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Current Opinion in Neurobiology Feb 2024Recently, a confluence between trends in neuroscience and machine learning has brought a renewed focus on unsupervised learning, where sensory processing systems learn... (Review)
Review
Recently, a confluence between trends in neuroscience and machine learning has brought a renewed focus on unsupervised learning, where sensory processing systems learn to exploit the statistical structure of their inputs in the absence of explicit training targets or rewards. Sophisticated experimental approaches have enabled the investigation of the influence of sensory experience on neural self-organization and its synaptic bases. Meanwhile, novel algorithms for unsupervised and self-supervised learning have become increasingly popular both as inspiration for theories of the brain, particularly for the function of intermediate visual cortical areas, and as building blocks of real-world learning machines. Here we review some of these recent developments, placing them in historical context and highlighting some research lines that promise exciting breakthroughs in the near future.
Topics: Unsupervised Machine Learning; Machine Learning; Brain; Algorithms
PubMed: 38154417
DOI: 10.1016/j.conb.2023.102834 -
British Journal of Anaesthesia Dec 2020
Topics: Anesthesiology; Automation; Bias; Humans; Machine Learning; Perioperative Care; Risk Assessment
PubMed: 32838979
DOI: 10.1016/j.bja.2020.07.040 -
World Neurosurgery Apr 2022Recent years have witnessed artificial intelligence (AI) make meteoric leaps in both medicine and surgery, bridging the gap between the capabilities of humans and... (Review)
Review
Recent years have witnessed artificial intelligence (AI) make meteoric leaps in both medicine and surgery, bridging the gap between the capabilities of humans and machines. Digitization of operating rooms and the creation of massive quantities of data have paved the way for machine learning and computer vision applications in surgery. Surgical phase recognition (SPR) is a newly emerging technology that uses data derived from operative videos to train machine and deep learning algorithms to identify the phases of surgery. Advancement of this technology will be key in establishing context-aware surgical systems in the future. By automatically recognizing and evaluating the current surgical scenario, these intelligent systems are able to provide intraoperative decision support, improve operating room efficiency, assess surgical skills, and aid in surgical training and education. Still in its infancy, SPR has been mainly studied in laparoscopic surgeries, with a contrasting stark lack of research within neurosurgery. Given the high-tech and rapidly advancing nature of neurosurgery, we believe SPR has a tremendous untapped potential in this field. Herein, we present an overview of the SPR technology, its potential applications in neurosurgery, and the challenges that lie ahead.
Topics: Artificial Intelligence; Deep Learning; Humans; Machine Learning; Neurosurgery; Neurosurgical Procedures
PubMed: 35026457
DOI: 10.1016/j.wneu.2022.01.020 -
IEEE Transactions on Neural Networks... Feb 2024Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with...
Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques. However, even the most advanced machine learning techniques require relatively large, labeled EEG repositories. EEG data collection and labeling are costly. Moreover, combining available datasets to achieve a large data volume is usually infeasible due to inconsistent experimental paradigms across trials. Self-supervised learning (SSL) solves these challenges because it enables learning from EEG records across trials with variable experimental paradigms, even when the trials explore different phenomena. It aggregates multiple EEG repositories to increase accuracy, reduce bias, and mitigate overfitting in machine learning training. In addition, SSL could be employed in situations where there is limited labeled training data, and manual labeling is costly. This article: 1) provides a brief introduction to SSL; 2) describes some SSL techniques employed in recent studies, including EEG; 3) proposes current and potential SSL techniques for future investigations in EEG studies; 4) discusses the cons and pros of different SSL techniques; and 5) proposes holistic implementation tips and potential future directions for EEG SSL practices.
Topics: Neural Networks, Computer; Electroencephalography; Machine Learning; Supervised Machine Learning
PubMed: 35867362
DOI: 10.1109/TNNLS.2022.3190448 -
Briefings in Bioinformatics Jul 2022Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are... (Review)
Review
Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody-antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.
Topics: Antibodies; Deep Learning; Feasibility Studies; Machine Learning
PubMed: 35830864
DOI: 10.1093/bib/bbac267 -
Surgical Pathology Clinics Mar 2023Machine learning methods have been growing in prominence across all areas of medicine. In pathology, recent advances in deep learning (DL) have enabled computational... (Review)
Review
Machine learning methods have been growing in prominence across all areas of medicine. In pathology, recent advances in deep learning (DL) have enabled computational analysis of histological samples, aiding in diagnosis and characterization in multiple disease areas. In cancer, and particularly endocrine cancer, DL approaches have been shown to be useful in tasks ranging from tumor grading to gene expression prediction. This review summarizes the current state of DL research in endocrine cancer histopathology with an emphasis on experimental design, significant findings, and key limitations.
