113.116 hasil untuk machine learning
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Application of machine learning in bacteriophage research
…the newly identified bacteriophages is inevitable for future research. Machine learning includes powerful techniques that enable the analysis of complex datasets for knowledge discovery and pattern recognition. In this study, we have conducted a comprehensive review of machine learning methods…
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Machine learning designs non-hemolytic antimicrobial peptides
Machine learning (ML) consists of the recognition of patterns from training data and offers the opportunity to exploit large structure–activity databases for drug design. In the area of peptide drugs, ML is mostly being tested to design antimicrobial peptides (AMPs), a class of biomolecules…
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Principles and Practice of Explainable Machine Learning
…trust them? In this report, we focus specifically on data-driven methods—machine learning (ML) and pattern recognition models in particular—so as to survey and distill the results and observations from the literature. The purpose of this report can be especially appreciated by noting that ML models…
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Semantic similarity and machine learning with ontologies
…used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and…
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Machine learning analysis of TCGA cancer data
In recent years, machine learning (ML) researchers have changed their focus towards biological problems that are difficult to analyse with standard approaches. Large initiatives such as The Cancer Genome Atlas (TCGA) have allowed the use of omic data for the training of these algorithms. In order…
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A review of machine learning in obesity
…understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic…
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Machine learning guided aptamer refinement and discovery
…practically constrained to a fraction of the theoretical sequence space. Machine learning provides an opportunity to intelligently navigate this space to identify high-performing aptamers. Here, we propose an approach that employs particle display (PD) to partition a library of aptamers by…
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How Machine Learning Will Revolutionize Electrochemical Sciences
…materials to a more selective process by leveraging model predictions. Machine learning (ML) offers data-driven predictions and can be helpful. Herein we ask if ML can revolutionize the development cycle from decades to a few years. We outline the necessary characteristics of such ML…
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Machine learning estimation of tissue optical properties
Dynamic, in vivo measurement of the optical properties of biological tissues is still an elusive and critically important problem. Here we develop a technique for inverting a Monte Carlo simulation to extract tissue optical properties from the statistical moments of the spatio-temporal response of…
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Machine Learning in Drug Discovery: A Review
…research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation…
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Machine Learning and Student Performance in Teams
This project applies a variety of machine learning algorithms to the interactions of first year college students using the GroupMe messaging platform to collaborate online on a team project. The project assesses the efficacy of these techniques in predicting existing measures of team member…
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Optimization of investment strategies through machine learning
The main objective of this research is to develop a sustainable stock quantitative investing model based on Machine Learning and Economic Value-Added techniques for optimizing investment strategies. Quantitative stock selection and algorithmic trading are the two features of the model. Principal…
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Machine learning for mHealth apps quality evaluation
…quality of mHealth apps. The objective of this paper is to use supervised machine learning algorithms to evaluate the quality of mHealth apps according to the ISO/IEC 25010 quality model based on user feedback. For this purpose, a total of 1682 user reviews have been collected from 86 mHealth apps…
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Prediction of hypercholesterolemia using machine learning techniques
…resources for conducting screening and/or prevention programs. Using machine learning techniques to identify factors contributing to hypercholesterolemia and developing predictive models can help early detection of hypercholesterolemia, especially in developing countries. METHODS: Data from the…
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Machine Learning in Dentistry: A Scoping Review
Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry…
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