AIMC Topic: Machine Learning

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Feature Selection and Dwarf Mongoose Optimization Enabled Deep Learning for Heart Disease Detection.

Computational intelligence and neuroscience
Heart disease causes major death across the entire globe. Hence, heart disease prediction is a vital part of medical data analysis. Recently, various data mining and machine learning practices have been utilized to detect heart disease. However, thes...

A machine learning approach for predicting perihematomal edema expansion in patients with intracerebral hemorrhage.

European radiology
OBJECTIVES: Preventing the expansion of perihematomal edema (PHE) represents a novel strategy for the improvement of neurological outcomes in intracerebral hemorrhage (ICH) patients. Our goal was to predict early and delayed PHE expansion using a mac...

Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches.

Journal of chemical information and modeling
Hematotoxicity has been becoming a serious but overlooked toxicity in drug discovery. However, only a few models have been reported for the prediction of hematotoxicity. In this study, we constructed a high-quality dataset comprising 759 hematotoxic...

Machine Learning-Based Models with High Accuracy and Broad Applicability Domains for Screening PMT/vPvM Substances.

Environmental science & technology
Persistent, mobile, and toxic (PMT) substances and very persistent and very mobile (vPvM) substances can transport over long distances from various sources, increasing the public health risk. A rapid and high-throughput screening of PMT/vPvM substanc...

A machine learning method for improving the accuracy of radiation biodosimetry by combining data from the dicentric chromosomes and micronucleus assays.

Scientific reports
A large-scale malicious or accidental radiological event can expose vast numbers of people to ionizing radiation. The dicentric chromosome (DCA) and cytokinesis-block micronucleus (CBMN) assays are well-established biodosimetry methods for estimating...

Machine learning-based predictions of gamma passing rates for virtual specific-plan verification based on modulation maps, monitor unit profiles, and composite dose images.

Physics in medicine and biology
Machine learning (ML) methods have been implemented in radiotherapy to aid virtual specific-plan verification protocols, predicting gamma passing rates (GPR) based on calculated modulation complexity metrics because of their direct relation to dose d...

Ensemble learning for glioma patients overall survival prediction using pre-operative MRIs.

Physics in medicine and biology
: Gliomas are the most common primary brain tumors. Approximately 70% of the glioma patients diagnosed with glioblastoma have an averaged overall survival (OS) of only ∼16 months. Early survival prediction is essential for treatment decision-making i...

A machine learning-based framework to design capillary-driven networks.

Lab on a chip
We present a novel approach for the design of capillary-driven microfluidic networks using a machine learning genetic algorithm (ML-GA). This strategy relies on a user-friendly 1D numerical tool specifically developed to generate the necessary data t...

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation.

Genomics, proteomics & bioinformatics
Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by...

Assessing future technological impacts of patents based on the classification algorithms in machine learning: The case of electric vehicle domain.

PloS one
INTRODUCTION: Identifying the technologies that will drive technological changes over the coming years is important for the optimal allocation of firms' R&D resources and the deployment of innovation strategies. The citation frequency of a patent is ...