AIMC Topic: Neoplasms

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The best from both disciplines: integrating human and microbial signatures from whole genome sequencing to advance cancer diagnostics.

mSystems
Liquid biopsies are transforming oncology, enabling earlier diagnosis, dynamic treatment guidance, and personalized precision medicine, yet current approaches focusing mainly on circulating host cell-free DNA (cfDNA) neglect crucial information withi...

Advancements in the study of exosomes in disease diagnosis and treatment.

International journal of pharmaceutics
Exosomes are small, membrane-enclosed vesicles that can originate from a variety of sources and contain a wealth of material. They are involved in physiological processes such as cell-cell communication, cell migration, and anti-tumor immunity, and a...

Spatial omics: applications and utility in profiling the tumor microenvironment.

Cancer metastasis reviews
Spatial transcriptomics has emerged as a transformative technology in biomedical research, offering unprecedented insights into gene and protein expression within their native tissue context. Unlike conventional bulk or single-cell sequencing approac...

Transfer learning with multiomics integration and deep neural networks reveals drug resistance mechanisms in cancer.

Scientific reports
Drug resistance remains one of the primary challenges in effective cancer therapy. In this study, we employed a deep neural network (DNN)-based transfer learning (TL) approach to predict drug response and uncover drug resistance mechanisms. We integr...

Enabling whole genome sequencing analysis from FFPE specimens in clinical oncology.

Nature communications
The adoption of whole genome sequencing (WGS) in clinical oncology is challenged by low data quality and increased artifacts in standard-of-care formalin-fixed paraffin-embedded (FFPE) samples. Analysis of 56 fresh frozen (FF) and FFPE matched pairs ...

[The role of artificial intelligence in the design and feasibility of early-phase oncology clinical trials].

Orvosi hetilap
Oncology clinical trials play a pivotal role in the development of new therapeutic options; however, their implementation remains an extremely costly and time-consuming process. Artificial intelligence can open new horizons in the design and conduct ...

MarkerPredict: predicting clinically relevant predictive biomarkers with machine learning.

NPJ systems biology and applications
Precision oncology relies on predictive biomarkers for selecting targeted cancer therapies. Network-based properties of proteins, together with structural features such as intrinsic disorder, are likely to shape their potential as biomarkers. We ther...

Rapid cancer diagnosis using deep learning-powered label-free subcellular-resolution photoacoustic histology.

Science advances
Traditional hematoxylin and eosin staining in formalin-fixed paraffin-embedded sections, while essential for diagnostic pathology, is time-consuming, labor intensive, and prone to artifacts that can obscure critical histological details. Label-free u...

Multi-omics strategies for biomarker discovery and application in personalized oncology.

Molecular biomedicine
Multi-omics strategies, integrating genomics, transcriptomics, proteomics, and metabolomics, have revolutionized biomarker discovery and enabled novel applications in personalized oncology. Despite rapid technological developments, a comprehensive sy...

AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions.

Clinical and experimental medicine
Cancer's staggering molecular heterogeneity demands innovative approaches beyond traditional single-omics methods. The integration of multi-omics data, spanning genomics, transcriptomics, proteomics, metabolomics and radiomics, can improve diagnostic...