8.473 hasil untuk machine learning
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Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning
…histology method, which is based on multiplexed Cell Magneto-Rotation and Machine Learning. We note that standard studies looking at cells immobilized on microscope slides cannot reveal their shape shifting, no more than pinned butterfly collections can reveal their flight patterns. Using cell…
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Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs
…chemical similarity metrics and physicochemical properties using a machine-learning approach. We computed pairwise similarities between 1410 drugs for training classification models and used the drugs shared protein targets as class labels. The best performing models were random forest which gave…
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Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies
Mobile sensing data processed using machine learning models can passively and remotely assess mental health symptoms from the context of patients’ lives. Prior work has trained models using data from single longitudinal studies, collected from demographically homogeneous populations, over short…
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A machine learning framework for the evaluation of myocardial rotation in patients with noncompaction cardiomyopathy
…echocardiographic predictors for rigid body rotation (RBR) in NCC using a machine learning (ML) based model. Methods and results: Forty-nine outpatients with NCC diagnosis by echocardiography and magnetic resonance imaging (21 men, 42.8±14.8 years) were included. A comprehensive echocardiogram was…
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Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble
…models. Author summary: Prediction is challenging but recently developed Machine Learning techniques allow to dramatically improve prediction accuracy in several domains. However, these tools are often of little application in ecology due to the hardship of gathering information on the needed…
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A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets
Machine learning algorithms, including recent advances in deep learning, are promising for tools for detection and classification of broadband high frequency signals in passive acoustic recordings. However, these methods are generally data-hungry and progress has been limited by challenges related…
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Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants
…has not been studied. In this paper, an information theoretic method and machine learning technique are unified in a model to investigate the extent to which key factors limit cochlear implant electrode discrimination. The framework uses a neural network classifier to predict which electrode is…
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Identification of western North Atlantic odontocete echolocation click types using machine learning and spatiotemporal correlates
A combination of machine learning and expert analyst review was used to detect odontocete echolocation clicks, identify dominant click types, and classify clicks in 32 years of acoustic data collected at 11 autonomous monitoring sites in the western North Atlantic between 2016 and 2019…
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Two-year death prediction models among patients with Chagas Disease using machine learning-based methods
…treatment, which partly supports the high morbidity and mortality rate. Machine Learning (ML) can identify patterns in data that can be used to increase our understanding of a specific problem or make predictions about the future. Thus, the aim of this study was to evaluate different models of ML…
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Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach
Sarcasm is the main reason behind the faulty classification of tweets. It brings a challenge in natural language processing (NLP) as it hampers the method of finding people’s actual sentiment. Various feature engineering techniques are being investigated for the automatic detection of sarcasm…
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Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review
…risk stratifying patients with chest. There is growing recognition that machine learning (ML) will have a significant impact on the practice of medicine in the near future and may assist with diagnosis and risk stratification. This systematic review aims to evaluate how ML has been applied to…
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miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles
…experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose…
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Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data
…non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). We evaluated classifier training and discrimination performance in 100…
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Machine learning to extract muscle fascicle length changes from dynamic ultrasound images in real-time
…In this paper, we benchmark an approach that combines traditional machine learning (ML) models with B-mode ultrasound recordings to obtain muscle fascicle length changes in real-time. To gauge the utility of this framework for ‘in-the-loop’ applications, we evaluate accuracy of the extracted muscle…
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An apta-aggregation based machine learning assay for rapid quantification of lysozyme through texture parameters
A novel assay technique that involves quantification of lysozyme (Lys) through machine learning is put forward here. This article reports the tendency of the well- documented Ellington group anti-Lys aptamer, to produce aggregates when exposed to Lys. This property of apta-aggregation has been…
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A machine learning approach in the non-invasive prediction of intracranial pressure using Modified Photoplethysmography
…regression model was derived by applying an Extreme Gradient Boost (XGB) machine learning algorithm using retinal vascular pulse harmonic regression waveform amplitude (HRWa), first and second harmonic cosine and sine coefficients (an1,2, bn1,2) among other features. Gain and SHapley Additive…
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Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection
…Finally, we compare the RBM’s performance with different supervised learning approaches that include random forests and several deep neural network architectures. Author summary: We show that two-layer neural networks, Restricted Boltzmann Machines (RBM), can be successfully trained on sequence…
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Machine learning based multi-modal prediction of future decline toward Alzheimer’s disease: An empirical study
…years in advance, based on rich multi-modal data, and using modern deep learning methods. Crucially, the machine learning strategy we propose can handle different future time horizons and can be trained with heterogeneous data that exhibit missingness and non-uniform follow-up visit times. Our…
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Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study
Background: Machine learning (ML) algorithms are now increasingly used in infectious disease epidemiology. Epidemiologists should understand how ML algorithms behave within the context of outbreak data where missingness of data is almost ubiquitous. Methods: Using simulated data, we use a ML…
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