Topics: Humans; Deep Learning; Machine Learning; Neoplasms; Medicine; Endocrine Gland Neoplasms
PubMed: 36739164
DOI: 10.1016/j.path.2022.09.014 -
Trends in Cell Biology Jul 2023Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically... (Review)
Review
Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal 'hit' compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation. These powerful methods also promise to impact drug discovery by accelerating the identification of effective drugs and their modes of action. In this review, we highlight applications and adaptations of ML, especially DL methods for cell-based phenotypic drug discovery (PDD).
Topics: Artificial Intelligence; Deep Learning; Drug Discovery; Machine Learning; Phenotype
PubMed: 36623998
DOI: 10.1016/j.tcb.2022.11.011 -
The Analyst Oct 2021Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been... (Review)
Review
Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been compromised for electrode fabrication and data analysis. Machine learning and experimental designs are chemometric tools that have been proved to be useful in method development and data analysis. This minireview summarizes recent applications of machine learning and experimental designs in electroanalytical chemistry. First, experimental designs, , full factorial, central composite, and Box-Behnken are discussed as systematic approaches to optimize electrode fabrication to consider the effects from individual variables and their interactions. Then, the principles of machine learning algorithms, including linear and logistic regressions, neural network, and support vector machine, are introduced. These machine learning models have been implemented to extract complex relationships between chemical structures and their electrochemical properties and to analyze complicated electrochemical data to improve calibration and analyte classification, such as in electronic tongues. Lastly, the future of machine learning and experimental designs in electrochemical sensors is outlined. These chemometric strategies will accelerate the development and enhance the performance of electrochemical devices for point-of-care diagnostics and commercialization.
Topics: Algorithms; Biosensing Techniques; Machine Learning; Neural Networks, Computer; Support Vector Machine
PubMed: 34585185
DOI: 10.1039/d1an01148k -
Environmental Science and Pollution... Sep 2023The sustainability of the earth depends on renewable energy. Forecasting the output of renewable energy has a big impact on how we operate and manage our power networks.... (Review)
Review
The sustainability of the earth depends on renewable energy. Forecasting the output of renewable energy has a big impact on how we operate and manage our power networks. Accurate forecasting of renewable energy generation is crucial to ensuring grid dependability and permanence and reducing the risk and cost of the energy market and infrastructure. Although there are several approaches to forecasting solar radiation on a global scale, the two most common ones are machine learning algorithms and cloud pictures combined with physical models. The objective is to present a summary of machine learning-based techniques for solar irradiation forecasting in this context. Renewable energy is being used more and more in the world's energy grid. Numerous strategies, including hybrids, physical models, statistical approaches, and artificial intelligence techniques, have been developed to anticipate the use of renewable energy. This paper examines methods for forecasting renewable energy based on deep learning and machine learning. Review and analysis of deep learning and machine learning forecasts for renewable energy come first. The second paragraph describes metaheuristic optimization techniques for renewable energy. The third topic was the open issue of projecting renewable energy. I will wrap up with a few potential future job objectives.
Topics: Artificial Intelligence; Deep Learning; Machine Learning; Renewable Energy; Algorithms; Forecasting
PubMed: 37552450
DOI: 10.1007/s11356-023-29064-w -
Scientific Reports Sep 2022The analytic procedures incorporated to facilitate the delivery of projects are often referred to as project analytics. Existing techniques focus on retrospective...
The analytic procedures incorporated to facilitate the delivery of projects are often referred to as project analytics. Existing techniques focus on retrospective reporting and understanding the underlying relationships to make informed decisions. Although machine learning algorithms have been widely used in addressing problems within various contexts (e.g., streamlining the design of construction projects), limited studies have evaluated pre-existing machine learning methods within the delivery of construction projects. Due to this, the current research aims to contribute further to this convergence between artificial intelligence and the execution construction project through the evaluation of a specific set of machine learning algorithms. This study proposes a machine learning-based data-driven research framework for addressing problems related to project analytics. It then illustrates an example of the application of this framework. In this illustration, existing data from an open-source data repository on construction projects and cost overrun frequencies was studied in which several machine learning models (Python's Scikit-learn package) were tested and evaluated. The data consisted of 44 independent variables (from materials to labour and contracting) and one dependent variable (project cost overrun frequency), which has been categorised for processing under several machine learning models. These models include support vector machine, logistic regression, k-nearest neighbour, random forest, stacking (ensemble) model and artificial neural network. Feature selection and evaluation methods, including the Univariate feature selection, Recursive feature elimination, SelectFromModel and confusion matrix, were applied to determine the most accurate prediction model. This study also discusses the generalisability of using the proposed research framework in other research contexts within the field of project management. The proposed framework, its illustration in the context of construction projects and its potential to be adopted in different contexts will significantly contribute to project practitioners, stakeholders and academics in addressing many project-related issues.
Topics: Artificial Intelligence; Logistic Models; Machine Learning; Retrospective Studies; Support Vector Machine
PubMed: 36085353
DOI: 10.1038/s41598-022-19728-x