AIMC Topic: Humans

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π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing.

Nature communications
Peptide sequencing via tandem mass spectrometry (MS/MS) is essential in proteomics. Unlike traditional database searches, deep learning excels at de novo peptide sequencing, even for peptides missing from existing databases. Current deep learning mod...

Selective diagnostics of Amyotrophic Lateral Sclerosis, Alzheimer's and Parkinson's Diseases with machine learning and miRNA.

Metabolic brain disease
The diagnosis of neurological diseases can be expensive, invasive, and inaccurate, as it is often difficult to distinguish between different types of diseases with similar motor symptoms. However, the dysregulation of miRNAs can be used to create a r...

Deep learning-based discovery of compounds for blood pressure lowering effects.

Scientific reports
The hypotensive side effects caused by drugs during their use have been a vexing issue. Recent studies have found that deep learning can effectively predict the biological activity of compounds by mining patterns and rules in the data, providing a po...

Type 2 diabetes prediction method based on dual-teacher knowledge distillation and feature enhancement.

Scientific reports
Diabetes prediction is an important topic in the field of medical health. Accurate prediction can help early intervention and reduce patients' health risks and medical costs. This paper proposes a data preprocessing method, including removing outlier...

Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine.

Scientific reports
The traditional diagnosis of chronic hepatitis C usually relies on liver biopsy. Diagnosing chronic hepatitis C based on serum indices provides a non-invasive way to determine the stage of chronic hepatitis C without liver biopsy. In this paper, we p...

Drug discovery and mechanism prediction with explainable graph neural networks.

Scientific reports
Apprehension of drug action mechanism is paramount for drug response prediction and precision medicine. The unprecedented development of machine learning and deep learning algorithms has expedited the drug response prediction research. However, exist...

Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions.

Scientific reports
How to utilize modern technological means to provide both accurate scoring and objective feedback for martial arts movements has become an issue that needs to be addressed in the field of physical education. This study proposes an intelligent scoring...

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to purine metabolism-associated genes.

Scientific reports
Sepsis, characterized as a systemic inflammatory response triggered by pathogen invasion, represents a continuum that may progress from mild systemic infection to severe sepsis, potentially culminating in septic shock and multiple organ dysfunction s...

Recurrent models of orientation selectivity enable robust early-vision processing in mixed-signal neuromorphic hardware.

Nature communications
Mixed signal analog/digital neuromorphic circuits represent an ideal medium for reproducing bio-physically realistic dynamics of biological neural systems in real-time. However, similar to their biological counterparts, these circuits have limited re...

Varying pixel resolution significantly improves deep learning-based carotid plaque histology segmentation.

Scientific reports
Carotid plaques-the buildup of cholesterol, calcium, cellular debris, and fibrous tissues in carotid arteries-can rupture, release microemboli into the cerebral vasculature and cause strokes. The likelihood of a plaque rupturing is thought to be asso...