AIMC Topic: Algorithms

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Using a Dual-Input Convolutional Neural Network for Automated Detection of Pediatric Supracondylar Fracture on Conventional Radiography.

Investigative radiology
OBJECTIVES: This study aimed to develop a dual-input convolutional neural network (CNN)-based deep-learning algorithm that utilizes both anteroposterior (AP) and lateral elbow radiographs for the automated detection of pediatric supracondylar fractur...

Can Machine Learning Improve Screening for Targeted Delinquency Prevention Programs?

Prevention science : the official journal of the Society for Prevention Research
The cost-effectiveness of targeted delinquency prevention programs for children depends on the accuracy of the screening process. Screening accuracy is often poor, resulting in wasted resources and missed opportunities to avert negative outcomes. Thi...

An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning.

Neurosurgery
BACKGROUND: Although survival statistics in patients with glioblastoma multiforme (GBM) are well-defined at the group level, predicting individual patient survival remains challenging because of significant variation within strata.

PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins.

Bioinformatics (Oxford, England)
MOTIVATION: Gram-positive bacteria have developed secretion systems to transport proteins across their cell wall, a process that plays an important role during host infection. These secretion mechanisms have also been harnessed for therapeutic purpos...

Machine learning and its applications in plant molecular studies.

Briefings in functional genomics
The advent of high-throughput genomic technologies has resulted in the accumulation of massive amounts of genomic information. However, biologists are challenged with how to effectively analyze these data. Machine learning can provide tools for bette...

Artificial intelligence-based multi-objective optimization protocol for protein structure refinement.

Bioinformatics (Oxford, England)
MOTIVATION: Protein structure refinement is an important step of protein structure prediction. Existing approaches have generally used a single scoring function combined with Monte Carlo method or Molecular Dynamics algorithm. The one-dimension optim...

Machine Learning Models Improve the Diagnostic Yield of Peripheral Blood Flow Cytometry.

American journal of clinical pathology
OBJECTIVES: Peripheral blood flow cytometry (PBFC) is useful for evaluating circulating hematologic malignancies (HM) but has limited diagnostic value for screening. We used machine learning to evaluate whether clinical history and CBC/differential p...

Human Locomotion Classification for Different Terrains Using Machine Learning Techniques.

Critical reviews in biomedical engineering
Gait analysis on healthy subjects was performed based on surface electromyographic and acceleration sensor signal, implemented through machine learning approaches. The surface EMG and 3-axes acceleration signals have been acquired for 5 different ter...

Evaluation of genotoxicity after acute and chronic exposure to 2,4-dichlorophenoxyacetic acid herbicide (2,4-D) in rodents using machine learning algorithms.

The Journal of toxicological sciences
2,4-Dichlorophenoxyacetic acid (2,4-D) is one of the most widely used herbicides in the world, but its mutagenic and carcinogenic potential is still controversial. We simulated environmental exposure to 2,4-D, with the objective of evaluating the gen...