AIMC Topic: Machine Learning

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Integrating bioassay and machine learning data for ecological risk assessments of herbicide use on Ulva australis.

Marine pollution bulletin
Herbicide contamination of aquatic ecosystems poses a critical risk to biodiversity. Bioassays provide useful ecological insights on responses to herbicides; however, they require a model organism. Ulva australis is an ideal candidate for herbicide t...

A Novel Linear Machine Learning Method Based on DNA Hybridization Reaction Circuit.

IEEE transactions on nanobioscience
DNA hybridization reaction is a significant technology in the field of semi-synthetic biology and holds great potential for use in biological computation. In this study, we propose a novel machine learning model based on a DNA hybridization reaction ...

MACHINE LEARNING AND BIOINFORMATICS TO IDENTIFY COAGULATION BIOMARKERS IN SEPSIS-RELATED KIDNEY INJURY.

Shock (Augusta, Ga.)
Background: Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication with mortality rates exceeding 50%, yet its molecular drivers remain poorly defined. Dysregulated coagulation is increasingly implicated in SA-AKI pathogene...

Wet and dry cough classification using cough sound characteristics and machine learning: A systematic review.

International journal of medical informatics
BACKGROUND: Distinguishing between productive (wet) and non-productive (dry) cough types is important for evaluating respiratory health, assisting in differential diagnosis, and monitoring disease progression. However, assessing cough type through th...

Systemic coagulation-inflammation index in the prediction of ISR in patients undergoing drug-eluting stents implant: A retrospective study based on multiple machine learning methods.

International journal of cardiology
BACKGROUND: The Systemic Coagulation-Inflammation index (SCI) is an innovative hematological metric that accurately reflects both coagulopathic and inflammatory dynamics. In this paper, the objective of this paper is to explain the prognostic impact ...

Machine learning and statistical classification in CRISPR-Cas12a diagnostic assays.

Biosensors & bioelectronics
CRISPR-based diagnostics have gained increasing attention as biosensing tools able to address limitations in contemporary molecular diagnostic tests. To maximize the performance of CRISPR-based assays, much effort has focused on optimizing the chemis...

Comparing logistic regression and machine learning for obesity risk prediction: A systematic review and meta-analysis.

International journal of medical informatics
BACKGROUND: Logistic regression (LR) has traditionally been the standard method used for predicting binary health outcomes; however, machine learning (ML) methods are increasingly popular.

Ethical aspects and user preferences in applying machine learning to adjust eHealth addressing substance use: A mixed-methods study.

International journal of medical informatics
BACKGROUND: Digital health interventions targeting substance use disorders are being increasingly implemented. Data science methodology has the potential to enhance involvement and efficacy of these interventions, though application may raise ethical...

Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma.

International journal of medical informatics
BACKGROUND: In Pancreatic Ductal Adenocarcinoma (PDAC), current prognostic scores are unable to fully capture the biological heterogeneity of the disease. While some approaches investigating the role of multi-omics in PDAC are emerging, the analysis ...

MIP-based electrochemical sensor with machine learning for accurate ZIKV detection in protein- and glucose-rich urine.

Analytical biochemistry
Nowadays, a multitude of biosensors are being developed worldwide. However, a significant challenge arises when these biosensors are tested in real sample environments, as many of them fail to perform as expected. This can lead to ambiguous results a...