AIMC Topic: Machine Learning

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Assessment of estrogenic potential from exudates of microcystin-producing and non-microcystin-producing Microcystis by metabolomics, machine learning and E-screen assay.

Journal of hazardous materials
Cyanobacterial blooms, often dominated by Microcystis aeruginosa, are capable of producing estrogenic effects. It is important to identify specific estrogenic compounds produced by cyanobacteria, though this can prove challenging owing to the complex...

High-Precision Viral Detection Using Electrochemical Kinetic Profiling of Aptamer-Antigen Recognition in Clinical Samples and Machine Learning.

Angewandte Chemie (International ed. in English)
High-precision viral detection at point of need with clinical samples plays a pivotal role in the diagnosis of infectious diseases and the control of a global pandemic. However, the complexity of clinical samples that often contain very low viral con...

Machine learning-based integration develops a stress response stated T cell (Tstr)-related score for predicting outcomes in clear cell renal cell carcinoma.

International immunopharmacology
BACKGROUND: Establishment of a reliable prognostic model and identification of novel biomarkers are urgently needed to develop precise therapy strategies for clear cell renal cell carcinoma (ccRCC). Stress response stated T cells (Tstr) are a new T-c...

Stack-AAgP: Computational prediction and interpretation of anti-angiogenic peptides using a meta-learning framework.

Computers in biology and medicine
BACKGROUND: Angiogenesis plays a vital role in the pathogenesis of several human diseases, particularly in the case of solid tumors. In the realm of cancer treatment, recent investigations into peptides with anti-angiogenic properties have yielded en...

Enhancing cross-subject EEG emotion recognition through multi-source manifold metric transfer learning.

Computers in biology and medicine
Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain adaptation challenges in affective brain-computer interfaces (aBCI). However, previous TL methods usually use a stationary distance, such as Euclidean distanc...

Machine learning-based 3D segmentation of mitochondria in polarized epithelial cells.

Mitochondrion
Mitochondria are dynamic organelles that alter their morphological characteristics in response to functional needs. Therefore, mitochondrial morphology is an important indicator of mitochondrial function and cellular health. Reliable segmentation of ...

Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features.

Journal of environmental management
Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administra...

Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes.

Sensors (Basel, Switzerland)
Classification-based myoelectric control has attracted significant interest in recent years, leading to prosthetic hands with advanced functionality, such as multi-grip hands. Thus far, high classification accuracies have been achieved by increasing ...

Merging Counter-Propagation and Back-Propagation Algorithms: Overcoming the Limitations of Counter-Propagation Neural Network Models.

International journal of molecular sciences
Artificial neural networks (ANNs) are nowadays applied as the most efficient methods in the majority of machine learning approaches, including data-driven modeling for assessment of the toxicity of chemicals. We developed a combined neural network me...

Applying Machine Learning for Enhanced MicroRNA Analysis: A Companion Risk Tool for Oral Squamous Cell Carcinoma in Standard Care Incisional Biopsy.

Biomolecules
Machine learning analyses within the realm of oral cancer outcomes are relatively underexplored compared to other cancer types. This study aimed to assess the performance of machine learning algorithms in identifying oral cancer patients, utilizing m...