AIMC Topic: Machine Learning

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Predicting bone cancer drugs properties through topological indices and machine learning.

Scientific reports
Chemical graph theory and topological indices are key tools in the study of molecular structures and their properties. This research explores anticancer drugs using neighborhood degree-based topological indices and compares their efficacy through reg...

Potential application of Healitide-GP1, a novel antibacterial peptide, in wound healing: in vitro studies.

Scientific reports
Wound healing is a complex process that can be compromised by bacterial infections, leading to delayed healing and an increased risk of complications. The aim of this study was to design and develop a novel antibacterial peptide, Healitide-GP1, which...

Machine Learning-Assisted Real-Time Inflammation Monitoring and Optimal Treatment of Diabetic Wounds Based on a Ratiometric Fluorescent Sensing Peptide Hydrogel.

Nano letters
Managing inflammation in diabetic chronic wounds remains a major clinical challenge, primarily due to the lack of real-time monitoring techniques. To address this issue, we developed a peptide hydrogel capable of simultaneously monitoring the inflamm...

Aging associated immunosenescence in rheumatoid arthritis identified by machine learning and single cell profiling.

Scientific reports
Rheumatoid arthritis (RA) is increasingly prevalent among older adults, who often experience more severe symptoms and face significant treatment challenges. This study aims to identify specific genes associated with aging in RA and to analyze their i...

Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition.

Scientific reports
The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is ch...

Discovery of CRISPR-Cas12a clades using a large language model.

Nature communications
CRISPR-Cas systems revolutionize life science. Metagenomes contain millions of unknown Cas proteins. Traditional mining relies on protein sequence alignments. In this work, we employ an evolutionary scale language model (ESM) to learn the information...

Developing a hybrid machine learning model to predict treatment time duration as a workflow regulation tool in public and private dental clinics.

Scientific reports
This study aimed to design a desktop application that implements machine learning algorithms to predict dental treatment time durations, assess the accuracy of the model, and assess its clinical efficiency. The Python programming language was used to...

Identification and analysis of the endoplasmic reticulum stress hub genes in sepsis-associated ARDS.

Scientific reports
Acute respiratory distress syndrome (ARDS) is one of the most common and serious complications in the development of sepsis. Endoplasmic reticulum stress (ERS) plays an important role in the pathophysiologic process of sepsis-associated ARDS. The aim...

Challenges and Opportunities in Smart Biosensing for Biomanufacturing.

ACS synthetic biology
Traditional metabolic engineering has largely focused on the direct construction of synthetic metabolic pathways, often overlooking the critical role of regulation. In contrast, natural metabolic pathways are inherently tightly regulated, enabling ro...