5,527 results match your criteria learn heterogeneous

DeepR2cov: deep representation learning on heterogeneous drug networks to discover anti-inflammatory agents for COVID-19.

Brief Bioinform 2021 Jun 11. Epub 2021 Jun 11.

College of Computer Science and Electronic Engineering, Hunan University, China.

Recent studies have demonstrated that the excessive inflammatory response is an important factor of death in coronavirus disease 2019 (COVID-19) patients. In this study, we propose a deep representation on heterogeneous drug networks, termed DeepR2cov, to discover potential agents for treating the excessive inflammatory response in COVID-19 patients. This work explores the multi-hub characteristic of a heterogeneous drug network integrating eight unique networks. Read More

View Article and Full-Text PDF

IMCHGAN: Inductive Matrix Completion with Heterogeneous Graph Attention Networks for Drug-Target Interactions Prediction.

IEEE/ACM Trans Comput Biol Bioinform 2021 Jun 11;PP. Epub 2021 Jun 11.

Computational approaches for prediction of drug-target interactions (DTIs) are highly desired in comparison to traditional biological experiments as its fast and low price. We present a novel Inductive Matrix Completion with Heterogeneous Graph Attention Network approach (IMCHGAN) for predicting DTIs. IMCHGAN first adopts a two-level neural attention mechanism approach to learn drug and target latent feature representations from the DTI heterogeneous network respectively. Read More

View Article and Full-Text PDF

Recent Trends in Multipotent Human Mesenchymal Stem/Stromal Cells: Learning from History and Advancing Clinical Applications.

Kevin Dzobo

OMICS 2021 Jun 25;25(6):342-357. Epub 2021 May 25.

International Center for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, Cape Town, South Africa.

Early cell biology reports demonstrated the presence of cells with stem-like properties in bone marrow, with both hematopoietic and mesenchymal lineages. Over the years, various investigations have purified and characterized mesenchymal stromal/stem cells (MSCs) from different human tissues as cells with multilineage differentiation potential under the appropriate conditions. Due to their appealing characteristics and versatile potentials, MSCs are leveraged in many applications in medicine such as oncology, bioprinting, and as recent as therapeutics discovery and innovation for COVID-19. Read More

View Article and Full-Text PDF

Estimating heterogeneous survival treatment effect in observational data using machine learning.

Stat Med 2021 Jun 10. Epub 2021 Jun 10.

Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.

Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with survival outcomes. Using flexible machine learning methods in the counterfactual framework is a promising approach to address challenges due to complex individual characteristics, to which treatments need to be tailored. To evaluate the operating characteristics of recent survival machine learning methods for the estimation of treatment effect heterogeneity and inform better practice, we carry out a comprehensive simulation study presenting a wide range of settings describing confounded heterogeneous survival treatment effects and varying degrees of covariate overlap. Read More

View Article and Full-Text PDF

Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records.

Multimodal Learn Clin Decis Support Clin Image Based Proc (2020) 2020 Oct 1;12445:13-23. Epub 2020 Oct 1.

Vanderbilt University, , Nashville, USA.

Type II diabetes mellitus (T2DM) is a significant public health concern with multiple known risk factors (, body mass index (BMI), body fat distribution, glucose levels). Improved prediction or prognosis would enable earlier intervention before possibly irreversible damage has occurred. Meanwhile, abdominal computed tomography (CT) is a relatively common imaging technique. Read More

View Article and Full-Text PDF
October 2020

Adolescent Vision Health During the Outbreak of COVID-19: Association Between Digital Screen Use and Myopia Progression.

Front Pediatr 2021 25;9:662984. Epub 2021 May 25.

The First Affiliated Hospital, Xian Jiaotong University, Xian, China.

The coronavirus (COVID-19) pandemic has impacted education systems globally, making digital devices common arrangements for adolescent learning. However, vision consequences of such behavioral changes are not well-understood. This study investigates the association between duration of daily digital screen engagement and myopic progression among 3,831 Chinese adolescents during the COVID-19 pandemic. Read More

View Article and Full-Text PDF

Developmental Language Disorder and Psychopathology: Disentangling Shared Genetic and Environmental Influences.

J Learn Disabil 2021 Jun 11:222194211019961. Epub 2021 Jun 11.

The University of New Mexico, Albuquerque, USA.

