AIMC Topic: Neoplasms

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Functional Characterization of Variants of Unknown Significance of Fibroblast Growth Factor Receptors 1-4 and Comparison With AI Model-Based Prediction.

JCO precision oncology
PURPOSE: Fibroblast growth factor receptors (FGFRs; FGFR1, FGFR2, FGFR3, FGFR4) are frequently mutated oncogenes in solid cancers. The oncogenic potential of FGFR rearrangements and few hotspot point mutations is well established, but the majority of...

Next-generation cancer therapeutics: unveiling the potential of liposome-based nanoparticles through bioinformatics.

Mikrochimica acta
Cancer remains one of the most deadly diseases in the world, requiring constant growth and improvements in therapeutic strategies. Traditional cancer treatments, such as chemotherapy, radiotherapy, and surgery, have limitations like off-target releas...

Artificial Intelligence in cancer epigenomics: a review on advances in pan-cancer detection and precision medicine.

Epigenetics & chromatin
DNA methylation is a fundamental epigenetic modification that regulates gene expression and maintains genomic stability. Consequently, DNA methylation remains a key biomarker in cancer research, playing a vital role in diagnosis, prognosis, and tailo...

Classifying Stereotactic Radiosurgery Patients by Primary Diagnosis Using Natural Language Processing of Clinical Notes.

JCO clinical cancer informatics
PURPOSE: Accurate identification of the primary tumor diagnosis of patients who have undergone stereotactic radiosurgery (SRS) from electronic health records is a critical but challenging task. Traditional methods of identifying the primary tumor his...

Noise-Consistent Hypergraph Autoencoder Based on Contrastive Learning for Cancer ceRNA Association Prediction in Complex Biological Regulatory Networks.

Journal of chemical information and modeling
Competitive endogenous RNA (ceRNA) regulatory networks (CENA) have advanced our understanding of noncoding RNAs' roles in complex diseases, providing a theoretical basis for disease mechanisms. Existing ceRNA-disease association prediction methods ar...

Explainable one-class feature extraction by adaptive resonance for anomaly detection in quality assurance.

PloS one
In this study, we address the inherent challenges in radiotherapy (RT) plan quality assessment (QA). RT, a prevalent cancer treatment, utilizes high-energy beams to target tumors while sparing adjacent healthy tissues. Typically, an RT plan is refine...

Preferences for Telephone Cancer Information and Support in People with Cancer and Carers: Attribute and Level Selection for a Discrete Choice Experiment.

The patient
BACKGROUND AND OBJECTIVE: Telephone cancer information and support services (CISS) deliver essential evidence-based resources for people living with cancer. This research aimed to describe how attributes and levels were developed for a future discret...

The latest advances with natural products in drug discovery and opportunities for the future: a 2025 update.

Expert opinion on drug discovery
INTRODUCTION: The landscape of drug discovery is rapidly evolving, with natural products (NPs) playing a pivotal role in the development of novel therapeutics. Despite their historical significance, challenges persist in fully harnessing their potent...

MFDSMC: Accurate Identification of Cancer-Driver Synonymous Mutations Using Multiperspective Feature Representation Learning.

Journal of chemical information and modeling
Synonymous mutations do not change amino acid sequences, but they can drive cancer by influencing splicing, mRNA structure, translation efficiency, and other molecular mechanisms. Although driver synonymous mutations are significantly outnumbered by ...

GNNs surpass transformers in tumor medical image segmentation.

Scientific reports
To assess the suitability of Transformer-based architectures for medical image segmentation and investigate the potential advantages of Graph Neural Networks (GNNs) in this domain. We analyze the limitations of the Transformer, which models medical i...