AIMC Topic: Tumor Suppressor Proteins
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Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools.
Cancer reports (Hoboken, N.J.)
Nov 11, 2019
BACKGROUND: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the a...
Tumor Suppressor Protein p53
Tumor Suppressor Proteins
DNA Mutational Analysis
DNA Repair Enzymes
Drug Resistance, Neoplasm
Cancer Vaccines
Microtubules
Prognosis
Mutation
Humans
Models, Genetic
Tumor Microenvironment
Machine Learning
Antineoplastic Combined Chemotherapy Protocols
Genetic Heterogeneity
Brain Neoplasms
Brain
Glioblastoma
Chemoradiotherapy, Adjuvant
Immune Checkpoint Inhibitors
Precision Medicine
Clonal Evolution
Computational Biology
DNA Modification Methylases
Setting Up a Surface-Enhanced Raman Scattering Database for Artificial-Intelligence-Based Label-Free Discrimination of Tumor Suppressor Genes.
Analytical chemistry
Dec 4, 2018
The quality of input data in deep learning is tightly associated with the ultimate performance of the machine learner. Taking advantage of the unique merits of surface-enhanced Raman scattering (SERS) methodology in the collection and construction of...
Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.
AJNR. American journal of neuroradiology
May 10, 2018
BACKGROUND AND PURPOSE: The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimenta...
Multi-label Inductive Matrix Completion for Joint MGMT and IDH1 Status Prediction for Glioma Patients.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Sep 4, 2017
MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which is...
Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach.
Scientific reports
Apr 25, 2017
Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differ...
Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma.
Computer methods and programs in biomedicine
Dec 30, 2016
BACKGROUND AND OBJECTIVE: The O-methylguanine-DNA-methyltransferase (MGMT) promoter methylation has been shown to be associated with improved outcomes in patients with glioblastoma (GBM) and may be a predictive marker of sensitivity to chemotherapy. ...
Integrative modeling of multi-omics data to identify cancer drivers and infer patient-specific gene activity.
BMC systems biology
Feb 11, 2016
BACKGROUND: High throughput technologies have been used to profile genes in multiple different dimensions, such as genetic variation, copy number, gene and protein expression, epigenetics, metabolomics. Computational analyses often treat these differ...
A quantitative characterization of the heterogeneous response of glioblastoma U-87 MG cell line to temozolomide.
Scientific reports
May 8, 2025
Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and ...
Intratumoral Resolution of Driver Gene Mutation Heterogeneity in Renal Cancer Using Deep Learning.
Cancer research
Aug 3, 2022
UNLABELLED: Intratumoral heterogeneity arising from tumor evolution poses significant challenges biologically and clinically. Dissecting this complexity may benefit from deep learning (DL) algorithms, which can infer molecular features from ubiquitou...
MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2018
Glioblastoma Multiforme (GBM), a malignant brain tumor, is among the most lethal of all cancers. Temozolomide is the primary chemotherapy treatment for patients diagnosed with GBM. The methylation status of the promoter or the enhancer regions of the...
Humans
Neural Networks, Computer
Glioblastoma
Dacarbazine
DNA Modification Methylases
DNA Repair Enzymes
Drug Resistance, Neoplasm
Brain Neoplasms
Computational Biology
Databases, Genetic
Magnetic Resonance Imaging
DNA Methylation
Tumor Suppressor Proteins
Temozolomide
Antineoplastic Agents, Alkylating
Regulatory Sequences, Nucleic Acid
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