214.423 hasil untuk machine learning
Menampilkan 441–460 · 1,46 detik · RSS
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Machine learning predicts new anti-CRISPR proteins
…identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method to aid direct identification of new potential anti-CRISPRs using only protein sequence information. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking. We then…
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Choosing the right molecular machine learning potential
…level and yield such important observables as reaction rates and spectra. Machine learning potentials promise to significantly reduce the computational cost and hence enable otherwise unfeasible simulations. However, the surging number of such potentials begs the question of which one to choose or…
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GDP Forecasting: Machine Learning, Linear or Autoregression?
This paper compares the predictive power of different models to forecast the real U.S. GDP. Using quarterly data from 1976 to 2020, we find that the machine learning K-Nearest Neighbour (KNN) model captures the self-predictive ability of the U.S. GDP and performs better than traditional time series…
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Employing Multimodal Machine Learning for Stress Detection
…are identified in the literature for this purpose; however, very few machine learning-based methods are proposed for such multimodal fusion tasks. In this work, a multimodal AI-based framework is proposed to monitor a person's working behavior and stress levels. We propose a methodology for…
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Machine learning in patient flow: a review
This work is a review of the ways in which machine learning has been used in order to plan, improve or aid the problem of moving patients through healthcare services. We decompose the patient flow problem into four subcategories: prediction of demand on a healthcare institution, prediction of the…
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Machine learning approaches for drug combination therapies
…and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized…
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Machine learning in optical coherence tomography angiography
…vascular changes. The feasibility of using quantitative OCTA features for machine learning classification of different retinopathies has been demonstrated. Deep learning-based applications have also been explored for automatic OCTA image analysis and disease classification. In this article, we…
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Artificial intelligence: machine learning for chemical sciences
Research in molecular sciences witnessed the rise and fall of Artificial Intelligence (AI)/ Machine Learning (ML) methods, especially artificial neural networks, few decades ago. However, we see a major resurgence in the use of modern ML methods in scientific research during the last few years…
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Protein pK(a) Prediction with Machine Learning
…between protein structure and function. In this work, we introduce a deep learning-based protein pK(a) predictor DeepKa, which is trained and validated with the pK(a) values derived from continuous constant-pH molecular dynamics (CpHMD) simulations of 279 soluble proteins. Here, the CpHMD…
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Machine Learning Predictions of Cancer Driver Mutations
A method to predict the activation status of kinase domain mutations in cancer is presented. This method, which makes use of the machine learning technique support vector machines (SVM), has applications to cancer treatment, as well as numerous other diseases that involve kinase misregulation.
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Machine Learning in Modeling of Mouse Behavior
Mouse behavior is a primary outcome in evaluations of therapeutic efficacy. Exhaustive, continuous, multiparametric behavioral phenotyping is a valuable tool for understanding the pathophysiological status of mouse brain diseases. Automated home cage behavior analysis produces highly granulated…
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Machine learning models and over-fitting considerations
Machine learning models may outperform traditional statistical regression algorithms for predicting clinical outcomes. Proper validation of building such models and tuning their underlying algorithms is necessary to avoid over-fitting and poor generalizability, which smaller datasets can be more…
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Artificial intelligence and machine learning in nephropathology
…convolutional neural networks, that are currently the state of the art in machine learning software for this task. Because AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly in the example of oncopathology. Moreover, we discuss…
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Inverse Design of Materials by Machine Learning
…With the development of physics, statistics, computer science, etc., machine learning offers the opportunity to systematically find new materials. Especially by inverse design based on machine learning, one can make use of the existing knowledge without attempting mathematical inversion of the…
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Research Progress of Gliomas in Machine Learning
…of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the…
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Machine learning for DCO-OFDM based LiFi
…appropriate DC bias level for DCO-OFDM is important. This paper applies machine learning (ML) algorithms to find optimum DC-bias value for DCO-OFDM based LiFi systems. For this, a dataset is generated for DCO-OFDM using MATLAB tool. Next, ML algorithms are applied using Python programming language…
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Enterprise Risk Assessment Based on Machine Learning
Scientific risk assessment is an important guarantee for the healthy development of an enterprise. With the continuous development and maturity of machine learning technology, it has played an important role in the field of data prediction and risk assessment. This paper conducts research on the…
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Predicting bloodstream infection outcome using machine learning
…patient stratification. We developed electronic medical record-based machine learning models that predict patient outcomes of BSI. The area under the receiver-operating characteristics curve was 0.82 for a full featured inclusive model, and 0.81 for a compact model using only 25 features. Our…
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