AIMC Topic: Neoplasms

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A novel Harris Hawks Optimization-based clustering method for elucidating genetic associations in osteoarthritis and Diverse Cancer Types.

Computers in biology and medicine
Considering the high incidence of osteoarthritis (OA), especially of the knee and hip, this study explores the possible genetic associations between OA and cancer types, including cancers of the bladder, kidney, breast, and prostate. The objective of...

CancerNet: A comprehensive deep learning framework for precise and intelligible cancer identification.

Computers in biology and medicine
The medical community continually seeks innovative solutions to address healthcare challenges, particularly in cancer detection. A promising approach involves the use of Artificial Intelligence (AI) techniques, specifically Deep Learning (DL) models....

A cluster attention-based multiple instance learning network for enhancing histopathological image interpretation.

Computers in biology and medicine
BACKGROUND: Histopathological diagnosis involves examining abnormal architectural patterns and cellular-level changes. Whole slide images (WSIs) provide comprehensive digital representations of tissue samples, enabling detailed analysis and interpret...

Advances in photoactivated carbon-based nanostructured materials for targeted cancer therapy.

Advanced drug delivery reviews
In this review, we explore key innovations in photoactivated therapeutic programming of carbon-based nanomaterials (CBNs), focusing on their diverse nanostructural configurations and their exceptional photothermal, photochemical, and photoacoustic pr...

Extracellular vesicles as nature's nano carriers in cancer therapy: Insights toward preclinical studies and clinical applications.

Pharmacological research
Extracellular vesicles (EVs), which are secreted by various cell types, hold significant potential for cancer therapy. However, there are several challenges and difficulties that limit their application in clinical settings. This review, which integr...

Structure-based artificial intelligence-aided design of MYC-targeting degradation drugs for cancer therapy.

Biochemical and biophysical research communications
The MYC protein is an oncoprotein that plays a crucial role in various cancers. Although its significance has been well recognized in research, the development of drugs targeting MYC remains relatively slow. In this study, we developed a novel MYC pe...

Prediction of bacterial and fungal bloodstream infections using machine learning in patients undergoing chemotherapy.

European journal of cancer (Oxford, England : 1990)
PURPOSE: This study aimed to develop a machine learning (ML) model to predict bloodstream infection (BSI) in chemotherapy patients.

Nanotechnology-Enhanced siRNA Delivery: Revolutionizing Cancer Therapy.

ACS applied bio materials
RNA interference (RNAi) has emerged as a transformative approach for cancer therapy, enabling precise gene silencing through small interfering RNA (siRNA). However, the clinical application of siRNA-based treatments faces challenges such as rapid deg...

Artificial intelligence approaches for tumor phenotype stratification from single-cell transcriptomic data.

eLife
Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and no...

Phage display enables machine learning discovery of cancer antigen-specific TCRs.

Science advances
T cells targeting epitopes in infectious diseases or cancer play a central role in spontaneous and therapy-induced immune responses. Epitope recognition is mediated by the binding of the T cell receptor (TCR), and TCRs recognizing clinically relevant...