AIMC Topic: Machine Learning

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Ensemble intelligence prediction algorithms and land use scenarios to measure carbon emissions of the Yangtze River Delta: A machine learning model based on Long Short-Term Memory.

PloS one
Land use in urban agglomerations is the main source of carbon emissions, and reducing them and improving land use efficiency are the keys to achieving sustainable development goals (SDGs). To advance the literature on densely populated cities and hig...

Frequency-adjusted borders ordinal forest: A novel tree ensemble method for ordinal prediction.

The British journal of mathematical and statistical psychology
Ordinal responses commonly occur in psychology, e.g., through school grades or rating scales. Where traditionally parametric statistical models like the proportional odds model have been used, machine learning (ML) methods such as random forest (RF) ...

Effective analysis of thyroid toxicity and mechanisms of acetyltributyl citrate using network toxicology, molecular docking, and machine learning strategies.

Toxicology
The growing prevalence of environmental pollutants has raised concerns about their potential role in thyroid dysfunction and related disorders. Previous research suggests that various chemicals, including plasticizers like acetyl tributyl citrate (AT...

Machine learning-based prognostic prediction and surgical guidance for intrahepatic cholangiocarcinoma.

Bioscience trends
The prognosis following radical surgery for intrahepatic cholangiocarcinoma (ICC) is poor, and optimal follow-up strategies remain unclear, with ongoing debates regarding anatomic resection (AR) versus non-anatomic resection (NAR). This study include...

Applications of and issues with machine learning in medicine: Bridging the gap with explainable AI.

Bioscience trends
In recent years, machine learning, and particularly deep learning, has shown remarkable potential in various fields, including medicine. Advanced techniques like convolutional neural networks and transformers have enabled high-performance predictions...

FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.

Molecular pharmaceutics
The Biopharmaceutics Classification System (BCS) has facilitated biowaivers and played a significant role in enhancing drug regulation and development efficiency. However, the productivity of measuring the key discriminative properties of BCS, solubi...

Unraveling pathogenesis and potential biomarkers for autism spectrum disorder associated with HIF1A pathway based on machine learning and experiment validation.

Neurobiology of disease
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a high social burden and limited treatments. Hypoxic condition of the brain is considered an important pathological mechanism of ASD. HIF1A is a key participant in brain...

Machine-Learning-Based Spectral Modeling: A Biomimetic Guide for Enhancing Esthetics.

Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]
OBJECTIVE: To provide guidelines and means for optimal coverage and distribution of computer models with 1-10 clusters, designed based on an in vivo extensive dental colorimetric database and compare the findings with some reputable shade guides.

Optimizing critical quality attributes of fast disintegrating tablets using artificial neural networks: a scientific benchmark study.

Drug development and industrial pharmacy
OBJECTIVE: The objective of this study is to create predictive models utilizing machine learning algorithms, including Artificial Neural Networks (ANN), k-nearest neighbor (kNN), support vector machines (SVM), and linear regression, to predict critic...