Bone Marrow Transplantation - Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT Skip to main content Thank you for visiting nature.com.

To maximise the clinical benefits of machine learning algorithms, we need to rethink our approach to explanation, argue David Watson and colleagues ### Key messages Machine learning algorithms are an ...

Furthermore, machine learning can accurately assess the conditions of patients. With the use of cutting-edge machine learning algorithms, clinical diagnosis, and clinical interventions, along with ...

Rooted in mathematics, the novel machine learning algorithm is called CEBRA (pronounced zebra), and learns the hidden structure in the neural code. What information the CEBRA learns from the raw ...

The fundamental aspect of our analysis focuses on the predictive power of combining different data types, which traditionally include clinical parameters, genetic markers, and demographic and ...

The big genomics data from various aspects (e.g., DNA polymorphism, transcriptomics, and proteomics) is now available in cancer research and clinic application. These (multi-)omics data come with a ...

Furthermore, quantum machine learning algorithms can also be utilized in areas such as cybersecurity, smart manufacturing, and energy management, delivering efficient data analysis and ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes ...

Feb 29, 2024: Machine learning innovations and applications in nanotechnology (Nanowerk Spotlight) Machine Learning in nanotechnology represents a vibrant fusion of artificial intelligence (AI) with ...

Specialization: Machine LearningInstructor: Geena Kim, Assistant Teaching ProfessorPrior knowledge needed: Calculus, Linear algebra, PythonLearning Outcomes Explain what unsupervised learning is, and ...