AIMC Topic: Machine Learning

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Machine-learning-powered extraction of molecular diffusivity from single-molecule images for super-resolution mapping.

Communications biology
While critical to biological processes, molecular diffusion is difficult to quantify, and spatial mapping of local diffusivity is even more challenging. Here we report a machine-learning-enabled approach, pixels-to-diffusivity (Pix2D), to directly ex...

A compact review of progress and prospects of deep learning in drug discovery.

Journal of molecular modeling
BACKGROUND: Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer...

Data drift in medical machine learning: implications and potential remedies.

The British journal of radiology
Data drift refers to differences between the data used in training a machine learning (ML) model and that applied to the model in real-world operation. Medical ML systems can be exposed to various forms of data drift, including differences between th...

Online continual learning with declarative memory.

Neural networks : the official journal of the International Neural Network Society
Deep neural networks are enjoying unprecedented attention and success in recent years. However, catastrophic forgetting undermines the performance of deep models when the training data are arrived sequentially in an online multi-task learning fashion...

An octonion-based nonlinear echo state network for speech emotion recognition in Metaverse.

Neural networks : the official journal of the International Neural Network Society
While the Metaverse is becoming a popular trend and drawing much attention from academia, society, and businesses, processing cores used in its infrastructures need to be improved, particularly in terms of signal processing and pattern recognition. A...

MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction.

Journal of chemical information and modeling
The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from availa...

Cell type-specific interpretation of noncoding variants using deep learning-based methods.

GigaScience
Interpretation of noncoding genomic variants is one of the most important challenges in human genetics. Machine learning methods have emerged recently as a powerful tool to solve this problem. State-of-the-art approaches allow prediction of transcrip...

Robust deep learning object recognition models rely on low frequency information in natural images.

PLoS computational biology
Machine learning models have difficulty generalizing to data outside of the distribution they were trained on. In particular, vision models are usually vulnerable to adversarial attacks or common corruptions, to which the human visual system is robus...

Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review.

Critical reviews in analytical chemistry
The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recentl...

Deep Learning-Based Modeling of Drug-Target Interaction Prediction Incorporating Binding Site Information of Proteins.

Interdisciplinary sciences, computational life sciences
Chemogenomics, also known as proteochemometrics, covers various computational methods for predicting interactions between related drugs and targets on large-scale data. Chemogenomics is used in the early stages of drug discovery to predict the off-ta...