AIMC Topic: Cell Line, Tumor

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is a novel marker for bladder cancer prognosis: evidence based on experimental studies, machine learning and single-cell sequencing.

Frontiers in immunology
BACKGROUND: Bladder cancer, a highly fatal disease, poses a significant threat to patients. Positioned at 19q13.2-13.3, LIG1, one of the four DNA ligases in mammalian cells, is frequently deleted in tumour cells of diverse origins. Despite this, the ...

Machine learning analysis of oxidative stress-related phenotypes for specific gene screening in ovarian cancer.

Environmental toxicology
BACKGROUND: Oxidative stress serves a crucial role in tumor development. However, the relationship between ovarian cancer and oxidative stress remains unknown. We aimed to create an oxidative stress-related prognostic signature to enhance the prognos...

Automated Bio-AFM Generation of Large Mechanome Data Set and Their Analysis by Machine Learning to Classify Cancerous Cell Lines.

ACS applied materials & interfaces
Mechanobiological measurements have the potential to discriminate healthy cells from pathological cells. However, a technology frequently used to measure these properties, i.e., atomic force microscopy (AFM), suffers from its low output and lack of s...

Accurate Identification of Cancer Cells in Complex Pre-Clinical Models Using a Deep-Learning Neural Network: A Transfection-Free Approach.

Advanced biology
3D co-cultures are key tools for in vitro biomedical research as they recapitulate more closely the in vivo environment while allowing a tighter control on the culture's composition and experimental conditions. The limited technologies available for ...

Single-detector multiplex imaging flow cytometry for cancer cell classification with deep learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged as a powerful tool for cell analysis in various biomedical fields such as cancer detection. In this study, we develop multiplex imaging flow cytometry...

Machine learning-based screening and validation of liver metastasis-specific genes in colorectal cancer.

Scientific reports
Colorectal liver metastasis (CRLM) is challenging in the clinical treatment of colorectal cancer. Limited research has been conducted on how CRLM develops. RNA sequencing data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome...

SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations.

BMC bioinformatics
BACKGROUND: The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated n...

Using deep learning to decipher the impact of telomerase promoter mutations on the dynamic metastatic morpholome.

PLoS computational biology
Melanoma showcases a complex interplay of genetic alterations and intra- and inter-cellular morphological changes during metastatic transformation. While pivotal, the role of specific mutations in dictating these changes still needs to be fully eluci...

A New Fingerprint and Graph Hybrid Neural Network for Predicting Molecular Properties.

Journal of chemical information and modeling
Machine learning plays a role in accelerating drug discovery, and the design of effective machine learning models is crucial for accurately predicting molecular properties. Characterizing molecules typically involves the use of molecular fingerprints...