AIMC Topic: Machine Learning

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Unlocking sustainable growth: exploring the catalytic role of green finance in firms' green total factor productivity.

Environmental science and pollution research international
Promoting the development of green finance (GF) is a critical way to address the environmental and developmental problems in China. While existing studies have examined the macroscopic role of GF, few pay attention to its impact on micro-enterprises....

A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data.

Scientific reports
The emergence of collaborations, which standardize and combine multiple clinical databases across different regions, provide a wealthy source of data, which is fundamental for clinical prediction models, such as patient-level predictions. With the ai...

Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers.

Journal of advanced research
INTRODUCTION: Small-molecule Programmable Cell Death Protein 1/Programmable Death-Ligand 1 (PD1/PDL1) inhibition via PDL1 dimerization has the potential to lead to inexpensive drugs with better cancer patient outcomes and milder side effects. However...

Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
OBJECTIVE: To develop logistic regression nomogram and machine learning (ML)-based models to predict 3-month unfavorable functional outcome for acute ischemic stroke (AIS) patients undergoing reperfusion therapy.

Artificial intelligence and machine learning trends in kidney care.

The American journal of the medical sciences
BACKGROUND: The integration of artificial intelligence (AI) and machine learning (ML) in kidney care has seen a significant rise in recent years. This study specifically analyzed AI and ML research publications related to kidney care to identify lead...

ACPScanner: Prediction of Anticancer Peptides by Integrated Machine Learning Methodologies.

Journal of chemical information and modeling
Novel therapeutic alternatives for cancer treatment are increasingly attracting global research attention. Although chemotherapy remains a primary clinical solution, it often results in significant side effects for patients. In recent years, anticanc...

Extended-wavelength diffuse reflectance spectroscopy dataset of animal tissues for bone-related biomedical applications.

Scientific data
Diffuse reflectance spectroscopy (DRS) has been extensively studied in both preclinical and clinical settings for multiple applications, notably as a minimally invasive diagnostic tool for tissue identification and disease delineation. In this study,...

Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra.

Environmental science & technology
Surface-enhanced Raman spectroscopy (SERS) has been well explored as a highly effective characterization technique that is capable of chemical pollutant detection and identification at very low concentrations. Machine learning has been previously use...

Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.

Science (New York, N.Y.)
We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the...

Generation of tissues outside the field of view (FOV) of radiation therapy simulation imaging based on machine learning and patient body outline (PBO).

Radiation oncology (London, England)
BACKGROUND: It is not unusual to see some parts of tissues are excluded in the field of view of CT simulation images. A typical mitigation is to avoid beams entering the missing body parts at the cost of sub-optimal planning.