AIMC Topic: Machine Learning

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Prediction of Five-Year Survival Rate for Rectal Cancer Using Markov Models of Convolutional Features of RhoB Expression on Tissue Microarray.

IEEE/ACM transactions on computational biology and bioinformatics
The ability to predict survival in cancer is clinically important because the finding can help patients and physicians make optimal treatment decisions. Artificial intelligence in the context of deep learning has been increasingly realized by the inf...

Validating instructional design and predicting student performance in histology education: Using machine learning via virtual microscopy.

Anatomical sciences education
As a part of modern technological environments, virtual microscopy enriches histological learning, with support from large institutional investments. However, existing literature does not supply empirical evidence of its role in improving pedagogy. V...

African bovid tribe classification using transfer learning and computer vision.

Annals of the New York Academy of Sciences
Objective analytical identification methods are still a minority in the praxis of paleobiological sciences. Subjective interpretation of fossils and their modifications remains a nonreplicable expert endeavor. Identification of African bovids is a cr...

Divide-and-conquer the NAS puzzle in resource-constrained federated learning systems.

Neural networks : the official journal of the International Neural Network Society
Federated Learning (FL) is a privacy-preserving distributed machine learning approach geared towards applications in edge devices. However, the problem of designing custom neural architectures in federated environments is not tackled from the perspec...

Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices.

Clinical pharmacology and therapeutics
Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery a...

Bridging the Worlds of Pharmacometrics and Machine Learning.

Clinical pharmacokinetics
Precision medicine requires individualized modeling of disease and drug dynamics, with machine learning-based computational techniques gaining increasing popularity. The complexity of either field, however, makes current pharmacological problems opaq...

Sentiment Analysis of Tweets on Menu Labeling Regulations in the US.

Nutrients
Menu labeling regulations in the United States mandate chain restaurants to display calorie information for standard menu items, intending to facilitate healthy dietary choices and address obesity concerns. For this study, we utilized machine learnin...

Artificial Intelligence in Oncological Hybrid Imaging.

Nuklearmedizin. Nuclear medicine
BACKGROUND:  Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications ...

Revealing Academic Evolution and Frontier Pattern in the Field of Uveitis Using Bibliometric Analysis, Natural Language Processing, and Machine Learning.

Ocular immunology and inflammation
PURPOSE: Numerous uveitis articles were published in this century, underneath which hides valuable intelligence. We aimed to characterize the evolution and patterns in this field.

Artificial intelligence methods in kinase target profiling: Advances and challenges.

Drug discovery today
Kinases have a crucial role in regulating almost the full range of cellular processes, making them essential targets for therapeutic interventions against various diseases. Accurate kinase-profiling prediction is vital for addressing the selectivity/...