8.473 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties
…In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC50 values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs…
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Glaucomatous Patterns in Frequency Doubling Technology (FDT) Perimetry Data Identified by Unsupervised Machine Learning Classifiers
Purpose: The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes…
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Do Two Machine-Learning Based Prognostic Signatures for Breast Cancer Capture the Same Biological Processes?
…for this apparent discrepancy have been explained by the limits of simple machine-learning identification and ranking techniques, and the biological relevance and meaning of the prognostic gene lists was questioned. Subsequently, proponents of the prognostic gene lists claimed that different lists…
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The Validation and Assessment of Machine Learning: A Game of Prediction from High-Dimensional Data
In applied statistics, tools from machine learning are popular for analyzing complex and high-dimensional data. However, few theoretical results are available that could guide to the appropriate machine learning tool in a new application. Initial development of an overall strategy thus often…
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Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization
…are complex and highly constrained. Here we describe a predictive, machine-learning approach that captures this complexity to facilitate successful MP engineering and design. Machine learning on carefully-chosen training sequences made by structure-guided SCHEMA recombination has enabled us to…
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Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters
Purpose: This study aimed to develop a machine learning-based algorithm for objective classification of the optic disc in patients with open-angle glaucoma (OAG), using quantitative parameters obtained from ophthalmic examination instruments. Methods: This study enrolled 163 eyes of 105 OAG…
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Multiparametric MRI Characterization and Prediction in Autism Spectrum Disorder Using Graph Theory and Machine Learning
This study employed graph theory and machine learning analysis of multiparametric MRI data to improve characterization and prediction in autism spectrum disorders (ASD). Data from 127 children with ASD (13.5±6.0 years) and 153 age- and gender-matched typically developing children (14.5±5.7 years)…
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A Contact-Imaging Based Microfluidic Cytometer with Machine-Learning for Single-Frame Super-Resolution Processing
…paper introduces a single-frame super-resolution processing with on-line machine-learning for contact images of cells. A corresponding contact-imaging based microfluidic cytometer prototype is demonstrated for cell recognition and counting. Compared with commercial flow cytometer, less than 8%…
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Effect of Subliminal Lexical Priming on the Subjective Perception of Images: A Machine Learning Approach
…overall priming effect on participants’ explicit ratings. In addition, machine learning algorithms such as support vector machines (SVMs), and AdaBoost classifiers were applied to infer the prime affect type from the ERPs. The highest classification rates of 95.0% and 70.0% obtained respectively…
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QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
Background: Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by…
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Machine learning to support social media empowered patients in cancer care and cancer treatment decisions
…OSG are complementary, yet significant adjunct to patient journeys. Machine learning and natural language processing techniques can be applied to these large volumes of unstructured text discussions accumulated in OSG for intelligent extraction of patient-reported demographics, behaviours…
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Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose
…adverse events. Remarkable efforts have been made to develop the machine learning based warfarin dosing algorithms incorporating clinical factors and genetic variants such as polymorphisms in CYP2C9 and VKORC1. The most widely validated pharmacogenetic algorithm is the IWPC algorithm based on…
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Cardiac Phase Space Tomography: A novel method of assessing coronary artery disease utilizing machine learning
…analysis. Cardiac Phase Space Tomography Analysis (cPSTA), employing machine-learned linear models from an elastic net method optimized by a genetic algorithm, analyzes thoracic phase signals to identify unique mathematical and tomographic features associated with the presence of flow-limiting…
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Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D . sechellia
…via introgression from a sister population. We developed a novel machine learning framework, called FILET (Finding Introgressed Loci via Extra-Trees) capable of revealing genomic introgression with far greater power than competing methods. FILET works by combining information from a number of…
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Webcrawling and machine learning as a new approach for the spatial distribution of atmospheric emissions
…is a model trained for image recognition and classification based on machine learning techniques. The images from the real estate advertisements are collected and processed to identify wood burning installations, which are automatically classified according to the three classes used in official…
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An open-source tool for analysis and automatic identification of dendritic spines using machine learning
Synaptic plasticity, the cellular basis for learning and memory, is mediated by a complex biochemical network of signaling proteins. These proteins are compartmentalized in dendritic spines, the tiny, bulbous, post-synaptic structures found on neuronal dendrites. The ability to screen a high number…
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Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours
…classification accuracies. We investigated a selection of supervised machine learning methods for interpreting behavioural data from captive otariids (fur seals and sea lions). We conducted controlled experiments with 12 seals, where their behaviours were filmed while they were wearing 3-axis…
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A Machine Learning Approach for Using the Postmortem Skin Microbiome to Estimate the Postmortem Interval
…average error of ±55 accumulated degree days (ADD). This study outlines a machine learning approach for the use of necrobiome data in the prediction of the PMI and thereby provides a successful proof-of- concept that skin microbiota is a promising tool in forensic death investigations.
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Predictors of Post-operative Mycetoma Recurrence Using Machine-Learning Algorithms: The Mycetoma Research Center Experience
Post-operative recurrence in mycetoma after adequate medical and surgical treatment is common and a serious problem. It has health, socio-economic and psychological detrimental effects on patients and families. It is with this in mind, we set out to determine the predictors of post-operative…
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Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis
…intra-rater variability, the use of methods which can automate it, like Machine Learning techniques, is of value. OBJECTIVE: The goal of this paper is to present the state of the art evidence, trends and gaps in the research related to bone age assessment studies that make use of Machine Learning…
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