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Showing 20 of 987 academic articles

CViewer: a Java-based statistical framework for integration of shotgun metagenomics with other omics datasets

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...

DOAJ Open Access (DOAJ) DOI: 10.1186/s40168-024-01834-9

A synergistic enhancement of the Ivy algorithm for GAN-based imbalanced classification

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...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-025-32510-z

miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.

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...

DOAJ Open Access (DOAJ) DOI: 10.1371/journal.pone.0258550

Bypassing the Kohn-Sham equations with machine learning

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...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41467-017-00839-3 PMC: 5636838

Machine learning unifies the modeling of materials and molecules

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...

OPENALEX cc-by-nc DOI: https://doi.org/10.1126/sciadv.1701816 PMC: 5729016

A review of machine learning in scheduling

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...

OPENALEX DOI: https://doi.org/10.1109/17.293383

Machine Learning in Orthopedics: A Literature Review

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...

OPENALEX cc-by DOI: https://doi.org/10.3389/fbioe.2018.00075 PMC: 6030383

Machine learning algorithms review

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...

OPENALEX cc-by DOI: https://doi.org/10.54254/2755-2721/4/20230355

Machine Learning in Healthcare Communication

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...

OPENALEX cc-by DOI: https://doi.org/10.3390/encyclopedia1010021

eD octor: machine learning and the future of medicine

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 ...

OPENALEX Open Access DOI: https://doi.org/10.1111/joim.12822

MARKETING ACTIVITIES OF IT COMPANIES: INFORMATION AND ORGANISATIONAL CAPABILITIES FOR DIGITAL PRODUCT DEVELOPMENT

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...

DOAJ Open Access (DOAJ) DOI: 10.30525/2661-5169/2024-1-4

Immunogenic cell death-related macrophage gene model for prognostic prediction in glioblastoma

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...

DOAJ Open Access (DOAJ) DOI: 10.1007/s12672-025-03557-1