AIMC Topic: Machine Learning

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Exploration of common pathogenesis and candidate hub genes between HIV and monkeypox co-infection using bioinformatics and machine learning.

Scientific reports
This study explored the pathogenesis of human immunodeficiency virus (HIV) and monkeypox co-infection, identifying candidate hub genes and potential drugs using bioinformatics and machine learning. Datasets for HIV (GSE 37250) and monkeypox (GSE 2412...

Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems.

Scientific reports
Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Nanotherapeutics, which merge nanotechnology with targeted drug delivery, exemplify this advancement b...

Liquid saliva-based Raman spectroscopy device with on-board machine learning detects COVID-19 infection in real-time.

The Analyst
With greater population density, the likelihood of viral outbreaks achieving pandemic status is increasing. However, current viral screening techniques use specific reagents, and as viruses mutate, test accuracy decreases. Here, we present the first ...

Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher.

IEEE transactions on medical imaging
Multiple Instance Learning (MIL) has demonstrated promise in Whole Slide Image (WSI) classification. However, a major challenge persists due to the high computational cost associated with processing these gigapixel images. Existing methods generally ...

Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning.

Frontiers in cellular and infection microbiology
BACKGROUND: This study aimed to characterize the oral and gut microbiota in prediabetes mellitus (Pre-DM) and type 2 diabetes mellitus (T2DM) patients while exploring the association between tongue manifestations and the oral-gut microbiota axis in d...

Identification and validation of interferon-stimulated gene 15 as a biomarker for dermatomyositis by integrated bioinformatics analysis and machine learning.

Frontiers in immunology
BACKGROUND: Dermatomyositis (DM) is an autoimmune disease that primarily affects the skin and muscles. It can lead to increased mortality, particularly when patients develop associated malignancies or experience fatal complications such as pulmonary ...

Identification of novel markers for neuroblastoma immunoclustering using machine learning.

Frontiers in immunology
BACKGROUND: Due to the unique heterogeneity of neuroblastoma, its treatment and prognosis are closely related to the biological behavior of the tumor. However, the effect of the tumor immune microenvironment on neuroblastoma needs to be investigated,...

Common laboratory results-based artificial intelligence analysis achieves accurate classification of plasma cell dyscrasias.

PeerJ
BACKGROUND: Plasma cell dyscrasias encompass a diverse set of disorders, where early and precise diagnosis is essential for optimizing patient outcomes. Despite advancements, current diagnostic methodologies remain underutilized in applying artificia...

Using machine learning to classify temporomandibular disorders: a proof of concept.

Journal of applied oral science : revista FOB
BACKGROUND: the escalating influx of patients with temporomandibular disorders and the challenges associated with accurate diagnosis by non-specialized dental practitioners underscore the integration of artificial intelligence into the diagnostic pro...

Data science and automation in the process of theorizing: Machine learning's power of induction in the co-duction cycle.

PloS one
Recent calls to take up data science either revolve around the superior predictive performance associated with machine learning or the potential of data science techniques for exploratory data analysis. Many believe that these strengths come at the c...