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🔎 Results for: BRCA1
LOC149485387 NCBI Gene
Organism: Colias philodice
1. LOC149485387
BRISC and BRCA1-A complex member 1-like [Colias philodice (clouded sulphur butterfly)]
Other Designations: BRISC and BRCA1-A complex member 1-like
Chromosome: 10
Annotation: Chromosome 10 NC_143095.1 (9339855..9340974)
ID: 149485387
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Unnamed Gene NCBI Gene
Organism:
No detailed information available.
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Unnamed Gene NCBI Gene
Organism:
1. LOC149482851
BRCA1-A complex subunit Abraxas 1-like [Colias philodice (clouded sulphur butterfly)]
Other Designations: BRCA1-A complex subunit Abraxas 1-like
Chromosome: 14
Annotation: Chromosome 14 NC_143099.1 (5614618..5616523)
ID: 149482851
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Unnamed Gene NCBI Gene
Organism:
1. BRAP
BRCA1 associated protein [Staurois parvus]
Other Designations: BRCA1-associated protein
Chromosome: Un
Annotation: NW_028317810.1 (2252026..2338847)
ID: 149473486
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BRAT1 NCBI Gene
Organism: Staurois parvus
No detailed information available.
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Unnamed Gene NCBI Gene
Organism:
No detailed information available.
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BRIP1 NCBI Gene
Organism: Staurois parvus
No detailed information available.
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Unnamed Gene NCBI Gene
Organism:
1. UIMC1
ubiquitin interaction motif containing 1 [Staurois parvus]
Other Designations: BRCA1-A complex subunit RAP80
Chromosome: Un
Annotation: NW_028319054.1 (80261..232688)
ID: 149437756
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LOC149426392 NCBI Gene
Organism: Hesperophylax consimilis
No detailed information available.
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Unnamed Gene NCBI Gene
Organism:
No detailed information available.
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LOC149421392 NCBI Gene
Organism: Hesperophylax consimilis
No detailed information available.
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Unnamed Gene NCBI Gene
Organism:
1. LOC149420002
BRCA1-A complex subunit Abraxas 1-like [Hesperophylax consimilis]
Other Designations: BRCA1-A complex subunit Abraxas 1-like
Chromosome: 11
Annotation: Chromosome 11 NC_142846.1 (29279062..29284948, complement)
ID: 149420002
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LOC149417562 NCBI Gene
Organism: Hesperophylax consimilis
No detailed information available.
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LOC149392460 NCBI Gene
Organism: Urtica dioica subsp. dioica
No detailed information available.
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Unnamed Gene NCBI Gene
Organism:
1. LOC149391884
BRISC/BRCA1-A complex protein [Urtica dioica subsp. dioica]
Other Designations: BRISC/BRCA1-A complex protein
Chromosome: 7
Annotation: Chromosome 7 NC_142762.1 (43471807..43474116)
ID: 149391884
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BARD1 NCBI Gene
Organism: Urtica dioica subsp. dioica
No detailed information available.
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Unnamed Gene NCBI Gene
Organism:
No detailed information available.
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LOC149375384 NCBI Gene
Organism: Copidosoma floridanum
No detailed information available.
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Unnamed Gene NCBI Gene
Organism:
1. LOC149372240
BRCA1-associated protein [Amblyomma mixtum]
Other Designations: BRCA1-associated protein
Chromosome: 1
Annotation: Chromosome 1 NC_143012.1 (303396027..303400982)
ID: 149372240
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LOC149371456 NCBI Gene
Organism: Amblyomma mixtum
No detailed information available.
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A Competing Risks Model with Binary Time Varying Covariates for Estimation of Breast Cancer Risks in BRCA1 Families arXiv
Published: 2020-02-12T21:45:27Z
Authors: Yun-Hee Choi, Hae Jung, Saundra Buys, Mary Daly, Esther John, John Hopper, Irene Andrulis, Mary-Beth Terry, Laurent Briollais
Mammographic screening and prophylactic surgery such as risk-reducing salpingo oophorectomy (RRSO) can potentially reduce breast cancer risks among mutation carriers of BRCA families. The evaluation of these interventions is usually complicated by the fact that their effects on breast cancer may change over time and by the presence of competing risks. We introduce a correlated competing risks model to model breast and ovarian cancer risks within BRCA1 families that accounts for time-varying covariates (TVCs). Different parametric forms for these TVCs are proposed for more flexibility and a correlated gamma frailty model is specified to account for the correlated competing events. We also introduced a new ascertainment correction approach that accounts for the selection of families through probands affected with either breast or ovarian cancer, or unaffected. Our simulation studies demonstrate the good performances of our proposed approach in terms of bias and precision of the estimators of model parameters and cause-specific penetrances over different levels of familial correlations. We apply our new approach to 498 BRCA1 mutation carrier families recruited through the Breast Cancer Family Registry. Our results demonstrate the importance of the functional form of the TVC when assessing the role of RRSO on breast cancer. In particular, under the best fitting TVC model, the overall effect of RRSO on breast cancer risk was statistically significant in women with BRCA1.
