AIMC Topic: Machine Learning

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Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data.

Surgical endoscopy
BACKGROUND: Personalized medicine requires the integration and analysis of vast amounts of patient data to realize individualized care. With Surgomics, we aim to facilitate personalized therapy recommendations in surgery by integration of intraoperat...

An interpretable machine learning model for selectivity of small-molecules against homologous protein family.

Future medicinal chemistry
In the early stages of drug discovery, various experimental and computational methods are used to measure the specificity of small molecules against a target protein. The selectivity of small molecules remains a challenge leading to off-target side ...

Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism.

Tomography (Ann Arbor, Mich.)
Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective:...

Toward Mental Effort Measurement Using Electrodermal Activity Features.

Sensors (Basel, Switzerland)
The ability to monitor mental effort during a task using a wearable sensor may improve productivity for both work and study. The use of the electrodermal activity (EDA) signal for tracking mental effort is an emerging area of research. Through analys...

Early Prediction of Diabetes Using an Ensemble of Machine Learning Models.

International journal of environmental research and public health
Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an incr...

A novel hybrid framework for metabolic pathways prediction based on the graph attention network.

BMC bioinformatics
BACKGROUND: Making clear what kinds of metabolic pathways a drug compound involves in can help researchers understand how the drug is absorbed, distributed, metabolized, and excreted. The characteristics of a compound such as structure, composition a...

Outbound Data Legality Analysis in CPTPP Countries under the Environment of Cross-Border Data Flow Governance.

Journal of environmental and public health
The governance of cross-border data flows around the digital economy, data security, and data sovereignty has become a crucial global governance issue. This paper evaluates the legitimacy of data exit rules of CPTPP countries based on machine learnin...

Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms.

Journal of environmental and public health
Applicability of statistical models in predicting chlorine decay remains minimally explored. This study predicted residual chlorine using six deep learning and nine machine learning techniques. Suitability of multimodel ensembles (MMEs) including ari...

Deep Learning-Based Classification of Spoken English Digits.

Computational intelligence and neuroscience
Classification of isolated digits is the basic challenge for many speech classification systems. While a lot of work has been carried out on spoken languages, only limited research work on spoken English digit data has been reported in the literature...

Producing chemically accurate atomic Gaussian process regression models by active learning for molecular simulation.

Journal of computational chemistry
Machine learning is becoming increasingly more important in the field of force field development. Never has it been more vital to have chemically accurate machine learning potentials because force fields become more sophisticated and their applicatio...