AIMC Topic: Machine Learning

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A review on intelligent impedance cytometry systems: Development, applications and advances.

Analytica chimica acta
Impedance cytometry is a well-established technique for counting and analyzing single cells, with several advantages, such as convenience, high throughput, and no labeling required. A typical experiment consists of the following steps: single-cell me...

Machine Learning and Electroencephalogram Signal based Diagnosis of Dipression.

Neuroscience letters
Depression is a psychological condition which hampers day to day activity (Thinking, Feeling or Action). The early detection of this illness will help to save many lives because it is now recognized as a global problem which could even lead to suicid...

Residue-level error detection in cryoelectron microscopy models.

Structure (London, England : 1993)
Building accurate protein models into moderate resolution (3-5 Å) cryoelectron microscopy (cryo-EM) maps is challenging and error prone. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model that identifies local back...

Machine learning in rare disease.

Nature methods
High-throughput profiling methods (such as genomics or imaging) have accelerated basic research and made deep molecular characterization of patient samples routine. These approaches provide a rich portrait of genes, molecular pathways and cell types ...

Machine learning in medical research.

Tidsskrift for den Norske laegeforening : tidsskrift for praktisk medicin, ny raekke

Genomic and machine learning-based screening of aquaculture-associated introgression into at-risk wild North American Atlantic salmon (Salmo salar) populations.

Molecular ecology resources
The negative genetic impacts of gene flow from domestic to wild populations can be dependent on the degree of domestication and exacerbated by the magnitude of pre-existing genetic differences between wild populations and the domestication source. Re...

Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.

Physical and engineering sciences in medicine
Parkinson's disease (PD) is characterized by slowed movements, speech disorders, an inability to control muscle movements, and tremors in the hands and feet. In the early stages of PD, the changes in these motor signs are very vague, so an objective ...

In the Age of Machine Learning Cryo-EM Research is Still Necessary: A Path toward Precision Medicine.

Advanced biology
Machine learning has proven useful in analyzing complex biological data and has greatly influenced the course of research in structural biology and precision medicine. Deep neural network models oftentimes fail to predict the structure of complex pro...

Artificial intelligence in vascular surgical decision making.

Seminars in vascular surgery
Despite advances in prevention, detection, and treatment, cardiovascular disease is a leading cause of mortality and represents a major health problem worldwide. Artificial intelligence and machine learning have brought new insights to the management...

VLAD: Task-agnostic VAE-based lifelong anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Lifelong learning represents an emerging machine learning paradigm that aims at designing new methods providing accurate analyses in complex and dynamic real-world environments. Although a significant amount of research has been conducted in image cl...