AIMC Topic: Machine Learning

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Drug grouping learning for improving evidence-based treatment recommendations.

Computers in biology and medicine
Clinical practice guidelines (CPGs) are essential tools that facilitate the translation of the growing body of scientific evidence into clinical practice by providing clinicians with evidence-based recommendations. The first step of CPG development i...

Adsorption Energy Prediction Model for CO Reduction on Electrocatalysts Containing Previously Unencountered Metal Elements.

Journal of chemical information and modeling
Electrochemical carbon dioxide reduction (CORR) using electrocatalysts has gained attention for its potential to convert atmospheric CO into value-added chemicals. Recently, machine learning (ML) has emerged as a promising approach for catalyst devel...

Machine Learning for the Prediction of Size and Encapsulation Efficiency of mRNA-Loaded Lipid Nanoparticles Following a Postencapsulation Approach.

ACS applied bio materials
Lipid nanoparticles (LNPs) have gained significant attention thanks to their ability to encapsulate and deliver mRNA. Exploring a variety of lipid compositions and different preparation processes is essential for a better understanding of the mRNA en...

Entropy-driven signal amplification integrated with machine learning in multiplex lateral flow immunoassay for sensitive Point-of-Care colon cancer diagnosis.

Journal of nanobiotechnology
Investigations on epithelial-mesenchymal transition (EMT) events occurring on circulating tumor cells (CTCs) are poised to significantly advance nanoliquid biopsy methodologies. This study presented a colorimetric multiplex lateral flow immunoassay s...

Secure federated transfer learning with enhanced secure multiparty computation for privacy preserving smart EHR systems.

Scientific reports
Federated Learning and Artificial Intelligence (AI) are two most intriguing and leading technologies in the intelligent healthcare business. Data must be collected, stored and analyzed from various companies. Patient data processing, particularly in ...

Fast and accurate visual acuity prediction based on optical aberrations and machine learning.

Scientific reports
In this work, we propose three machine learning-based methods for predicting visual acuity (VA). Two methods utilize regression trees (LSBoost and XGBoost), and the third employs a neural network that classifies simulated aberrated optotypes as "reco...

Non-invasive anemia detection from conjunctiva and sclera images using vision transformer with attention map explainability.

Scientific reports
Iron-deficiency anemia, a prevalent global health issue, traditionally requires invasive procedures for accurate diagnosis, such as a blood sample for measuring hemoglobin (Hgb) concentration. Nevertheless, this marker can be visually assessed by obs...

Advances in machine learning-enhanced microfluidic cell sorting.

Science advances
Cell sorting, essential for diagnostics and early intervention, has evolved from conventional methods to sophisticated microfluidic approaches. These miniaturized systems leverage precise hydrodynamic control, facilitating major advances in tumor cel...

In silico-driven protocol for hit-to-lead optimization: a case study on PDE9A inhibitors.

Journal of computer-aided molecular design
Hit-to-lead (H2L) optimization is a critical stage in small-molecule drug discovery, where efficient exploration of chemical space is required to identify promising lead compounds. Conventional H2L workflows rely on iterative synthesis and experiment...

Application of risk prediction model to evaluate the effect of mechanical ventilation on postoperative pulmonary complications in thoracic surgery.

European journal of medical research
OBJECTIVE: This retrospective cohort study aimed to systematically evaluate the impact of different mechanical ventilation strategies on postoperative pulmonary complications (PPCs) in thoracic surgery and to establish a risk prediction model that fa...