AIMC Topic: Machine Learning

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DeepEGFR a graph neural network for bioactivity classification of EGFR inhibitors.

Scientific reports
Epidermal Growth Factor Receptor (EGFR) plays a critical role in the development of several cancers. Thus, modulation/inhibition of EGFR activity is an appealing target of developing novel cancer therapeutics. With the advent of modern machine learni...

Gene expression signatures from whole blood predict amyotrophic lateral sclerosis case status and survival.

Nature communications
Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurodegenerative disease with a median survival of only 2 to 4 years from diagnosis. Improved tools are needed to shorten diagnostic delays and improve prognostication to benefit clinical care....

Machine learning-driven classification and prognostic prediction of kidney renal clear cell carcinoma using APOBEC family expression signatures.

Scientific reports
Apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like (APOBEC) cytidine deaminases are a highly evolutionarily conserved protein family. Their members are closely associated with DNA damage repair and involved in the genesis and progressio...

Machine learning methods on BioVid heat pain database for pain intensity estimation.

Scientific reports
Pain assessment is a critical aspect of medical practice, directly influencing patient treatment and quality of life. Traditional pain evaluation methods, such as the Numerical Rating Scale (NRS), Visual Analog Scale (VAS), and Verbal Rating Scale (V...

An interpretable machine learning model predicts the interactive and cumulative risks of different environmental chemical exposures on depression.

Translational psychiatry
Humans are exposed to a multitude of environmental chemical mixtures (ECMs) in daily life that may influence depression risk. While prior studies have shown individual ECM exposures to depression, the cumulative and interactive effects of multiple co...

The application of amplitude of low-frequency fluctuations metrics in the diagnosis and prediction of treatment response as well as their associated genes and biological processes in patients with bipolar disorder.

Translational psychiatry
While previous studies have reported functional abnormalities in the prefrontal-limbic-subcortical circuit, the treatment effects on this activity remain unclear. This longitudinal study aimed to investigate spontaneous brain activity in bipolar diso...

Comparative study of coronary artery disease prediction: conventional QRISK3 versus enhanced machine learning models combined with particle swarm optimisation algorithm.

Open heart
BACKGROUND: Coronary artery disease (CAD) is one of the biggest causes of mortality worldwide. Risk stratification for early detection is essential for the primary prevention of CAD. QRISK3 is known to overestimate future CAD risk in some populations...

Detecting Perceived Unfair Treatment Among US College Students Using Mobile Sensing: Pilot Machine Learning Study.

JMIR formative research
BACKGROUND: Experiences of unfair treatment on college campuses are linked to adverse mental and physical health outcomes, highlighting the need for interventions. However, detecting such experiences relies mainly on self-reports. No prior research h...

Discovering sensorimotor agency in cellular automata using diversity search.

Science advances
The field of artificial life studies how life-like phenomena such as agency and self-regulation can self-organize in computer simulations. In cellular automata (CA), a key open question is whether it is possible to find environment rules that self-or...

Prediction of water quality in the middle area of Yangtze River using efficient machine learning model.

Environmental geochemistry and health
The Yangtze River, as the longest river in China and the third-longest in the world, holds immense significance for the country's ecological security and sustainable development. The water quality in its middle reaches directly impacts millions of pe...