RPRM NCBI Gene
Description: reprimo, TP53 dependent G2 arrest mediator homolog
Organism: Staurois parvus
Chromosome: Un
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NCBI Gene Records
TP53BP1 NCBI Gene
Description: tumor protein p53 binding protein 1
Organism: Staurois parvus
Chromosome: Un
View NCBI Gene RecordTcs5 NCBI Gene
Description: TP53 regulating kinase
Organism: Hesperophylax consimilis
Chromosome: 28
View NCBI Gene RecordTcs5 NCBI Gene
Description: TP53 regulating kinase
Organism: Amblyomma mixtum
Chromosome: 3
View NCBI Gene RecordTP53BP1 NCBI Gene
Description: tumor protein p53 binding protein 1
Organism: Sceloporus occidentalis
Chromosome: Un
View NCBI Gene RecordarXiv Research Articles
Comparison of Modules of Wild Type and Mutant Huntingtin and TP53 Protein Interaction Networks: Implications in Biological Processes and Functions arXiv
Published: July 13, 2013
Authors: Mahashweta Basu, Nitai P. Bhattacharyya, Pradeep K. Mohanty
Disease-causing mutations usually change the interacting partners of mutant proteins. In this article, we propose that the biological consequences of mutation are directly related to the alteration of corresponding protein protein interaction networks (PPIN). Mutation of Huntingtin (HTT) which causes Huntington's disease (HD) and mutations to TP53 which is associated with different cancers are studied as two example cases. We construct the PPIN of wild type and mutant proteins separately and identify the structural modules of each of the networks. The functional role of these modules are then assessed by Gene Ontology (GO) enrichment analysis for biological processes (BPs). We find that a large number of significantly enriched (p<0.0001) GO terms in mutant PPIN were absent in the wild type PPIN indicating the gain of BPs due to mutation. Similarly some of the GO terms enriched in wild type PPIN cease to exist in the modules of mutant PPIN, representing the loss. GO terms common in modules of mutant and wild type networks indicate both loss and gain of BPs. We further assign relevant biological function(s) to each module by classifying the enriched GO terms associated with it. It turns out that most of these biological functions in HTT networks are already known to be altered in HD and those of TP53 networks are altered in cancers. We argue that gain of BPs, and the corresponding biological functions, are due to new interacting partners acquired by mutant proteins. The methodology we adopt here could be applied to genetic diseases where mutations alter the ability of the protein to interact with other proteins.
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Expectation-Maximization (EM) Algorithms for Mapping Short Reads Illustrated with FAIRE data and the TP53-WRAP53 Gene Region arXiv
Published: December 29, 2009
Authors: Peter J. Waddell, Timothy Herston
Huge numbers of short reads are being generated for mapping back to the genome to discover the frequency of transcripts, miRNAs, DNAase hypersensitive sites, FAIRE regions, nucleosome occupancy, etc. Since these reads are typically short (e.g., 36 base pairs) and since many eukaryotic genomes, including humans, have highly repetitive sequences then many of these reads map to two or more locations in the genome. Current mapping of these reads, grading them according to 0, 1 or 2 mismatches wastes a great deal of information. These short sequences are typically mapped with no account of the accuracy of the sequence, even in company software when per base error rates are being reported by another part of the machine. Further, multiply mapping locations are frequently discarded altogether or allocated with no regard to where other reads are accumulating. Here we show how to combine probabilistic mapping of reads with an EM algorithm to iteratively improve the empirical likelihood of the allocation of short reads. Mapping using LAST takes into account the per base accuracy of the read, plus insertions and deletions, plus anticipated occasional errors or SNPs with respect to the parent genome. The probabilistic EM algorithm iteratively allocates reads based on the proportion of reads mapping within windows on the previous cycle, along with any prior information on where the read best maps. The methods are illustrated with FAIRE ENCODE data looking at the very important head-to-head gene combination of TP53 and WRAP 53.
