AIMC Topic: Machine Learning

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Differentiation of canine and feline neoplasms using multi-modal imaging and machine learning.

Scientific reports
Canine/feline (sub-)cutaneous tumors, which include lipomas, mastocytomas and soft tissue sarcomas, introduce diagnostic challenges due to inherent tissue heterogeneity, accompanied by diverse clinical pathogenesis. Current study integrates conventio...

Prediction of coronary heart disease based on klotho levels using machine learning.

Scientific reports
The diagnostic accuracy for coronary heart disease (CHD) needs to be improved. Some studies have indicated that klotho protein levels upon admission comprise an independent risk factor for CHD and have clinical value for predicting CHD. This study ai...

Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy.

Scientific reports
One possible adverse effect of breast irradiation is the development of pulmonary fibrosis. The aim of this study was to determine whether planning CT scans can predict which patients are more likely to develop lung lesions after treatment. A retrosp...

Identifying shared hub genes in LIRI and MASLD through bioinformatics analysis and machine learning.

Scientific reports
Patients with metabolic dysfunction-associated steatotic liver disease (MASLD) are more susceptible to liver ischemia-reperfusion injury (LIRI), complicating liver surgery outcomes. This study aimed to uncover shared hub genes and mechanisms linking ...

A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis.

Nature communications
Sepsis is a life-threatening organ dysfunction due to a dysfunctional response to infection. Delays in diagnosis have substantial impact on survival. Herein, blood samples from 586 in-house patients with suspected sepsis are used in conjunction with ...

Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study.

Journal of medical Internet research
BACKGROUND: Fetal growth restriction (FGR) is a common complication of preeclampsia. FGR in patients with preeclampsia increases the risk of neonatal-perinatal mortality and morbidity. However, previous prediction methods for FGR are class-biased or ...

Chaperonin containing TCP1 subunit 5 as a novel pan-cancer prognostic biomarker for tumor stemness and immunotherapy response: insights from multi-omics data, integrated machine learning, and experimental validation.

Cancer immunology, immunotherapy : CII
BACKGROUND: Chaperonin containing TCP1 subunit 5 (CCT5), a vital component of the molecular chaperonin complex, has been implicated in tumorigenesis, cancer stemness maintenance, and therapeutic resistance. Nevertheless, its comprehensive roles in pa...

Machine-learning-guided identification of protein secondary structures using spectral and structural descriptors.

Biomaterials science
Interrogation of the secondary structures of proteins is essential for designing and engineering more effective and safer protein-based biomaterials and other classes of theranostic materials. Protein secondary structures are commonly assessed using ...

Development and validation of a CT-based radiomics machine learning model for differentiating immune-related interstitial pneumonia.

International immunopharmacology
INTRODUCTION: Immune checkpoint inhibitor-related interstitial pneumonia (CIP) poses a diagnostic challenge due to its radiographic similarity to other pneumonias. We developed a non-invasive model using CT imaging to differentiate CIP from other pne...

Predicting Membrane Fouling of Submerged Membrane Bioreactor Wastewater Treatment Plants Using Machine Learning.

Environmental science & technology
Membrane fouling remains a significant challenge in the operation of membrane bioreactors (MBRs). Plant operators rely heavily on observations of filtration performance from noisy sensor data to assess membrane fouling conditions and lab-based protoc...