AIMC Topic: Machine Learning

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Multidependency Graph Convolutional Networks and Contrastive Learning for Drug Repositioning.

Journal of chemical information and modeling
The goal of drug repositioning is to expedite the drug development process by finding novel therapeutic applications for approved drugs. Using multifeature learning, different computational drug repositioning techniques have recently been introduced ...

Engineering highly active nuclease enzymes with machine learning and high-throughput screening.

Cell systems
Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization is often hindered by a rugged fitness landscape and costly experiments. In this work, we present TeleProt, a machine learning (ML) ...

FlyVISTA, an integrated machine learning platform for deep phenotyping of sleep in .

Science advances
There is great interest in using genetically tractable organisms such as to gain insights into the regulation and function of sleep. However, sleep phenotyping in has largely relied on simple measures of locomotor inactivity. Here, we present FlyVI...

Ensemble-Based Model-Agnostic Meta-Learning with Operational Grouping for Intelligent Sensory Systems.

Sensors (Basel, Switzerland)
Model-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in assembly lines, where rapid and accurate fault classification of arms is essential. Despite gaining sig...

Utilization of Machine Learning in the Prediction, Diagnosis, Prognosis, and Management of Chronic Myeloid Leukemia.

International journal of molecular sciences
Chronic myeloid leukemia is a clonal hematologic disease characterized by the presence of the Philadelphia chromosome and the BCR::ABL1 fusion protein. Integrating different molecular, genetic, clinical, and laboratory data would improve the diagnost...

Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies.

Cancer imaging : the official publication of the International Cancer Imaging Society
PURPOSE: This study aimed to select robust features against lung motion in a phantom study and use them as input to feature selection algorithms and machine learning classifiers in a clinical study to predict the lymphovascular invasion (LVI) of non-...

An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening patients who may benefit from immunotherapy. Here, we developed and validated an interpretable machine l...

Modifiable risk factors of vaccine hesitancy: insights from a mixed methods multiple population study combining machine learning and thematic analysis during the COVID-19 pandemic.

BMC medicine
BACKGROUND: Vaccine hesitancy, the delay in acceptance or reluctance to vaccinate, ranks among the top threats to global health. Identifying modifiable factors contributing to vaccine hesitancy is crucial for developing targeted interventions to incr...

Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors.

BMC health services research
OBJECTIVE: Predicting healthcare demand is essential for effective resource allocation and planning. This study applies Andersen's Behavioral Model of Health Services Use, focusing on predisposing, enabling, and need factors, using data from the 2022...

Application of the LDA model to identify topics in telemedicine conversations on the X social network.

BMC health services research
The evolution experienced by global society, in the post-COVID 19 era, is marked by the quite obligatory use of digital media in many sectors, as is the case for the health sector. Quite frequently, both patients and health professionals use social m...