AIMC Topic: Humans

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Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning.

Scientific reports
Predicting low nuclear grade DCIS before surgery can improve treatment choices and patient care, thereby reducing unnecessary treatment. Due to the high heterogeneity of DCIS and the limitations of biopsies in fully characterizing tumors, current dia...

Quantitative analysis and evaluation of winter and summer landscape colors in the Yangzhou ancient Canal utilizing deep learning.

Scientific reports
Color is an important index for human visual evaluation of landscape, and it is also a key factor affecting people's recognition and experience of heritage landscape. In this study, five important sites of the Yangzhou Grand Canal were selected for t...

MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival.

Scientific reports
We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene...

Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control.

Scientific reports
Cytochrome P450 1A2, as many isoenzymes, can generate multiple metabolites from a single substrate. A loose coupling between substrate binding and oxygen activation makes possible substrate reorientations at the active site prior to catalysis. In the...

Synergistic transfer learning and adversarial networks for breast cancer diagnosis: benign vs. invasive classification.

Scientific reports
Current breast cancer diagnosis methods often face limitations such as high cost, time consumption, and inter-observer variability. To address these challenges, this research proposes a novel deep learning framework that leverages generative adversar...

Machine learning analysis identified NNMT as a potential therapeutic target for hepatocellular carcinoma based on PCD-related genes.

Scientific reports
Programmed cell death (PCD) plays a critical role in cancer biology, influencing tumor progression and treatment response. This study aims to investigate the role of PCD-related genes in hepatocellular carcinoma (HCC), identifying potential prognosti...

High performance fake review detection using pretrained DeBERTa optimized with Monarch Butterfly paradigm.

Scientific reports
In this era of internet, e-commerce has grown tremendously and the customers are increasingly relying on reviews for product information. As these reviews influence the purchasing ability of the future customer, it can give a positive or negative imp...

Modifying the severity and appearance of psoriasis using deep learning to simulate anticipated improvements during treatment.

Scientific reports
A neural network was trained to generate synthetic images of severe and moderate psoriatic plaques, after being trained on 375 photographs of patients with psoriasis taken in a clinical setting. A latent w-space vector was identified that allowed the...

Machine learning identifies clinical tumor mutation landscape pathways of resistance to checkpoint inhibitor therapy in NSCLC.

Journal for immunotherapy of cancer
BACKGROUND: Immune checkpoint inhibitors (CPIs) have revolutionized cancer therapy for several tumor indications. However, a substantial fraction of patients treated with CPIs derive no benefit or have short-lived responses to CPI therapy. Identifyin...

Accuracy of machine learning and traditional statistical models in the prediction of postpartum haemorrhage: a systematic review.

BMJ open
OBJECTIVES: To evaluate whether postpartum haemorrhage (PPH) can be predicted using both machine learning (ML) and traditional statistical models.