AIMC Topic: Models, Theoretical

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Manganese (Mn) removal prediction using extreme gradient model.

Ecotoxicology and environmental safety
Exploring the Manganese (Mn) removal prediction with several independent variables is tremendously critical and indispensable to understand the pattern of removal process. Mn is one of the key heavy metals (HMs) stipulated by the WHO for the developm...

Enhancing the interpretability of transcription factor binding site prediction using attention mechanism.

Scientific reports
Transcription factors (TFs) regulate the gene expression of their target genes by binding to the regulatory sequences of target genes (e.g., promoters and enhancers). To fully understand gene regulatory mechanisms, it is crucial to decipher the relat...

Predicting Alzheimer's disease progression using deep recurrent neural networks.

NeuroImage
Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more t...

PSA-based machine learning model improves prostate cancer risk stratification in a screening population.

World journal of urology
CONTEXT: The majority of prostate cancer diagnoses are facilitated by testing serum Prostate Specific Antigen (PSA) levels. Despite this, there are limitations to the diagnostic accuracy of PSA. Consideration of patient demographic factors and bioche...

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology.

Clinical pharmacology and therapeutics
The amount of "big" data generated in clinical oncology, whether from molecular, imaging, pharmacological, or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive a...

A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns.

Molecules (Basel, Switzerland)
In this study, a novel hybrid surrogate machine learning model based on a feedforward neural network (FNN) and one step secant algorithm (OSS) was developed to predict the load-bearing capacity of concrete-filled steel tube columns (CFST), whereas th...

Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients.

PloS one
Opioids play a critical role in acute postoperative pain management. Our objective was to develop machine learning models to predict postoperative opioid requirements in patients undergoing ambulatory surgery. To develop the models, we used a periope...

Application of a new HMW framework derived ANN model for optimization of aquatic dissolved organic matter removal by coagulation.

Chemosphere
Removing dissolved organic matter (DOM) with polyaluminium chloride is one of the primary goals of drinking water treatment. In this study, a new HMW framework was proposed, which divided the factors affecting coagulation into three parts consisting ...

A lobster-inspired bending module for compliant robotic applications.

Bioinspiration & biomimetics
Ideally, robots may be designed to adapt to different tasks such as heavy lifting and handling delicate objects, in which the requirements in force compliance and position accuracy vary dramatically. While conventional rigid actuators are usually cha...

Strategies for Testing Intervention Matching Schemes in Cancer.

Clinical pharmacology and therapeutics
Personalized medicine, or the tailoring of health interventions to an individual's nuanced and often unique genetic, biochemical, physiological, behavioral, and/or exposure profile, is seen by many as a biological necessity given the great heterogene...