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Molecular Phylogenetics and Evolution Jul 2024Machine learning has increasingly been applied to a wide range of questions in phylogenetic inference. Supervised machine learning approaches that rely on simulated... (Review)
Review
Machine learning has increasingly been applied to a wide range of questions in phylogenetic inference. Supervised machine learning approaches that rely on simulated training data have been used to infer tree topologies and branch lengths, to select substitution models, and to perform downstream inferences of introgression and diversification. Here, we review how researchers have used several promising machine learning approaches to make phylogenetic inferences. Despite the promise of these methods, several barriers prevent supervised machine learning from reaching its full potential in phylogenetics. We discuss these barriers and potential paths forward. In the future, we expect that the application of careful network designs and data encodings will allow supervised machine learning to accommodate the complex processes that continue to confound traditional phylogenetic methods.
Topics: Phylogeny; Machine Learning; Supervised Machine Learning; Models, Genetic
PubMed: 38565358
DOI: 10.1016/j.ympev.2024.108066 -
Advances and applications of machine learning and deep learning in environmental ecology and health.Environmental Pollution (Barking, Essex... Oct 2023Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image... (Review)
Review
Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.
Topics: Deep Learning; Machine Learning; Algorithms; Environmental Health; Ecology
PubMed: 37567408
DOI: 10.1016/j.envpol.2023.122358 -
The American Journal of Geriatric... Mar 2024The goal of this overview is to help clinicians develop basic proficiency with the terminology of deep learning and understand its fundamentals and early applications.... (Review)
Review
The goal of this overview is to help clinicians develop basic proficiency with the terminology of deep learning and understand its fundamentals and early applications. We describe what machine learning and deep learning represent and explain the underlying data science principles. We also review current promising applications and identify ethical issues that bear consideration. Deep Learning is a new type of machine learning that is remarkably good at finding patterns in data, and in some cases generating realistic new data. We provide insights into how deep learning works and discuss its relevance to geriatric psychiatry.
Topics: Humans; Aged; Mental Health; Deep Learning; Machine Learning; Geriatric Psychiatry
PubMed: 38142162
DOI: 10.1016/j.jagp.2023.11.008 -
Physica Medica : PM : An International... Mar 2021The manuscript aims at providing an overview of the published algorithms/automation tool for artificial intelligence applied to imaging for Healthcare. A PubMed search... (Review)
Review
The manuscript aims at providing an overview of the published algorithms/automation tool for artificial intelligence applied to imaging for Healthcare. A PubMed search was performed using the query string to identify the proposed approaches (algorithms/automation tools) for artificial intelligence (machine and deep learning) in a 5-year period. The distribution of manuscript in the various disciplines and the investigated image types according to the AI approaches are presented. The limitation and opportunity of AI application in the clinical practice or in the next future research is discussed.
Topics: Algorithms; Artificial Intelligence; Deep Learning; Diagnostic Imaging; Machine Learning
PubMed: 33826964
DOI: 10.1016/j.ejmp.2021.03.026 -
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