AIMC Topic: Machine Learning

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Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs.

Genes
In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvem...

Development of a patients' satisfaction analysis system using machine learning and lexicon-based methods.

BMC health services research
BACKGROUND: Patients' rights are integral to medical ethics. This study aimed to perform sentiment analysis and opinion mining on patients' messages by a combination of lexicon-based and machine learning methods to identify positive or negative comme...

A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application.

BMC bioinformatics
BACKGROUND: Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the identification of various maligna...

Machine learning at the edge for AI-enabled multiplexed pathogen detection.

Scientific reports
Multiplexed detection of biomarkers in real-time is crucial for sensitive and accurate diagnosis at the point of use. This scenario poses tremendous challenges for detection and identification of signals of varying shape and quality at the edge of th...

MIDRC CRP10 AI interface-an integrated tool for exploring, testing and visualization of AI models.

Physics in medicine and biology
. Developing Machine Learning models (N Gorre et al 2023) for clinical applications from scratch can be a cumbersome task requiring varying levels of expertise. Seasoned developers and researchers may also often face incompatible frameworks and data ...

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer.

PLoS computational biology
One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strat...

Integrating single-cell analysis and machine learning to create glycosylation-based gene signature for prognostic prediction of uveal melanoma.

Frontiers in endocrinology
BACKGROUND: Increasing evidence suggests a correlation between glycosylation and the onset of cancer. However, the clinical relevance of glycosylation-related genes (GRGs) in uveal melanoma (UM) is yet to be fully understood. This study aimed to shed...

Predicting mutational function using machine learning.

Mutation research. Reviews in mutation research
Genetic variations are one of the major causes of phenotypic variations between human individuals. Although beneficial as being the substrate of evolution, germline mutations may cause diseases, including Mendelian diseases and complex diseases such ...

GutBug: A Tool for Prediction of Human Gut Bacteria Mediated Biotransformation of Biotic and Xenobiotic Molecules Using Machine Learning.

Journal of molecular biology
Dietary components and bioactive molecules present in functional foods and nutraceuticals provide various beneficial effects including modulation of host gut microbiome. These metabolites along with orally administered drugs can be potentially bio-tr...

Prediction of enzymatic function with high efficiency and a reduced number of features using genetic algorithm.

Computers in biology and medicine
The post-genomic era has raised a growing demand for efficient procedures to identify protein functions, which can be accomplished by applying machine learning to the characteristics set extracted from the protein. This approach is feature-based and ...