AIMC Topic: Machine Learning

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Multiomics Analysis of Disulfidptosis Patterns and Integrated Machine Learning to Predict Immunotherapy Response in Lung Adenocarcinoma.

Current medicinal chemistry
BACKGROUND: Recent studies have unveiled disulfidptosis as a phenomenon intimately associated with cellular damage, heralding new avenues for exploring tumor cell dynamics. We aimed to explore the impact of disulfide cell death on the tumor immune mi...

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion.

Recent advances in inflammation & allergy drug discovery
BACKGROUND: Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising...

[Artificial intelligence in pathological anatomy].

Arkhiv patologii
The review presents key concepts and global developments in the field of artificial intelligence used in pathological anatomy. The work examines two types of artificial intelligence (AI): weak and strong ones. A review of experimental algorithms usin...

Using Artificial Intelligence to Interpret Clinical Flow Cytometry Datasets for Automated Disease Diagnosis and/or Monitoring.

Methods in molecular biology (Clifton, N.J.)
Flow cytometry (FC) is routinely used for hematological disease diagnosis and monitoring. Advancement in this technology allows us to measure an increasing number of markers simultaneously, generating complex high-dimensional datasets. However, curre...

IoT-based incubator monitoring and machine learning powered alarm predictions.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Incubators, especially the ones for babies, require continuous monitoring for anomaly detection and taking action when necessary.

Radiomics and Artificial Intelligence in Renal Lesion Assessment.

Critical reviews in oncogenesis
Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores t...

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes.

Methods in molecular biology (Clifton, N.J.)
The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires larg...

Machine Learning for Biological Design.

Methods in molecular biology (Clifton, N.J.)
We briefly present machine learning approaches for designing better biological experiments. These approaches build on machine learning predictors and provide additional tools to guide scientific discovery. There are two different kinds of objectives ...

Machine-learning-guided Directed Evolution for AAV Capsid Engineering.

Current pharmaceutical design
Target gene delivery is crucial to gene therapy. Adeno-associated virus (AAV) has emerged as a primary gene therapy vector due to its broad host range, long-term expression, and low pathogenicity. However, AAV vectors have some limitations, such as i...