AIMC Topic: Machine Learning

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Artificial intelligence - based approaches based on random forest algorithm for signal analysis: Potential applications in detection of chemico - biological interactions.

Chemico-biological interactions
Random Forest (RF) is a powerful ensemble-based supervised machine learning technique that builds multiple decision trees using bootstrap aggregating and random feature selection to improve classification and regression accuracy while reducing overfi...

GMFOLD: Subgraph matching for high-throughput DNA-aptamer secondary structure classification and machine learning interpretability.

Mathematical biosciences
Aptamers are oligonucleotide receptors that bind to their targets with high affinity. Here, we consider aptamers comprised of single-stranded DNA that undergo target-binding-induced conformational changes, giving rise to unique secondary and tertiary...

Regional cortical thinning and area reduction are associated with cognitive impairment in hemodialysis patients.

Brain research bulletin
Magnetic resonance imaging (MRI) has shown that patients with end-stage renal disease have decreased gray matter volume and density. However, the cortical area and thickness in patients on hemodialysis are uncertain, and the relationship between pati...

Advances in risk prediction models for Glaucoma: An updated narrative review.

Experimental eye research
Glaucoma is a leading cause of irreversible blindness and is characterized by optic nerve atrophy and progressive visual field loss. Risk prediction models are crucial for early screening and personalized treatment by identifying high-risk individual...

Blockchain-aided comparative study of heart disease detection using machine learning-based approaches with an expanded dataset.

Computers in biology and medicine
Heart disease, also known as cardiovascular disease (CVD), is a diverse set of conditions that disrupt the normal functioning of the cardiovascular system by narrowing the coronary arteries. These arteries are used for blood circulation and the deliv...

Enhancing energy consumption prediction and interpretability in wastewater treatment plants: A novel temporal difference-weighted resampling framework with cross validation for imbalanced regression.

Journal of environmental management
Accurate prediction of energy consumption is crucial for optimizing wastewater treatment plant (WWTP) operations. However, imbalanced data caused by variable influent conditions often compromises machine learning (ML) model accuracy. This study propo...

Discovery of SARS-CoV-2 papain-like protease inhibitors through machine learning and molecular simulation approaches.

Drug discoveries & therapeutics
The papain-like protease (PLpro), a cysteine protease found in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), plays a crucial role in viral replication by cleaving the viral polyproteins and interfering with the host's innate immune re...

Integrating multi-omics and machine learning for disease resistance prediction in legumes.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Multi-omics assisted prediction of disease resistance mechanisms using machine learning has the potential to accelerate the breeding of resistant legume varieties. Grain legumes, such as soybean (Glycine max (L.) Merr.), chickpea (Cicer arietinum L.)...

Leveraging machine learning for monitoring afforestation in mining areas: evaluating Tata Steel's restoration efforts in Noamundi, India.

Environmental monitoring and assessment
Mining activities have long been associated with significant environmental impacts, including deforestation, habitat degradation, and biodiversity loss, necessitating targeted strategies like afforestation to mitigate ecological damage. Tata Steel's ...