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Causal machine learning for predicting treatment outcomes

Nature Medicine
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs.
S. Feuerriegel   +9 more
semanticscholar   +1 more source

Flink-ML: machine learning in Apache Flink

Brazilian Journal of Technology
The emergence of Big Data has spurred the development of various frameworks designed for efficient data storage and processing. Key frameworks include Hadoop, Spark, Flink, Storm, Pig, and Zookeeper. Among these, Apache Flink stands out as a prominent open-source platform known for its powerful stream and batch processing capabilities.
Messaoud Mezati, Ines Aouria
openaire   +1 more source

Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach

Medical and Biological Engineering and Computing, 2020
N. Mahmood   +5 more
semanticscholar   +1 more source

Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations

Nature Machine Intelligence
One of the most promising applications of machine learning in computational physics is to accelerate the solution of partial differential equations (PDEs).
Nick McGreivy, Ammar Hakim
semanticscholar   +1 more source

Machine learning for microbiologists

Nature Reviews Microbiology, 2023
Francesco Asnicar   +2 more
exaly  

A guide to machine learning for biologists

Nature Reviews Molecular Cell Biology, 2021
Joe G Greener   +2 more
exaly  

Machine learning methods to model multicellular complexity and tissue specificity

Nature Reviews Materials, 2021
Aaron K Wong, Olga G Troyanskaya
exaly  

Machine Learning (ML) and Toxicity Studies

2022
Shyamasree Ghosh, Rathi Dasgupta
openaire   +1 more source

Machine learning sheds light on microbial dark proteins

Nature Reviews Microbiology, 2023
Aaron T Hammack, Crysten E Blaby-Haas
exaly  

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