AIMC Topic: Machine Learning

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Uncertainty Quantification and Flagging of Unreliable Predictions in Predicting Mass Spectrometry-Related Properties of Small Molecules Using Machine Learning.

International journal of molecular sciences
Mass spectral identification (in particular, in metabolomics) can be refined by comparing the observed and predicted properties of molecules, such as chromatographic retention. Significant advancements have been made in predicting these values using ...

Roadmap for the evolution of monitoring: developing and evaluating waveform-based variability-derived artificial intelligence-powered predictive clinical decision support software tools.

Critical care (London, England)
BACKGROUND: Continuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic informati...

Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

Cell communication and signaling : CCS
BACKGROUND: Epidermal growth factor receptor (EGFR) T790M mutation often occurs during long durational erlotinib treatment of non-small cell lung cancer (NSCLC) patients, leading to drug resistance and disease progression. Identification of new selec...

Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.

BMC infectious diseases
BACKGROUND: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predi...

Machine learning classification meets migraine: recommendations for study evaluation.

The journal of headache and pain
The integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classification of migraine types and subtypes. However, inconsistencies in study design, lack of methodolo...

AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer's disease using high-throughput sequencing data.

Scientific reports
AD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing, an enormous amount of AD-associated genomics data is available. However, the information about the involvement of these...

Cell-free plasma telomere length correlated with the risk of cardiovascular events using machine learning classifiers.

Scientific reports
This retrospective study explored the association between circulating cell-free plasma telomere length (cf-TL) and coronary artery disease (CAD) and heart failure (HF). Data from 518 participants were collected, including clinical and laboratory data...

Machine learning approach identifies inflammatory gene signature for predicting survival outcomes in hepatocellular carcinoma.

Scientific reports
BACKGROUND: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, often linked to chronic inflammation. Our study aimed to probe inflammation pathways at the genetic level and pinpoint biomarkers linked to HCC patient ...

Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples.

Scientific data
Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. Successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, requires large amounts of...

Machine Learning-Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm Validation.

Journal of medical Internet research
BACKGROUND: Previous efforts to apply machine learning-based natural language processing to longitudinally collected social media data have shown promise in predicting suicide risk.