AIMC Topic: Machine Learning

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A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides.

Molecules (Basel, Switzerland)
Food protein-derived antihypertensive peptides are a representative type of bioactive peptides. Several models based on partial least squares regression have been constructed to delineate the relationship between the structure and activity of the pep...

Machine learning based canine posture estimation using inertial data.

PloS one
The aim of this study was to design a new canine posture estimation system specifically for working dogs. The system was composed of Inertial Measurement Units (IMUs) that are commercially available, and a supervised learning algorithm which was deve...

Interaction of systemic drugs causing ocular toxicity with organic cation transporter: an artificial intelligence prediction.

Journal of biomolecular structure & dynamics
Chronic disease patients (cancer, arthritis, cardiovascular diseases) undergo long-term systemic drug treatment. Membrane transporters in ocular barriers could falsely recognize these drugs and allow their trafficking into the eye from systemic circu...

The synergy of cybernetical intelligence with medical image analysis for deep medicine: A methodological perspective.

Computer methods and programs in biomedicine
CONCEPTUAL INTRODUCTION: To introduce the concept of cybernetical intelligence, deep learning, development history, international research, algorithms, and the application of these models in smart medical image analysis and deep medicine are reviewed...

An open competition involving thousands of competitors failed to construct useful abstract classifiers for new diagnostic test accuracy systematic reviews.

Research synthesis methods
There are currently no abstract classifiers, which can be used for new diagnostic test accuracy (DTA) systematic reviews to select primary DTA study abstracts from database searches. Our goal was to develop machine-learning-based abstract classifiers...

Developing a User-Friendly Code for the Fast Estimation of Well-Behaved Real-Space Partial Charges.

Journal of chemical information and modeling
The Quantum Theory of Atoms in Molecules (QTAIM) provides an intuitive, yet physically sound, strategy to determine the partial charges of any chemical system relying on the topology induced by the electron density ρ() . In a previous work [ , , 0141...

Characterizing Uncertainty in Machine Learning for Chemistry.

Journal of chemical information and modeling
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the d...

PCRMLP: A Two-Stage Network for Point Cloud Registration in Urban Scenes.

Sensors (Basel, Switzerland)
Point cloud registration plays a crucial role in 3D mapping and localization. Urban scene point clouds pose significant challenges for registration due to their large data volume, similar scenarios, and dynamic objects. Estimating the location by ins...

Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review.

Sensors (Basel, Switzerland)
The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing de...

Performance of active learning models for screening prioritization in systematic reviews: a simulation study into the Average Time to Discover relevant records.

Systematic reviews
BACKGROUND: Conducting a systematic review demands a significant amount of effort in screening titles and abstracts. To accelerate this process, various tools that utilize active learning have been proposed. These tools allow the reviewer to interact...