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Effective Data Mining Technique for Classification Cancers via Mutations in Gene using Neural Network arXiv
Published: August 6, 2016
Authors: Ayad Ghany Ismaeel, Dina Yousif Mikhail
The prediction plays the important role in detecting efficient protection and therapy of cancer. The prediction of mutations in gene needs a diagnostic and classification, which is based on the whole database (big dataset), to reach sufficient accuracy results. Since the tumor suppressor P53 is approximately about fifty percentage of all human tumors because mutations that occur in the TP53 gene into the cells. So, this paper is applied on tumor p53, where the problem is there are several primitive databases (excel database) contain datasets of TP53 gene with its tumor protein p53, these databases are rich datasets that cover all mutations and cause diseases (cancers). But these Data Bases cannot reach to predict and diagnosis cancers, i.e. the big datasets have not efficient Data Mining method, which can predict, diagnosis the mutation, and classify the cancer of patient. The goal of this paper to reach a Data Mining technique, that employs neural network, which bases on the big datasets. Also, offers friendly predictions, flexible, and effective classified cancers, in order to overcome the previous techniques drawbacks. This proposed technique is done by using two approaches, first, bioinformatics techniques by using BLAST, CLUSTALW, etc, in order to know if there are malignant mutations or not. The second, data mining by using neural network; it is selected (12) out of (53) TP53 gene database fields. To clarify, one of these 12 fields (gene location field) did not exists in TP53 gene database; therefore, it is added to the database of TP53 gene in training and testing back propagation algorithm, in order to classify specifically the types of cancers. Feed Forward Back Propagation supports this Data Mining method with data training rate (1) and Mean Square Error (MSE) (0.00000000000001). This effective technique allows in a quick, accurate and easy way to classify the type of cancer.
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PET/CT Radiogenomic Mutation Prediction in Non-Small Cell Lung Cancer Using Multi-Label Learning arXiv
Published: August 10, 2026
Authors: Mona Furukawa, Sai Hyne, Daniel R. McGowan, Bartłomiej W. Papież
Lung cancer remains one of the leading causes of cancer- related mortality worldwide. Although targeted therapies have improved outcomes for patients with non-small cell lung cancer (NSCLC), they rely on mutation profiling through tissue biopsy, an invasive procedure with several limitations. This study investigates PET/CT-based radio- genomic prediction of epidermal growth factor receptor (EGFR), tumour protein 53 (TP53), and Kirsten rat sarcoma viral oncogene (KRAS) mutations using deep learning. We further evaluate whether pairwise multi-label learning improves mutation prediction compared with conventional single-gene classification. To the best of our knowledge, this is among the first studies to systematically investigate multi-label learning for PET/CT radiogenomic mutation prediction in NSCLC. Experiments were conducted on a novel UK-based radiogenomics cohort. Joint pre- diction of KRAS and TP53 improved AUC from 0.58 to 0.64 for KRAS and from 0.69 to 0.71 for TP53. For the EGFR/KRAS pair, only EGFR benefited from joint learning, while no improvement was observed for the EGFR/TP53 pair. These findings demonstrate that the effectiveness of multi-label learning depends on the specific combination of gene mutations being modelled, suggesting that mutation-specific modelling strategies may be preferable for PET/CT radiogenomic prediction.
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Novel Mining of Cancer via Mutation in Tumor Protein P53 using Quick Propagation Network arXiv
Published: May 20, 2015
Authors: Ayad Ghany Ismaeel, Raghad Zuhair Yousif
There is multiple databases contain datasets of TP53 gene and its tumor protein P53 which believed to be involved in over 50% of human cancers cases, these databases are rich as datasets covered all mutations caused diseases (cancers), but they haven't efficient mining method can classify and diagnosis mutations patient's then predict the cancer of that patient. This paper proposed a novel mining of cancer via mutations because there is no mining method before offers friendly, effective and flexible predict or diagnosis of cancers via using whole common database of TP53 gene (tumor protein P53) as dataset and selecting a minimum number of fields in training and testing quick propagation algorithm which supporting this miming method. Simulating quick propagation network for the train dataset shows results the Correlation (0.9999), R-squared (0.9998) and mean of Absolute Relative Error (0.0029), while the training for the ALL datasets (train, test and validation dataset) have results the Correlation (0.9993), R-squared (0.9987) and mean of Absolute Relative Error (0.0057).
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