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Briefings in Bioinformatics Sep 2023
Topics: Artificial Intelligence; Machine Learning; Algorithms; Biology
PubMed: 37965807
DOI: 10.1093/bib/bbad415 -
Zoological Research Nov 2023Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision,... (Review)
Review
Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision, natural language processing and speech recognition in revolutionizing zoological research. This review provides an overview of the primary tasks, core models, datasets, and applications of AI in zoological research, including animal classification, resource conservation, behavior, development, genetics and evolution, breeding and health, disease models, and paleontology. Additionally, we explore the challenges and future directions of integrating AI into this field. Based on numerous case studies, this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom. As we build a bridge between beast and byte realms, this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored.
Topics: Animals; Artificial Intelligence; Machine Learning
PubMed: 37933101
DOI: 10.24272/j.issn.2095-8137.2023.263 -
Zhongguo Xiu Fu Chong Jian Wai Ke Za... Dec 2023To review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice.
OBJECTIVE
To review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice.
METHODS
A comprehensive literature review was conducted to assess the status of machine learning algorithms in orthopaedic trauma research, both nationally and internationally.
RESULTS
The rapid advancement of computer data processing and the growing convergence of medicine and industry have led to the widespread utilization of artificial intelligence in healthcare. Currently, machine learning plays a significant role in orthopaedic trauma, demonstrating high performance and accuracy in various areas including fracture image recognition, diagnosis stratification, clinical decision-making, evaluation, perioperative considerations, and prognostic risk prediction. Nevertheless, challenges persist in the development and clinical implementation of machine learning. These include limited database samples, model interpretation difficulties, and universality and individualisation variations.
CONCLUSION
The expansion of clinical sample sizes and enhancements in algorithm performance hold significant promise for the extensive application of machine learning in supporting orthopaedic trauma diagnosis, guiding decision-making, devising individualized medical strategies, and optimizing the allocation of clinical resources.
Topics: Humans; Algorithms; Artificial Intelligence; Machine Learning; Orthopedics; Wounds and Injuries; Biomedical Research
PubMed: 38130202
DOI: 10.7507/1002-1892.202308064 -
Sensors (Basel, Switzerland) Aug 2023Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions...
Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study's results could help improve coaching techniques and enhance batsmen's performance in cricket, ultimately improving the game's overall quality.
Topics: Humans; Algorithms; Cricket Sport; Machine Learning; Support Vector Machine
PubMed: 37571624
DOI: 10.3390/s23156839 -
Microbial Genomics Aug 2023Results published in an article by Poore . (. 2020;579:567-574) suggested that machine learning models can almost perfectly distinguish between tumour types based on...
Results published in an article by Poore . (. 2020;579:567-574) suggested that machine learning models can almost perfectly distinguish between tumour types based on their microbial composition using machine learning models. Whilst we believe that there is the potential for microbial composition to be used in this manner, we have concerns with the paper that make us question the certainty of the conclusions drawn. We believe there are issues in the areas of the contribution of contamination, handling of batch effects, false positive classifications and limitations in the machine learning approaches used. This makes it difficult to identify whether the authors have identified true biological signal and how robust these models would be in use as clinical biomarkers. We commend Poore . on their approach to open data and reproducibility that has enabled this analysis. We hope that this discourse assists the future development of machine learning models and hypothesis generation in microbiome research.
Topics: Humans; Reproducibility of Results; Microbiota; Machine Learning; Neoplasms
PubMed: 37555750
DOI: 10.1099/mgen.0.001088 -
PloS One 2023In recent years precise and up-to-date information regarding seabed depth has become more and more important for companies and institutions that operate on coastlines....
In recent years precise and up-to-date information regarding seabed depth has become more and more important for companies and institutions that operate on coastlines. While direct, in-situ measurements are performed regularly, they are expensive, time-consuming and impractical to be performed in short time intervals. At the same time, an ever-increasing amount of satellite imaging data becomes available. With these images, it became possible to develop bathymetry estimation algorithms that can predict seabed depth and utilize them systematically. Since there are a number of theoretical approaches, physical models, and empirical techniques to use satellite observations in order to estimate depth in the coastal zone, the presented article compares the performance and precision of the most common one to modern machine learning algorithms. More specifically, the models based on shallow neural networks, decision trees and Random Forest algorithms have been proposed, investigated and confronted with the performance of pure analytical models. The particular proposed machine learning models differ also in a set of satellite data bands used as an input as well as in applying or not geographical weighting in the learning process. The obtained results point towards the best performance of the regression tree algorithm that incorporated as inputs information about data localization, raw reflectance data from four satellite data bands and a quotient of logarithms of B2 and B3 bands. The study for the paper was performed in relatively optically difficult and spatially variant conditions of the south Baltic coastline starting at Szczecin, Poland on the west (53°26'17'' N, 14°32'32'' E) to Hel peninsula (54°43'04,3774'' N 18°37'56,9175'' E). The reference bathymetry data was acquired from Polish Marine Administration. It was obtained through profile probing with single-beam sonar or direct in-situ probing.
