AIMC Topic: Machine Learning

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Image learning to accurately identify complex mixture components.

The Analyst
The study of complex mixtures is very important for exploring the evolution of natural phenomena, but the complexity of the mixtures greatly increases the difficulty of material information extraction. Image perception-based machine-learning techniqu...

XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers.

PLoS computational biology
Graph representations are traditionally used to represent protein structures in sequence design protocols in which the protein backbone conformation is known. This infrequently extends to machine learning projects: existing graph convolution algorith...

Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.

PloS one
Identification of medical conditions using claims data is generally conducted with algorithms based on subject-matter knowledge. However, these claims-based algorithms (CBAs) are highly dependent on the knowledge level and not necessarily optimized f...

Web-based and machine learning approaches for identification of patient-reported outcomes in inflammatory bowel disease.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Messages from an Internet forum are raw material that emerges in a natural setting (i.e., non-induced by a research situation).

Explainable artificial intelligence for pharmacovigilance: What features are important when predicting adverse outcomes?

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Explainable Artificial Intelligence (XAI) has been identified as a viable method for determining the importance of features when making predictions using Machine Learning (ML) models. In this study, we created models that ta...

Machine Learning: An Overview and Applications in Pharmacogenetics.

Genes
This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the de...

Improving Ponzi Scheme Contract Detection Using Multi-Channel TextCNN and Transformer.

Sensors (Basel, Switzerland)
With the development of blockchain technologies, many Ponzi schemes disguise themselves under the veil of smart contracts. The Ponzi scheme contracts cause serious financial losses, which has a bad effect on the blockchain. Existing Ponzi scheme cont...

Machine Learning to Predict Outcomes and Cost by Phase of Care After Coronary Artery Bypass Grafting.

The Annals of thoracic surgery
BACKGROUND: Machine learning may enhance prediction of outcomes after coronary artery bypass grafting (CABG). We sought to develop and validate a dynamic machine learning model to predict CABG outcomes at clinically relevant pre- and postoperative ti...

4mC-RF: Improving the prediction of 4mC sites using composition and position relative features and statistical moment.

Analytical biochemistry
N4-methylcytosine (4 mC) is an important epigenetic modification that occurs enzymatically by the action of DNA methyltransferases. 4 mC sites exist in prokaryotes and eukaryotes while playing a vital role in regulating gene expression, DNA replicati...

Development and validation of multiple machine learning algorithms for the classification of G-protein-coupled receptors using molecular evolution model-based feature extraction strategy.

Amino acids
Machine learning is one of the most potential ways to realize the function prediction of the incremental large-scale G-protein-coupled receptors (GPCR). Prior research reveals that the key to determining the overall classification accuracy of GPCR is...