There is considerable variability in the extent to which young people with developmental language disorder (DLD) experience mental health difficulties. What drives these individual differences remains unclear. In the current article, data from the Twin Early Development Study were used to investigate the genetic and environmental influences on psychopathology in children and adolescents with DLD ( = 325) and those without DLD ( = 865). Read More

View Article and Full-Text PDF

Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects.

Brief Bioinform 2021 Jun 10. Epub 2021 Jun 10.

Data Science Institute, University of Technology Sydney, Ultimo, NSW 2007, Australia.

Single-cell sequencing is a biotechnology to sequence one layer of genomic information for individual cells in a tissue sample. For example, single-cell DNA sequencing is to sequence the DNA from every single cell. Increasing in complexity, single-cell multi-omics sequencing, or single-cell multimodal omics sequencing, is to profile in parallel multiple layers of omics information from a single cell. Read More

View Article and Full-Text PDF

Quantifying Competitive Degradation Processes in Supported Nanocatalyst Systems.

Nano Lett 2021 Jun 10. Epub 2021 Jun 10.

University of Pennsylvania, Department of Materials Science and Engineering, Philadelphia, Pennsylvania 19104, United States.

The stability of supported metal nanoparticles determines the activity and lifetime of heterogeneous catalysts. Catalysts can destabilize through several thermodynamic and kinetic pathways, and the competition between these mechanisms complicates efforts to quantify and predict the overall evolution of supported nanoparticles in reactive environments. Pairing transmission electron microscopy with unsupervised machine learning, we quantify the destabilization of hundreds of supported Au nanoparticles in real-time to develop a model describing the observed particle evolution as a competition between evaporation and surface diffusion. Read More

View Article and Full-Text PDF

Cross Modal Few-Shot Contextual Transfer for Heterogenous Image Classification.

Front Neurorobot 2021 24;15:654519. Epub 2021 May 24.

The School of Software Technology, Dalian University of Technology, Dalian, China.

Deep transfer learning aims at dealing with challenges in new tasks with insufficient samples. However, when it comes to few-shot learning scenarios, due to the low diversity of several known training samples, they are prone to be dominated by specificity, thus leading to one-sidedness local features instead of the reliable global feature of the actual categories they belong to. To alleviate the difficulty, we propose a cross-modal few-shot contextual transfer method that leverages the contextual information as a supplement and learns context awareness transfer in few-shot image classification scenes, which fully utilizes the information in heterogeneous data. Read More

View Article and Full-Text PDF

Maximum Margin Multi-Dimensional Classification.

IEEE Trans Neural Netw Learn Syst 2021 Jun 9;PP. Epub 2021 Jun 9.

Multi-dimensional classification (MDC) assumes heterogeneous class spaces for each example, where class variables from different class spaces characterize semantics of the example along different dimensions. The heterogeneity of class spaces leads to incomparability of the modeling outputs from different class spaces, which is the major difficulty in designing MDC approaches. In this article, we make a first attempt toward adapting maximum margin techniques for MDC problem and a novel approach named M³MDC is proposed. Read More

View Article and Full-Text PDF

[Quality management in a postgraduate refresher course in otolaryngology].

HNO 2021 Jun 9. Epub 2021 Jun 9.

Klinik für Allgemein‑, Viszeral- und Transplantationschirurgie, Medizinische Hochschule Hannover, Hannover, Deutschland.

Background: Residency training is often characterized by locally influenced training content and focus, which can lead to heterogeneous training outcomes. Refresher courses before the speciality certificate examinations can harmonize the situation.

Objective: The current publication aims to present a quality management system for evaluation of a postgraduate refresher course for otolaryngology residents. Read More

View Article and Full-Text PDF

Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering.

Sci Rep 2021 Jun 8;11(1):12109. Epub 2021 Jun 8.

Department of Intensive Care, Maastricht University Medical Centre+, University Maastricht, Maastricht, The Netherlands.

Critically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. Read More

View Article and Full-Text PDF

Whole-Genome Sequencing and Machine Learning Analysis of Staphylococcus aureus from Multiple Heterogeneous Sources in China Reveals Common Genetic Traits of Antimicrobial Resistance.

mSystems 2021 Jun 8:e0118520. Epub 2021 Jun 8.

School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, Leicestershire, United Kingdom.

Staphylococcus aureus is a worldwide leading cause of numerous diseases ranging from food-poisoning to lethal infections. Methicillin-resistant S. aureus (MRSA) has been found capable of acquiring resistance to most antimicrobials. Read More

View Article and Full-Text PDF

Accelerating Metadynamics-Based Free-Energy Calculations with Adaptive Machine Learning Potentials.

