AIMC Topic: Machine Learning

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Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics.

Nature communications
The ability to effectively represent microbiome dynamics is a crucial challenge in their quantitative analysis and engineering. By using autoencoder neural networks, we show that microbial growth dynamics can be compressed into low-dimensional repres...

Environmental Impacts of Machine Learning Applications in Protein Science.

Cold Spring Harbor perspectives in biology
Computing tools and machine learning models play an increasingly important role in biology and are now an essential part of discoveries in protein science. The growing energy needs of modern algorithms have raised concerns in the computational scienc...

Perceptions of Data Set Experts on Important Characteristics of Health Data Sets Ready for Machine Learning: A Qualitative Study.

JAMA network open
IMPORTANCE: The lack of data quality frameworks to guide the development of artificial intelligence (AI)-ready data sets limits their usefulness for machine learning (ML) research in health care and hinders the diagnostic excellence of developed clin...

Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
The development of artificial intelligence (AI) within nuclear imaging involves several ethically fraught components at different stages of the machine learning pipeline, including during data collection, model training and validation, and clinical u...

Machine learning approach reveals microbiome, metabolome, and lipidome profiles in type 1 diabetes.

Journal of advanced research
INTRODUCTION: Type 1 diabetes (T1D) is a complex disorder influenced by genetic and environmental factors. The gut microbiome, the serum metabolome, and the serum lipidome have been identified as key environmental factors contributing to the pathophy...

Machine learning-coupled tactile recognition with high spatiotemporal resolution based on cross-striped nanocarbon piezoresistive sensor array.

Biosensors & bioelectronics
Flexible pressure sensor arrays have been playing important roles in various applications of human-machine interface, including robotic tactile sensing, electronic skin, prosthetics, and human-machine interaction. However, it remains challenging to s...

FIT-graph: A multi-grained evolutionary graph based framework for disease diagnosis.

Artificial intelligence in medicine
Early assessment, with the help of machine learning methods, can aid clinicians in optimizing the diagnosis and treatment process, allowing patients to receive critical treatment time. Due to the advantages of effective information organization and i...

Diagnosis knowledge constrained network based on first-order logic for syndrome differentiation.

Artificial intelligence in medicine
Traditional Chinese medicine (TCM) has been recognized worldwide as a valuable asset of human medicine. The procedure of TCM is to treatment based on syndrome differentiation. However, the effect of TCM syndrome differentiation relies heavily on the ...

Exploring classical machine learning for identification of pathological lung auscultations.

Computers in biology and medicine
The use of machine learning in biomedical research has surged in recent years thanks to advances in devices and artificial intelligence. Our aim is to expand this body of knowledge by applying machine learning to pulmonary auscultation signals. Despi...

Enabling pathway design by multiplex experimentation and machine learning.

Metabolic engineering
The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures tha...