AIMC Topic: Machine Learning

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A study of autoencoders as a feature extraction technique for spike sorting.

PloS one
Spike sorting is the process of grouping spikes of distinct neurons into their respective clusters. Most frequently, this grouping is performed by relying on the similarity of features extracted from spike shapes. In spite of recent developments, cur...

An analysis of crude oil prices in the last decade (2011-2020): With deep learning approach.

PloS one
Crude Oil is one of the most important commodities in this world. We have studied the effects of Crude Oil inventories on crude oil prices over the last ten years (2011 to 2020). We tried to figure out how the Crude Oil price variance responds to inv...

The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis.

GigaScience
BACKGROUND: Widespread bioinformatics applications such as drug repositioning or drug-drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research...

The utility of machine learning for predicting donor discard in abdominal transplantation.

Clinical transplantation
BACKGROUND: Increasing access and better allocation of organs in the field of transplantation is a critical problem in clinical care. Limitations exist in accurately predicting allograft discard. Potential exists for machine learning to provide a bal...

A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction.

Sensors (Basel, Switzerland)
In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A furth...

On the Best Way to Cluster NCI-60 Molecules.

Biomolecules
Machine learning-based models have been widely used in the early drug-design pipeline. To validate these models, cross-validation strategies have been employed, including those using clustering of molecules in terms of their chemical structures. Howe...

ECG signal feature extraction trends in methods and applications.

Biomedical engineering online
Signal analysis is a domain which is an amalgamation of different processes coming together to form robust pipelines for the automation of data analysis. When applied to the medical world, physiological signals are used. It is becoming increasingly c...

A pharmacokinetic model based on the SSA-1DCNN-Attention method.

Journal of bioinformatics and computational biology
To solve the problem of the lack of representativeness of the training set and the poor prediction accuracy due to the limited number of training samples when the machine learning method is used for the classification and prediction of pharmacokineti...

Hybrid Generalized Regularized Extreme Learning Machine Through Gradient-Based Optimizer Model for Self-Cleansing Nondeposition with Clean Bed Mode of Sediment Transport.

Big data
Sediment transport modeling is an important problem to minimize sedimentation in open channels that could lead to unexpected operation expenses. From an engineering perspective, the development of accurate models based on effective variables involved...

Quantification of tumorsphere migration with a physics-based machine learning method.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Current analysis techniques available for migration assays only provide quantitative measurements for overall migration. However, the potential of regional migration analyses can open further insight into migration patterns and more avenues of experi...