J Chem Theory Comput 2021 Jun 8. Epub 2021 Jun 8.

School of Chemistry and Chemical Engineering, Queen's University Belfast, Belfast BT9 5AG, U.K.

There is an increasing demand for free-energy calculations using ab initio molecular dynamics these days. Metadynamics (MetaD) is frequently utilized to reconstruct the free-energy surface, but it is often computationally intractable for the first-principles calculations. Machine learning potentials (MLPs) have become popular alternatives. Read More

View Article and Full-Text PDF

Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis.

Proc Natl Acad Sci U S A 2021 Jun;118(24)

Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel

Despite their great promise, artificial intelligence (AI) systems have yet to become ubiquitous in the daily practice of medicine largely due to several crucial unmet needs of healthcare practitioners. These include lack of explanations in clinically meaningful terms, handling the presence of unknown medical conditions, and transparency regarding the system's limitations, both in terms of statistical performance as well as recognizing situations for which the system's predictions are irrelevant. We articulate these unmet clinical needs as machine-learning (ML) problems and systematically address them with cutting-edge ML techniques. Read More

View Article and Full-Text PDF

A Distributed Lyapunov-Based Redesign Approach for Heterogeneous Uncertain Agents With Cooperation-Competition Interactions.

IEEE Trans Neural Netw Learn Syst 2021 Jun 7;PP. Epub 2021 Jun 7.

A swarming behavior problem is investigated in this article for heterogeneous uncertain agents with cooperation-competition interactions. In such a problem, the agents are described by second-order continuous systems with different intrinsic nonlinear terms, which satisfies the ``linearity-in-parameters'' condition, and the agents' models are coupled together through a distributed protocol containing the information of competitive neighbors. Then, for four different types of cooperation-competition networks, a distributed Lyapunov-based redesign approach is proposed for the heterogeneous uncertain agents, where the distributed controller and the estimation laws of unknown parameters are obtained. Read More

View Article and Full-Text PDF

Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease.

Front Big Data 2021 20;4:661110. Epub 2021 May 20.

Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.

Alzheimer's disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. Read More

View Article and Full-Text PDF

H2020 project CAPTOR dataset: Raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network.

Data Brief 2021 Jun 11;36:107127. Epub 2021 May 11.

Institute of Environmental Assessment and Water Research, Spanish National Research Council (IDAEA-CSIC), Barcelona, Spain.

The H2020 CAPTOR project deployed three testbeds in Spain, Italy and Austria with low-cost sensors for the measurement of tropospheric ozone (O). The aim of the H2020 CAPTOR project was to raise public awareness in a project focused on citizen science. Each testbed was supported by an NGO in charge of deciding how to raise citizen awareness according to the needs of each country. Read More

View Article and Full-Text PDF

MeImmS: Predict Clinical Benefit of Anti-PD-1/PD-L1 Treatments Based on DNA Methylation in Non-small Cell Lung Cancer.

Front Genet 2021 20;12:676449. Epub 2021 May 20.

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.

Immunotherapy has become an effective therapy for cancer treatment. However, the development of biomarkers to predict immunotherapy response still remains a challenge. We have developed the DNA Methylation Immune Score, named "MeImmS," which can predict clinical benefits of non-small cell lung cancer (NSCLC) patients based on DNA methylation of 8 CpG sites. Read More

View Article and Full-Text PDF

Utilizing the Heterogeneity of Clinical Data for Model Refinement and Rule Discovery Through the Application of Genetic Algorithms to Calibrate a High-Dimensional Agent-Based Model of Systemic Inflammation.

Front Physiol 2021 19;12:662845. Epub 2021 May 19.

Departmen of Surgery, Larner College of Medicine, The University of Vermont, Burlington, VT, United States.

Accounting for biological heterogeneity represents one of the greatest challenges in biomedical research. Dynamic computational and mathematical models can be used to enhance the study and understanding of biological systems, but traditional methods for calibration and validation commonly do not account for the heterogeneity of biological data, which may result in overfitting and brittleness of these models. Herein we propose a machine learning approach that utilizes genetic algorithms (GAs) to calibrate and refine an agent-based model (ABM) of acute systemic inflammation, with a focus on accounting for the heterogeneity seen in a clinical data set, thereby avoiding overfitting and increasing the robustness and potential generalizability of the underlying simulation model. Read More

View Article and Full-Text PDF

Relevance of primary lesion location, tumor heterogeneity and genetic mutation demonstrated through tumor growth inhibition and overall survival modeling in metastatic colorectal cancer.

