
Keyword(s):  
Game Theory ◽  
Reinforcement Learning ◽  
Time Management ◽  
Quality Of Experience ◽  
Social Systems ◽  
Cost Effective ◽  
Superior Performance ◽  
Operational Effectiveness ◽  
Effective Manner

This paper addresses the problem of museum visitors’ Quality of Experience (QoE) optimization by viewing and treating the museum environment as a cyber-physical social system. To achieve this goal, we harness visitors’ internal ability to intelligently sense their environment and make choices that improve their QoE in terms of which the museum touring option is the best for them and how much time to spend on their visit. We model the museum setting as a distributed non-cooperative game where visitors selfishly maximize their own QoE. In this setting, we formulate the problem of Recommendation Selection and Visiting Time Management (RSVTM) and propose a two-stage distributed algorithm based on game theory and reinforcement learning, which learns from visitor behavior to make on-the-fly recommendation selections that maximize visitor QoE. The proposed framework enables autonomic visitor-centric management in a personalized manner and enables visitors themselves to decide on the best visiting strategies. Experimental results evaluating the performance of the proposed RSVTM algorithm under realistic simulation conditions indicate the high operational effectiveness and superior performance when compared to other recommendation approaches. Our results constitute a practical alternative for museums and exhibition spaces meant to enhance visitor QoE in a flexible, efficient, and cost-effective manner.

scholarly journals Electrospinning Ag-TiO2 Nanorod-Loaded Air Treatment Filters and Their Applications in Air Purification

Molecules ◽  
2020 ◽  
Vol 25 (15) ◽  
pp. 3369
Author(s):  
Shan-Jiang Wang ◽  
Xiao-Yang Zhang ◽  
Dan Su ◽  
Yun-Fan Wang ◽  
Chun-Meng Qian ◽  
...  
Keyword(s):  
Organic Pollutants ◽  
High Efficiency ◽  
Synergistic Effects ◽  
Cost Effective ◽  
Electrospinning Process ◽  
Advanced Materials ◽  
Superior Performance ◽  
Efficient Treatment ◽  
Air Treatment ◽  
Tio2 Nanorod

The efficient treatment of the problem of air pollution is a practical issue related to human health. The development of multi-functional air treatment filters, which can remove various kinds of pollutants, including particulate matter (PM) and organic gases, is a tireless pursuit aiming to address the actual needs of humans. Advanced materials and nano-manufacturing technology have brought about the opportunity to change conventional air filters for practical demands, with the aim of achieving the high-efficiency utilization of photons, a strong catalytic ability, and the synergetic degradation of multi-pollutants. In this work, visible-responding photocatalytic air treatment filters were prepared and combined with a fast and cost-effective electrospinning process. Firstly, we synthesized Ag-loaded TiO2 nanorod composites with a controlled size and number of loaded Ag nanoparticles. Then, multi-functional air treatment filters were designed by loading catalysts on electrospinning nanofibers combined with a programmable brush. We found that such Ag-TiO2 nanorod composite-loaded nanofibers displayed prominent PM filtration (~90%) and the degradation of organic pollutants (above 90%). The superior performance of purification could be demonstrated in two aspects. One was the improvement of the adsorption of pollutants derived from the increase of the specific surface area after the loading of catalysts, and the other was the plasmonic hot carriers, which induced a broadening of the optical absorption in the visible light range, meaning that many more photons were utilized effectively. The designed air treatment filters with synergistic effects for eliminating both PM and organic pollutants have promising potential for the future design and application of novel air treatment devices.

Nanomodified Endotracheal Tubes: Spatial Analysis of Reduced Bacterial Colonization in a Bench Top Airway Model

2012 ◽  
Vol 1418 ◽  
Author(s):  
Mary C. Machado ◽  
Keiko M. Tarquinio ◽  
Thomas J. Webster
Keyword(s):  
Computational Models ◽  
Bacterial Colonization ◽  
Air Flow ◽  
Cost Effective ◽  
Clinical Problem ◽  
High Morbidity ◽  
The Novel ◽  
Endotracheal Tubes ◽  
Surface Features ◽  
Log Reduction

ABSTRACTVentilator associated pneumonia (VAP) is a serious and costly clinical problem. Specifically, receiving mechanical ventilation for over 24 hours increases the risk of VAP and is associated with high morbidity, mortality and medical costs. Cost effective endotracheal tubes (ETTs) that are resistant to bacterial infection could help prevent this problem. The objective of this study was to determine differences in the growth ofStaphylococcus aureus(S. aureus) on nanomodified and unmodified polyvinyl chloride (PVC) ETTs under dynamic airway conditions. PVC ETTs were modified to have nanometer surface features by soaking them inRhizopus arrhisus,a fungal lipase. Twenty-four hour experiments (supported by computational models) showed that air flow conditions within the ETT influenced both the location and concentration of bacterial growth on the ETTs especially within areas of tube curvature. More importantly, experiments revealed a 1.5 log reduction in the total number ofS. aureuson the novel nanomodified ETTs compared to the conventional ETTs after 24 hours of air flow. This dynamic study showed that lipase etching can create nano-rough surface features on PVC ETTs that suppressS. aureusgrowth and, thus, may provide clinicians with an effective and inexpensive tool to combat VAP.

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