113,116 results for machine learning
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Prediction of Dental Implants Using Machine Learning Algorithms
…prediction of dental implant cases. This research develops a set of four machine learning algorithms to predict when a patient might need dental implants. These models are the Bayesian network, random forest, AdaBoost algorithm, and improved AdaBoost algorithm. This work shows that the developed…
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Prediction of Breast Cancer using Machine Learning Approaches
BACKGROUND: Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data. OBJECTIVE: This study aimed to predict breast cancer…
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Machine learning in perioperative medicine: a systematic review
BACKGROUND: Risk stratification plays a central role in anesthetic evaluation. The use of Big Data and machine learning (ML) offers considerable advantages for collection and evaluation of large amounts of complex health-care data. We conducted a systematic review to understand the role of ML in…
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MLIMC: Machine Learning-Based Implicit-Solvent Monte Carlo()
…model but is much faster in simulation time. In this work, we develop a machine learning-based implicit-solvent Monte Carlo (MLIMC) method by combining the advantages of both implicit solvent models in accuracy and efficiency. Specifically, the MLIMC method uses a fast and accurate PB-based machine…
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Machine learning for emerging infectious disease field responses
…with this challenge from the aspect of preventive medicine by exploiting machine learning technologies. The study has been based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality…
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Editorial: Machine Learning Methodologies to Study Molecular Interactions
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Ensembled machine learning framework for drug sensitivity prediction
…to predict drug sensitivity. The present study has proposed an ensemble learning framework for drug‐response prediction using a modified rotation forest. The proposed framework is further compared with three state‐of‐the‐art algorithms and two baseline methods using Genomics of Drug Sensitivity in…
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Machine learning for flow batteries: opportunities and challenges
…artificial intelligence (AI) has shown tremendous potential in chemistry. Machine learning (ML), as one of the most important branches of AI, plays an important role in accelerating the discovery and design of key materials for flow batteries (FBs), and the optimization of FB systems. In this…
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Disease diagnostics using machine learning of immune receptors
…We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis (Mal-ID) , an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune…
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Reformulating Reactivity Design for Data-Efficient Machine Learning
[Image: see text] Machine learning (ML) can deliver rapid and accurate reaction barrier predictions for use in rational reactivity design. However, model training requires large data sets of typically thousands or tens of thousands of barriers that are very expensive to obtain computationally or…
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Supervised Machine Learning Methods for Seasonal Influenza Diagnosis
…the same pattern in all populations. The aim of this work is to find a machine learning method to facilitate decision making in the clinical differentiation between positive and negative influenza patients, based on their symptoms and demographic features. The research sample consisted of 15480…
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Predicting amputation using machine learning: A systematic review
…of bias, most often due to small sample sizes. In conclusion, multiple machine learning models have been successfully developed that have the potential to be superior to traditional modeling techniques and prospective clinical judgment in predicting amputation. Further research is needed to…
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Handling DNA malfunctions by unsupervised machine learning model
…of the cell in later stages. In this paper, we introduced an Unsupervised Machine learning Model for DNA Damage Diagnosis and Analysis. Mainly, we employed K-means clustering unsupervised machine learning algorithms. Unsupervised algorithms commonly draw conclusions from datasets by solely…
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Extracting medicinal chemistry intuition via preference machine learning
…work we aim to replicate this process by applying artificial intelligence learning-to-rank techniques on feedback that was obtained from 35 chemists at Novartis over the course of several months. We exemplify the usefulness of the learned proxies in routine tasks such as compound prioritization…
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Interpretable machine learning-assisted screening of perovskite oxides
…approaches and density functional theory. Herein, interpretable machine learning is applied to identify perovskite oxides from virtual perovskite-type combinations by constructing classification and regression models to predict their thermodynamic stability and energy above the convex hull (E(h))…
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Machine learning the dimension of a Fano variety
…properties of a Fano variety directly from its quantum period. We apply machine learning to the question: does the quantum period of X know the dimension of X? Note that there is as yet no theoretical understanding of this. We show that a simple feed-forward neural network can determine the…
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Machine learning coarse-grained potentials of protein thermodynamics
…structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.
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