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Academic Articles & Research Papers
Showing 20 of 987 academic articles
Orges Koci, Richard K. Russell, M. Guftar Shaikh, Christine Edwards et al. • Microbiome
(2024)
Abstract Background Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, t...
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Open Access (DOAJ)
DOI: 10.1186/s40168-024-01834-9
Hanjie Xu, Jian Xiong, Jinyu Wu, Xianlai Zhou et al. • Scientific Reports
(2025)
Abstract The Ivy Algorithm (IVYA), a swarm intelligence algorithm inspired by plant growth, presents a novel framework for optimization. To unlock its full potential in complex, high-dimensional problems, it is crucial to address the fundam...
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Open Access (DOAJ)
DOI: 10.1038/s41598-025-32510-z
Upendra Kumar Pradhan, Nitesh Kumar Sharma, Prakash Kumar, Ashwani Kumar et al. • PLoS ONE
(2021)
Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are inv...
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Open Access (DOAJ)
DOI: 10.1371/journal.pone.0258550
Felix Brockherde, Leslie Vogt-Maranto, Li Li, Mark E. Tuckerman et al. • Nature Communications
(2017)
Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy fun...
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DOI: https://doi.org/10.1038/s41467-017-00839-3
PMC: 5636838
Albert P. Bartók, Sandip De, Carl R. Poelking, Noam Bernstein et al. • Science Advances
(2017)
Determining the stability of molecules and condensed phases is the cornerstone of atomistic modeling, underpinning our understanding of chemical and materials properties and transformations. We show that a machine-learning model, based on a...
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DOI: https://doi.org/10.1126/sciadv.1701816
PMC: 5729016
Danilo Bzdok, Andreas Meyer‐Lindenberg • Biological Psychiatry Cognitive Neuroscience and Neuroimaging
(2017)
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Open Access
DOI: https://doi.org/10.1016/j.bpsc.2017.11.007
Haldun Aytuğ, Siddhartha Bhattacharyya, G.J. Koehler, Jane L. Snowdon • IEEE Transactions on Engineering Management
(1994)
This paper has two primary purposes: to motivate the need for machine learning in scheduling systems and to survey work on machine learning in scheduling. In order to motivate the need for machine learning in scheduling, we briefly motivate...
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DOI: https://doi.org/10.1109/17.293383
Matthew Francis Dixon, Igor Halperin, Paul Bilokon • Springer eBooks
(2020)
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DOI: https://doi.org/10.1007/978-3-030-41068-1
Jaime Carbonell, Ryszard S. Michalski, Tom M. Mitchell • Springer eBooks
(1983)
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DOI: https://doi.org/10.1007/978-3-662-12405-5_1
Raymund C. Sison, Masamichi Shimura (1998)
After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge neces...
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Open Access
Federico Cabitza, Angela Locoro, Giuseppe Banfi • Frontiers in Bioengineering and Biotechnology
(2018)
In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic probl...
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DOI: https://doi.org/10.3389/fbioe.2018.00075
PMC: 6030383
Qingyang Ling • Applied and Computational Engineering
(2023)
Machine learning is a field of study where the computer can learn for itself without a human explicitly hardcoding the knowledge for it. These algorithms make up the backbone of machine learning. This paper aims to study the field of machin...
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DOI: https://doi.org/10.54254/2755-2721/4/20230355
Aaron Lee, Paul M. Taylor, Jayashree Kalpathy–Cramer, Adnan Tufail • Ophthalmology
(2017)
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Open Access
DOI: https://doi.org/10.1016/j.ophtha.2017.08.046
Pedro Domingos • Communications of the ACM
(2012)
Tapping into the "folk knowledge" needed to advance machine learning applications....
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DOI: https://doi.org/10.1145/2347736.2347755
Sarkar Siddique, James C. L. Chow • Encyclopedia
(2021)
Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelli...
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DOI: https://doi.org/10.3390/encyclopedia1010021
Gavin Hackeling (2014)
If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential...
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Guy S. Handelman, Hong Kuan Kok, Ronil Vikesh Chandra, Amir H. Razavi et al. • Journal of Internal Medicine
(2018)
Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often ...
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Open Access
DOI: https://doi.org/10.1111/joim.12822
Orazymbetov Y, Aitaliyev S, Jakuška P, Veikutienė A et al. • Perfusion
(2026)
PMC
PMC Public Access (View-Only)
DOI: 10.1177/02676591261440347
PMC: PMC13736593
Kostiantyn Fuks • Green, Blue and Digital Economy Journal
(2024)
The purpose of this article is to provide a comprehensive examination of the informational and organisational capabilities of marketing activities in the market for digital products and services. It highlights the importance of data analysi...
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Open Access (DOAJ)
DOI: 10.30525/2661-5169/2024-1-4
Yiming Mao, Ting Li, Wenqiang Lu, Chunhong Li et al. • Discover Oncology
(2025)
Abstract Glioblastoma (GBM) is an extremely aggressive and diverse type of brain tumor that is associated with a poor prognosis. Immunogenic cell death (ICD) is a particular form of cellular demise that is closely linked to antineoplastic i...
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Open Access (DOAJ)
DOI: 10.1007/s12672-025-03557-1