AIMC Topic: Machine Learning

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Impact of the oxidative balance score on cardiovascular-kidney-metabolic syndrome: A cross-sectional study with machine learning prediction.

PloS one
BACKGROUND AND AIM: The antioxidant diet and lifestyle are widely believed to prevent and even treat various diseases; however, their applicability to cardiovascular-kidney-metabolic (CKM) syndrome remains unknown. In this study, the correlation betw...

Identifying graft incompatible rootstocks for sweet cherry through machine learning algorithms.

PloS one
Graft incompatibility is a key factor in the development of dwarf and semi dwarf rootstocks for sweet cherry (Prunus avium L.) to improve yield, fruit quality, precocity, and labor efficiency. This study evaluated the graft incompatibility of eight g...

A novel potential biomarker panel to diagnose depression derived from big proteomic data.

Journal of affective disorders
BACKGROUND: There is still no clinical biomarker to diagnose depression. Given the complexity of a multifactorial disease like depression, a single biomarker is unlikely to capture the full heterogeneity of the disease and be applicable in clinical p...

AbDesign: database of point mutants of antibodies with associated structures reveals poor generalization of binding predictions from machine learning models.

mAbs
Antibodies are naturally evolved molecular recognition scaffolds that can bind a variety of surfaces. Their designability is crucial to the development of biologics, with computational methods holding promise in accelerating the delivery of medicines...

A machine learning-based predictive model for multilobar pulmonary consolidation induced by macrolide-resistant pneumonia caused by the 23S rRNA A2063G mutation.

Microbiology spectrum
This study aims to develop a machine learning (ML)-based predictive model for assessing the risk of multilobar pulmonary consolidation in children with macrolide-resistant pneumonia (MRMP) caused by the 23S rRNA A2063G mutation, a subgroup underrepr...

Upgrading Reliability in Molecular Property Prediction by Robust Quantification of Uncertainty from Machine Learning Models.

Journal of chemical information and modeling
Reliable methods to quantify the predictive uncertainty of machine learning (ML) models can significantly increase the impact of molecular property prediction and are routinely used in applications like active learning and ML-guided property optimiza...

RIGR: Resonance-Invariant Graph Representation for Molecular Property Prediction.

Journal of chemical information and modeling
Many successful machine learning models for molecular property prediction rely on Lewis structure representations, commonly encoded as SMILES strings. However, a key limitation arises with molecules exhibiting resonance, where multiple valid Lewis st...

Using Time Dependent Rate Analysis to Evaluate the Quality of Machine Learned Reaction Coordinates for Biasing and Computing Kinetics.

The journal of physical chemistry. B
Having an accurate reaction coordinate (RC) is essential for reliable kinetic characterization of molecular processes, but there are few quantitative metrics to evaluate RC quality. In this study, we consider the dimensionless γ metric from the Expon...

Developing an explainable machine learning model to predict false-negative citrin deficiency cases in newborn screening.

Orphanet journal of rare diseases
BACKGROUND: Neonatal Intrahepatic Cholestasis caused by Citrin Deficiency (NICCD) is an autosomal recessive disorder affecting the urea cycle and energy metabolism. Newborn screening (NBS) usually relies on elevated citrulline, but some patients have...

Understanding cholera dynamics in African countries with persistent outbreaks: a mathematical modeling approach.

BMC public health
BACKGROUND: Cholera, caused by Vibrio cholerae, is a global health challenge, spreading through water in areas lacking clean water and sanitation. Since 2021, the reemergence of cholera cases has increased significantly in endemic regions in Africa. ...