Br J Clin Pharmacol 2021 Jun 4. Epub 2021 Jun 4.

Merck Institute for Pharmacometrics, Merck Serono S.A., Switzerland, an affiliate of Merck KGaA, Darmstadt, Germany.

Aims: The aims of this work were to build a semi-mechanistic tumor growth inhibition (TGI) model for metastatic colorectal cancer (mCRC) patients receiving either cetuximab+chemotherapy or chemotherapy alone and to identify early predictors of overall survival (OS).

Methods: A total of 1716 patients from four mCRC clinical studies were included in the analysis. The TGI model was built with 8973 tumor size measurements where the probability of drop-out was also included and modeled as a time-to-event variable using parametric survival models, as it was the case in the OS analysis. Read More

View Article and Full-Text PDF

Genetics of Asthma and Allergic Diseases.

Handb Exp Pharmacol 2021 Jun 4. Epub 2021 Jun 4.

National Heart and Lung Institute, Imperial College London, London, UK.

Asthma genes have been identified through a range of approaches, from candidate gene association studies and family-based genome-wide linkage analyses to genome-wide association studies (GWAS). The first GWAS of asthma, reported in 2007, identified multiple markers on chromosome 17q21 as associates of the childhood-onset asthma. This remains the best replicated asthma locus to date. Read More

View Article and Full-Text PDF

Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle.

Sci Rep 2021 Jun 3;11(1):11793. Epub 2021 Jun 3.

Department of Pharmacology and Therapeutics, University of Florida College of Medicine, Gainesville, 32610, FL, USA.

Skeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Read More

View Article and Full-Text PDF

Reduced frontal white matter microstructure in healthy older adults with low tactile recognition performance.

Sci Rep 2021 Jun 3;11(1):11689. Epub 2021 Jun 3.

Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany.

The aging of the nervous system is a heterogeneous process. It remains a significant challenge to identify relevant markers of pathological and healthy brain aging. A central aspect of aging are decreased sensory acuities, especially because they correlate with the decline in higher cognitive functioning. Read More

View Article and Full-Text PDF

Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson's disease patients.

J Neuroeng Rehabil 2021 Jun 3;18(1):93. Epub 2021 Jun 3.

Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

Background: To objectively assess a patient's gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing.

Method: To address this issue, we present a comprehensive free-living evaluation dataset, including 146. Read More

View Article and Full-Text PDF

Grouping Students by Skill Level in Mini-Volleyball: Effect on Game Performance and Knowledge in Sport Education.

Percept Mot Skills 2021 Jun 3:315125211021812. Epub 2021 Jun 3.

Facultad de Deporte, UCAM Universidad Católica San Antonio de Murcia, Spain.

The purpose of this study was to explore any differences in game performance variables and knowledge among a cohort of high school students who participated in either homogeneous or heterogeneous skill level groups (N = 126) across a 12-lesson mini-volleyball sport education unit of study. This study followed a mixed-methods approach using a quasi-experimental pre-test/post-test design. The quantitative variables analyzed were decision making, skill execution, game performance, game involvement, and game knowledge. Read More

View Article and Full-Text PDF

LUNAR Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network.

IEEE/ACM Trans Comput Biol Bioinform 2021 Jun 3;PP. Epub 2021 Jun 3.

An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Read More

View Article and Full-Text PDF

Adsorption Enthalpies for Catalysis Modeling through Machine-Learned Descriptors.

Acc Chem Res 2021 Jun 3. Epub 2021 Jun 3.

Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Lichtenbergstr. 4, 85747 Garching, Germany.

ConspectusHeterogeneous catalysts are rather complex materials that come in many classes (e.g., metals, oxides, carbides) and shapes. Read More

View Article and Full-Text PDF

Radar-to-Lidar: Heterogeneous Place Recognition Joint Learning.

Front Robot AI 2021 17;8:661199. Epub 2021 May 17.

Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou, China.

Place recognition is critical for both offline mapping and online localization. However, current single-sensor based place recognition still remains challenging in adverse conditions. In this paper, a heterogeneous measurement based framework is proposed for long-term place recognition, which retrieves the query radar scans from the existing lidar (Light Detection and Ranging) maps. Read More

View Article and Full-Text PDF