AIMC Topic: Machine Learning

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MultiADE: A Multi-domain benchmark for Adverse Drug Event extraction.

Journal of biomedical informatics
OBJECTIVE: Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medical literature, social media and search engine logs. Over the years, many datasets have been created, ...

Machine learning-based prediction of bioactivity in HIV-1 protease: insights from electron density analysis.

Future medicinal chemistry
To develop a model for predicting the biological activity of compounds targeting the HIV-1 protease and to establish factors influencing enzyme inhibition. Machine learning models were built based on a combination of Richard Bader's theory of Atoms ...

Improving arterial stiffness prediction with machine learning utilizing hemodynamics and biomechanical features derived from phase contrast magnetic resonance imaging.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Arterial stiffness has emerged as a prominent marker of risk for cardiovascular diseases. Few studies are interested in predicting symptomatic or asymptomatic arterial stiffness from hemodynamics and biomechanics parameters. Machine learning models c...

Analysis of behavioral flow resolves latent phenotypes.

Nature methods
The accurate detection and quantification of rodent behavior forms a cornerstone of basic biomedical research. Current data-driven approaches, which segment free exploratory behavior into clusters, suffer from low statistical power due to multiple te...

Identification of TXN and F5 as novel diagnostic gene biomarkers of the severe asthma based on bioinformatics and machine learning analysis.

Autoimmunity
Asthma poses a major threat to human health. The aim of this study was to identify genetic markers of severe asthma and analyze the relationship between key genes and immune infiltration. Differentially expressed genes (DEGs) were first screened by d...

Persistence landscapes: Charting a path to unbiased radiological interpretation.

Oncotarget
Persistence landscapes, a sophisticated tool from topological data analysis, offer a promising approach to address biases in radiological interpretation and AI model development. By transforming complex topological features into statistically analyza...

Machine learning model reveals the role of angiogenesis and EMT genes in glioma patient prognosis and immunotherapy.

Biology direct
Gliomas represent a highly aggressive class of tumors located in the brain. Despite the availability of multiple treatment modalities, the prognosis for patients diagnosed with glioma remains unfavorable. Therefore, further exploration of new biomark...

The voice of depression: speech features as biomarkers for major depressive disorder.

BMC psychiatry
BACKGROUND: Psychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patients. This project ai...