AIMC Topic: Algorithms

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Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions.

Computational and mathematical methods in medicine
Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides...

Application of Improved LSTM Algorithm in Macroeconomic Forecasting.

Computational intelligence and neuroscience
From a macro perspective, futures index of agricultural products can reflect the trend of macroeconomy and can also have an early warning effect on the possible crisis and provide a reference for the government's economic forecast and macro control. ...

Classification of electrocardiogram signals with waveform morphological analysis and support vector machines.

Medical & biological engineering & computing
Electrocardiogram (ECG) indicates the occurrence of various cardiac diseases, and the accurate classification of ECG signals is important for the automatic diagnosis of arrhythmia. This paper presents a novel classification method based on multiple f...

Imputation of sensory properties using deep learning.

Journal of computer-aided molecular design
Predicting the sensory properties of compounds is challenging due to the subjective nature of the experimental measurements. This testing relies on a panel of human participants and is therefore also expensive and time-consuming. We describe the appl...

Heterogeneous treatment effect analysis based on machine-learning methodology.

CPT: pharmacometrics & systems pharmacology
Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment effects for individuals or subgroups in a population. For example, an HTE-informed understanding can critically guide physicians to individualize the medical treatme...

srBERT: automatic article classification model for systematic review using BERT.

Systematic reviews
BACKGROUND: Systematic reviews (SRs) are recognized as reliable evidence, which enables evidence-based medicine to be applied to clinical practice. However, owing to the significant efforts required for an SR, its creation is time-consuming, which of...

-Rung Orthopair Fuzzy Rough Einstein Aggregation Information-Based EDAS Method: Applications in Robotic Agrifarming.

Computational intelligence and neuroscience
The main purpose of this manuscript is to present a novel idea on the -rung orthopair fuzzy rough set (-ROFRS) by the hybridized notion of -ROFRSs and rough sets (RSs) and discuss its basic operations. Furthermore, by utilizing the developed concept,...

Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review.

Langenbeck's archives of surgery
PURPOSE: An indication for surgical therapy includes balancing benefits against risk, which remains a key task in all surgical disciplines. Decisions are oftentimes based on clinical experience while guidelines lack evidence-based background. Various...

The AlphaFold Database of Protein Structures: A Biologist's Guide.

Journal of molecular biology
AlphaFold, the deep learning algorithm developed by DeepMind, recently released the three-dimensional models of the whole human proteome to the scientific community. Here we discuss the advantages, limitations and the still unsolved challenges of the...

Improvement of peripheral nerve visualization using a deep learning-based MR reconstruction algorithm.

Magnetic resonance imaging
OBJECTIVE: To assess a new deep learning-based MR reconstruction method, "DLRecon," for clinical evaluation of peripheral nerves.