Topics: Algorithms; Geography; Machine Learning; Neural Networks, Computer
PubMed: 37713403
DOI: 10.1371/journal.pone.0291595 -
Human Reproduction (Oxford, England) Feb 2024With the exponential growth of computing power and accumulation of embryo image data in recent years, artificial intelligence (AI) is starting to be utilized in embryo... (Review)
Review
With the exponential growth of computing power and accumulation of embryo image data in recent years, artificial intelligence (AI) is starting to be utilized in embryo selection in IVF. Amongst different AI technologies, machine learning (ML) has the potential to reduce operator-related subjectivity in embryo selection while saving labor time on this task. However, as modern deep learning (DL) techniques, a subcategory of ML, are increasingly used, its integrated black-box attracts growing concern owing to the well-recognized issues regarding lack of interpretability. Currently, there is a lack of randomized controlled trials to confirm the effectiveness of such black-box models. Recently, emerging evidence has shown underperformance of black-box models compared to the more interpretable traditional ML models in embryo selection. Meanwhile, glass-box AI, such as interpretable ML, is being increasingly promoted across a wide range of fields and is supported by its ethical advantages and technical feasibility. In this review, we propose a novel classification system for traditional and AI-driven systems from an embryology standpoint, defining different morphology-based selection approaches with an emphasis on subjectivity, explainability, and interpretability.
Topics: Humans; Artificial Intelligence; Machine Learning; Embryo, Mammalian
PubMed: 38061074
DOI: 10.1093/humrep/dead254 -
Human Vaccines & Immunotherapeutics Dec 2023A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to... (Review)
Review
A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.
Topics: Multiomics; Transcriptome; Vaccines; Machine Learning
PubMed: 38100557
DOI: 10.1080/21645515.2023.2282803 -
PloS One 2023The objective of this study is to investigate the application of machine learning techniques to the large-scale human expert evaluation of the impact of academic...
The objective of this study is to investigate the application of machine learning techniques to the large-scale human expert evaluation of the impact of academic research. Using publicly available impact case study data from the UK's Research Excellence Framework (2014), we trained five machine learning models on a range of qualitative and quantitative features, including institution, discipline, narrative style (explicit and implicit), and bibliometric and policy indicators. Our work makes two key contributions. Based on the accuracy metric in predicting high- and low-scoring impact case studies, it shows that machine learning models are able to process information to make decisions that resemble those of expert evaluators. It also provides insights into the characteristics of impact case studies that would be favoured if a machine learning approach was applied for their automated assessment. The results of the experiments showed strong influence of institutional context, selected metrics of narrative style, as well as the uptake of research by policy and academic audiences. Overall, the study demonstrates promise for a shift from descriptive to predictive analysis, but suggests caution around the use of machine learning for the assessment of impact case studies.
Topics: Humans; Machine Learning; Health Facilities; Narration
PubMed: 37535633
DOI: 10.1371/journal.pone.0288469 -
Bioinformatics (Oxford, England) Oct 2023Biomedical and healthcare domains generate vast amounts of complex data that can be challenging to analyze using machine learning tools, especially for researchers...
MOTIVATION
Biomedical and healthcare domains generate vast amounts of complex data that can be challenging to analyze using machine learning tools, especially for researchers without computer science training.
RESULTS
Aliro is an open-source software package designed to automate machine learning analysis through a clean web interface. By infusing the power of large language models, the user can interact with their data by seamlessly retrieving and executing code pulled from the large language model, accelerating automated discovery of new insights from data. Aliro includes a pre-trained machine learning recommendation system that can assist the user to automate the selection of machine learning algorithms and its hyperparameters and provides visualization of the evaluated model and data.
AVAILABILITY AND IMPLEMENTATION
Aliro is deployed by running its custom Docker containers. Aliro is available as open-source from GitHub at: https://github.com/EpistasisLab/Aliro.
Topics: Software; Algorithms; Machine Learning; Language
PubMed: 37796839
DOI: 10.1093/bioinformatics/btad606