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Bioinformatics (Oxford, England) Oct 2019In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient...
MOTIVATION
In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel.
RESULTS
We demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems-three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available.
AVAILABILITY AND IMPLEMENTATION
The Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Machine Learning; Software; Support Vector Machine
PubMed: 30903692
DOI: 10.1093/bioinformatics/btz199 -
Methods (San Diego, Calif.) Jan 2023
Topics: Deep Learning; Computational Biology; Machine Learning
PubMed: 36503039
DOI: 10.1016/j.ymeth.2022.12.001 -
Cardiology Clinics May 2023Artificial intelligence (AI) encompasses a variety of computer algorithms that have a wide range of potential clinical applications in nuclear cardiology. This article... (Review)
Review
Artificial intelligence (AI) encompasses a variety of computer algorithms that have a wide range of potential clinical applications in nuclear cardiology. This article will introduce core terminology and concepts for AI including classifications of AI as well as training and testing regimens. We will then highlight the potential role for AI to improve image registration and image quality. Next, we will discuss methods for AI-driven image attenuation correction. Finally, we will review advancements in machine learning and deep-learning applications for disease diagnosis and risk stratification, including efforts to improve clinical translation of this valuable technology with explainable AI models.
Topics: Humans; Artificial Intelligence; Deep Learning; Algorithms; Machine Learning; Cardiology
PubMed: 37003673
DOI: 10.1016/j.ccl.2023.01.004 -
Medical Physics Jun 2023
Topics: Deep Learning; Diagnostic Imaging; Machine Learning; Radiography; Computers
PubMed: 36416869
DOI: 10.1002/mp.16025 -
ACS Sensors Nov 2020Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML)... (Review)
Review
Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.
Topics: Artificial Intelligence; Biosensing Techniques; Deep Learning; Machine Learning; Neural Networks, Computer
PubMed: 33185417
DOI: 10.1021/acssensors.0c01424 -
NMR in Biomedicine Apr 2022
Topics: Deep Learning; Machine Learning; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy
PubMed: 35253294
DOI: 10.1002/nbm.4713 -
Anesthesiology Clinics Sep 2021With the tremendous volume of data captured during surgeries and procedures, critical care, and pain management, the field of anesthesiology is uniquely suited for the... (Review)
Review
With the tremendous volume of data captured during surgeries and procedures, critical care, and pain management, the field of anesthesiology is uniquely suited for the application of machine learning, neural networks, and closed loop technologies. In the past several years, this area has expanded immensely in both interest and clinical applications. This article provides an overview of the basic tenets of machine learning, neural networks, and closed loop devices, with emphasis on the clinical applications of these technologies.
Topics: Anesthesia; Anesthesiology; Artificial Intelligence; Deep Learning; Humans; Machine Learning
PubMed: 34392886
DOI: 10.1016/j.anclin.2021.03.012 -
Journal of Nuclear Medicine : Official... Sep 2019The aim of this review is to provide readers with an update on the state of the art, pitfalls, solutions for those pitfalls, future perspectives, and challenges in the... (Review)
Review
The aim of this review is to provide readers with an update on the state of the art, pitfalls, solutions for those pitfalls, future perspectives, and challenges in the quickly evolving field of radiomics in nuclear medicine imaging and associated oncology applications. The main pitfalls were identified in study design, data acquisition, segmentation, feature calculation, and modeling; however, in most cases, potential solutions are available and existing recommendations should be followed to improve the overall quality and reproducibility of published radiomics studies. The techniques from the field of deep learning have some potential to provide solutions, especially in terms of automation. Some important challenges remain to be addressed but, overall, striking advances have been made in the field in the last 5 y.
Topics: Deep Learning; Diagnostic Imaging; Humans; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Machine Learning; Nuclear Medicine; Positron-Emission Tomography
PubMed: 31481588
DOI: 10.2967/jnumed.118.220582 -
Academic Radiology Jan 2020Artificial intelligence and deep learning are areas of high interest for radiology investigators at present. However, the field of machine learning encompasses multiple... (Review)
Review
Artificial intelligence and deep learning are areas of high interest for radiology investigators at present. However, the field of machine learning encompasses multiple statistics-based techniques useful for investigators, which may be complementary to deep learning approaches. After a refresher in basic statistical concepts, relevant considerations for machine learning practitioners are reviewed: regression, classification, decision boundaries, and bias-variance tradeoff. Regularization, ground truth, and populations are discussed along with compute and data management principles. Advanced statistical machine learning techniques including bootstrapping, bagging, boosting, decision trees, random forest, XGboost, and support vector machines are reviewed along with relevant examples from the radiology literature.
Topics: Algorithms; Artificial Intelligence; Machine Learning; Radiology; Support Vector Machine
PubMed: 31818379
DOI: 10.1016/j.acra.2019.07.030 -
Experimental Biology and Medicine... Nov 2023The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the... (Review)
Review
The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional and toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, -nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.
Topics: Humans; Deep Learning; Machine Learning; Neural Networks, Computer; Algorithms; Drug-Related Side Effects and Adverse Reactions
PubMed: 38057999
DOI: 10.1177/15353702231209421