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Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. [PDF]
Br J Radiol, 2023 Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology.
Wei L +8 more
europepmc +2 more sources
Machine Learning (ML) in Medicine: Review, Applications, and Challenges
Mathematics, 2021 Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in ...
Amir Masoud Rahmani +6 more
doaj +2 more sources
Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms. [PDF]
Neural Comput Appl, 2022 This paper proposes an e-diagnosis system based on machine learning (ML) algorithms to be implemented on the Internet of Medical Things (IoMT) environment, particularly for diagnosing diabetes mellitus (type 2 diabetes). However, the ML applications tend
Chang V, Bailey J, Xu QA, Sun Z.
europepmc +2 more sources
Optimisation of Knowledge Management (KM) with Machine Learning (ML) Enabled
Information, 2023 The emergence of artificial intelligence (AI) and its derivative technologies, such as machine learning (ML) and deep learning (DL), heralds a new era of knowledge management (KM) presentation and discovery. KM necessitates ML for improved organisational
Muhammad Anshari +4 more
doaj +2 more sources
ML-AdVInfect: A Machine-Learning Based Adenoviral Infection Predictor [PDF]
Frontiers in Molecular Biosciences, 2021 Adenoviruses (AdVs) constitute a diverse family with many pathogenic types that infect a broad range of hosts. Understanding the pathogenesis of adenoviral infections is not only clinically relevant but also important to elucidate the potential use of ...
Onur Can Karabulut +5 more
doaj +4 more sources
ACL2(ml): Machine-Learning for ACL2 [PDF]
Electronic Proceedings in Theoretical Computer Science, 2014 ACL2(ml) is an extension for the Emacs interface of ACL2. This tool uses machine-learning to help the ACL2 user during the proof-development. Namely, ACL2(ml) gives hints to the user in the form of families of similar theorems, and generates auxiliary ...
Jónathan Heras +1 more
doaj +5 more sources
Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML) [PDF]
Diagnostics, 2021 The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer.
Rima Hajjo +3 more
doaj +2 more sources
Security in Machine Learning (ML) Workflows
International Journal of Computing and EngineeringPurpose: This paper addresses the comprehensive security challenges inherent in the lifecycle of machine learning (ML) systems, including data collection, processing, model training, evaluation, and deployment.
Dinesh Reddy Chittibala +1 more
semanticscholar +2 more sources
Machine Learning at Microsoft with ML .NET [PDF]
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019 Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for ...
Ahmed, Zeeshan +33 more
core +2 more sources
Java-ML: a machine learning library [PDF]
, 2009 Java-ML is a collection of machine learning and data mining algorithms, which aims to be a readily usable and easily extensible API for both software developers and research scientists.
Abeel, Thomas +2 more
core +3 more sources