AIMC Topic: Tumor Suppressor Protein p53

Clear Filters Showing 21 to 30 of 42 articles

Machine learning-based prediction of microsatellite instability and high tumor mutation burden from contrast-enhanced computed tomography in endometrial cancers.

Scientific reports Oct 20, 2020
To evaluate whether radiomic features from contrast-enhanced computed tomography (CE-CT) can identify DNA mismatch repair deficient (MMR-D) and/or tumor mutational burden-high (TMB-H) endometrial cancers (ECs). Patients who underwent targeted massive...
Computer Simulation Prognosis Tomography, X-Ray Computed Female Aged Middle Aged Humans Machine Learning Mutation Tumor Suppressor Protein p53 Endometrial Neoplasms Carcinoma, Endometrioid DNA Polymerase II Poly-ADP-Ribose Binding Proteins Cohort Studies Uterus Neoplasm Staging Microsatellite Instability DNA Mismatch Repair
View on PubMed DOI

Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer.

British journal of cancer Jul 21, 2020
BACKGROUND: Radiogenomics is an emerging field that integrates "Radiomics" and "Genomics". In the current study, we aimed to predict the genetic information of pancreatic tumours in a simple, inexpensive, and non-invasive manner, using cancer imaging...
Humans Aged Aged, 80 and over Male Middle Aged B7-H1 Antigen Pancreatic Neoplasms Machine Learning Prognosis Retrospective Studies Tomography, X-Ray Computed Female Mutation Carcinoma, Pancreatic Ductal Tumor Suppressor Protein p53 Immunohistochemistry Imaging Genomics
View on PubMed DOI

In vitro and in silico genetic toxicity screening of flavor compounds and other ingredients in tobacco products with emphasis on ENDS.

Journal of applied toxicology : JAT Jul 13, 2020
Electronic nicotine delivery systems (ENDS) are regulated tobacco products and often contain flavor compounds. Given the concern of increased use and the appeal of ENDS by young people, evaluating the potential of flavors to induce DNA damage is impo...
Histones Electronic Nicotine Delivery Systems Consumer Product Safety Mice Rats Cell Line Phosphorylation Models, Molecular DNA Damage Flow Cytometry Tumor Suppressor Protein p53 Flavoring Agents Quantitative Structure-Activity Relationship Mutagenicity Tests Humans Machine Learning Biomarkers Animals Risk Assessment
View on PubMed DOI

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...
Microtubules Tumor Suppressor Protein p53 Tumor Suppressor Proteins Models, Genetic Antineoplastic Combined Chemotherapy Protocols Glioblastoma Mutation Genetic Heterogeneity Chemoradiotherapy, Adjuvant Clonal Evolution DNA Modification Methylases DNA Mutational Analysis DNA Repair Enzymes Drug Resistance, Neoplasm Tumor Microenvironment Brain Neoplasms Immune Checkpoint Inhibitors Computational Biology Humans Cancer Vaccines Machine Learning Brain Precision Medicine Prognosis
View on PubMed DOI

Artificial Intelligence Approach to Find Lead Compounds for Treating Tumors.

The journal of physical chemistry letters Jul 22, 2019
It has been demonstrated that MMP13 enzyme is related to most cancer cell tumors. The world's largest traditional Chinese medicine database was applied to screen for structure-based drug design and ligand-based drug design. To predict drug activity, ...
Binding Sites Proto-Oncogene Proteins c-mdm2 Cyclin-Dependent Kinase 6 Databases, Pharmaceutical Matrix Metalloproteinase 13 Models, Molecular Mutation Ligands Proto-Oncogene Proteins c-bcl-2 Tumor Suppressor Protein p53 Machine Learning Algorithms Peptides Drug Design Antineoplastic Agents Deep Learning Cyclin-Dependent Kinase Inhibitor p16 Proteins Databases, Protein
View on PubMed DOI

Analysis of the Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach.

International journal of molecular sciences Jun 18, 2019
Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body mass index, hormone replacement therapy, family history and germline mutations. Of these risk facto...
Estrogen Receptor alpha Protein Interaction Domains and Motifs Structure-Activity Relationship Mutation Molecular Dynamics Simulation Tumor Suppressor Protein p53 Protein Conformation Machine Learning Molecular Docking Simulation Protein Binding Polymorphism, Single Nucleotide Computational Biology Humans Neural Networks, Computer
View on PubMed DOI

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...
Artificial Intelligence Databases, Factual Tumor Suppressor Proteins Silicon Lab-On-A-Chip Devices Spectrum Analysis, Raman DNA Metal Nanoparticles Silver Tumor Suppressor Protein p53
View on PubMed DOI

iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations.

Proteins Dec 3, 2018
Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using...
Evolution, Molecular Proteins Models, Molecular Mutation Protein Domains Tumor Suppressor Protein p53 Thermodynamics Binding, Competitive Proto-Oncogene Proteins c-mdm2 Machine Learning Protein Binding Computational Biology
View on PubMed DOI

Control of Gene Regulatory Networks Using Bayesian Inverse Reinforcement Learning.

IEEE/ACM transactions on computational biology and bioinformatics Apr 26, 2018
Control of gene regulatory networks (GRNs) to shift gene expression from undesirable states to desirable ones has received much attention in recent years. Most of the existing methods assume that the cost of intervention at each state and time point,...
Gene Regulatory Networks Computational Biology Gene Expression Regulation, Neoplastic Models, Genetic Bayes Theorem Models, Biological Tumor Suppressor Protein p53 Proto-Oncogene Proteins c-mdm2 Wnt-5a Protein Software Algorithms Melanoma Skin Neoplasms Gene Expression Profiling Humans Machine Learning
View on PubMed DOI

From word models to executable models of signaling networks using automated assembly.

Molecular systems biology Nov 24, 2017
Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to b...
Computer Simulation DNA Damage Language Sulfonamides Melanoma Humans Indoles Skin Neoplasms Enzyme Inhibitors Neural Networks, Computer Antineoplastic Agents Drug Resistance, Neoplasm Cell Line, Tumor Tumor Suppressor Protein p53 Gene Expression Regulation, Neoplastic Models, Genetic Signal Transduction Natural Language Processing Proto-Oncogene Proteins p21(ras) Proto-Oncogene Proteins B-raf Vemurafenib
View on PubMed DOI
Popular Topics
Recent Journals