AIMC Topic: Machine Learning

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Personalized Machine Learning-Coupled Nanopillar Triboelectric Pulse Sensor for Cuffless Blood Pressure Continuous Monitoring.

ACS nano
A wearable system that can continuously track the fluctuation of blood pressure (BP) based on pulse signals is highly desirable for the treatments of cardiovascular diseases, yet the sensitivity, reliability, and accuracy remain challenging. Since th...

Identification of High-Reliability Regions of Machine Learning Predictions Based on Materials Chemistry.

Journal of chemical information and modeling
Progress in the application of machine learning (ML) methods to materials design is hindered by the lack of understanding of the reliability of ML predictions, in particular, for the application of ML to small data sets often found in materials scien...

Machine learning-based approach for predicting low birth weight.

BMC pregnancy and childbirth
BACKGROUND: Low birth weight (LBW) has been linked to infant mortality. Predicting LBW is a valuable preventative tool and predictor of newborn health risks. The current study employed a machine learning model to predict LBW.

Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers.

Biochimica et biophysica acta. Reviews on cancer
Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and ar...

Transformer based neural network for daily ground settlement prediction of foundation pit considering spatial correlation.

PloS one
Deep foundation pit settlement prediction based on machine learning is widely used for ensuring the safety of construction, but previous studies are limited to not fully considering the spatial correlation between monitoring points. This paper propos...

Variability analysis of LC-MS experimental factors and their impact on machine learning.

GigaScience
BACKGROUND: Machine learning (ML) technologies, especially deep learning (DL), have gained increasing attention in predictive mass spectrometry (MS) for enhancing the data-processing pipeline from raw data analysis to end-user predictions and rescori...

Multi-Adaptive Optimization for multi-task learning with deep neural networks.

Neural networks : the official journal of the International Neural Network Society
Multi-task learning is a promising paradigm to leverage task interrelations during the training of deep neural networks. A key challenge in the training of multi-task networks is to adequately balance the complementary supervisory signals of multiple...

Phase retrieval for X-ray differential phase contrast radiography with knowledge transfer learning from virtual differential absorption model.

Computers in biology and medicine
Grating-based X-ray phase contrast radiography and computed tomography (CT) are promising modalities for future medical applications. However, the ill-posed phase retrieval problem in X-ray phase contrast imaging has hindered its use for quantitative...

IF-AIP: A machine learning method for the identification of anti-inflammatory peptides using multi-feature fusion strategy.

Computers in biology and medicine
BACKGROUND: The most commonly used therapy currently for inflammatory and autoimmune diseases is nonspecific anti-inflammatory drugs, which have various hazardous side effects. Recently, some anti-inflammatory peptides (AIPs) have been found to be a ...

Challenges in developing cell culture media using machine learning.

Biotechnology advances
Microbial and mammalian cells are widely used in the food, pharmaceutical, and medical industries. Developing or optimizing culture media is essential to improve cell culture performance as a critical technology in cell culture engineering. Methodolo...