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Predicting clinical significance of BRCA1 and BRCA2 single nucleotide substitution variants with unknown clinical significance using probabilistic neural network and deep neural network-stacked autoencoder arXiv
Published: 2018-05-06T09:38:02Z
Authors: Ehsan Rahmatizad KhajePasha, Mahdi Bazarghan, Hamidreza Kheiri Manjili, Ramin Mohammadkhani, Ruhallah Amandi
Non-synonymous single nucleotide polymorphisms (nsSNPs) are single nucleotide substitution occurring in the coding region of a gene and leads to a change in amino-acid sequence of protein. The studies have shown these variations may be associated with disease. Thus, investigating the effects of nsSNPs on protein function will give a greater insight on how nsSNPs can lead into disease. Breast cancer is the most common cancer among women causing highest cancer death every year. BRCA1 and BRCA2 tumor suppressor genes are two main candidates of which, mutations in them can increase the risk of developing breast cancer. For prediction and detection of the cancer one can use experimental or computational methods, but the experimental method is very costly and time consuming in comparison with the computational method. The computer and computational methods have been used for more than 30 years. Here we try to predict the clinical significance of BRCA1 and BRCA2 nsSNPs as well as the unknown clinical significances. Nearly 500 BRCA1 and BRCA2 nsSNPs with known clinical significances retrieved from NCBI database. Based on hydrophobicity or hydrophilicity and their role in proteins' second structure, they are divided into 6 groups, each assigned with scores. The data are prepared in the acceptable form to the automated prediction mechanisms, Probabilistic Neural Network (PNN) and Deep Neural NetworkStacked AutoEncoder (DNN). With Jackknife cross validation we show that the prediction accuracy achieved for BRCA1 and BRCA2 using PNN are 87.97% and 82.17% respectively, while 95.41% and 92.80% accuracies achieved using DNN. The total required processing time for the training and testing the PNN is 0.9 second and DNN requires about 7 hours of training and it can predict instantly. both methods show great improvement in accuracy and speed compared to previous attempts.
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BRCA Gene Mutations in dbSNP: A Visual Exploration of Genetic Variants arXiv
Published: 2023-09-01T07:52:37Z
Authors: Woowon Jang, Shiwoo Koak, Jiwon Im, Utku Ozbulak, Joris Vankerschaver
BRCA genes, comprising BRCA1 and BRCA2 play indispensable roles in preserving genomic stability and facilitating DNA repair mechanisms. The presence of germline mutations in these genes has been associated with increased susceptibility to various cancers, notably breast and ovarian cancers. Recent advancements in cost-effective sequencing technologies have revolutionized the landscape of cancer genomics, leading to a notable rise in the number of sequenced cancer patient genomes, enabling large-scale computational studies. In this study, we delve into the BRCA mutations in the dbSNP, housing an extensive repository of 41,177 and 44,205 genetic mutations for BRCA1 and BRCA2, respectively. Employing meticulous computational analysis from an umbrella perspective, our research unveils intriguing findings pertaining to a number of critical aspects. Namely, we discover that the majority of BRCA mutations in dbSNP have unknown clinical significance. We find that, although exon 11 for both genes contains the majority of the mutations and may seem as if it is a mutation hot spot, upon analyzing mutations per base pair, we find that all exons exhibit similar levels of mutations. Investigating mutations within introns, while we observe that the recorded mutations are generally uniformly distributed, almost all of the pathogenic mutations in introns are located close to splicing regions (at the beginning or the end). In addition to the findings mentioned earlier, we have also made other discoveries concerning mutation types and the level of confidence in observations within the dbSNP database.
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Novel Method for Mutational Disease Prediction using Bioinformatics Techniques and Backpropagation Algorithm arXiv
Published: 2013-03-03T18:12:23Z
Authors: Ayad Ghany Ismaeel, Anar Auda Ablahad
Cancer is one of the most feared diseases in the world it has increased disturbingly and breast cancer occurs in one out of eight women, the prediction of malignancies plays essential roles not only in revealing human genome, but also in discovering effective prevention and treatment of cancers. Generally cancer disease driven by somatic mutations in an individual DNA sequence, or genome that accumulates during the lifetime of person. This paper is proposed a novel method can predict the disease by mutations despite The presence in gene sequence is not necessary it are malignant, so will be compare the protein of patient with the gene's protein of disease if there is difference between these two proteins then can say there is malignant mutations. This method will use bioinformatics techniques like FASTA, CLUSTALW, etc which shows whether malignant mutations or not, then training the backpropagation algorithm using all expected malignant mutations for a certain genes (e.g. BRCA1 and BRCA2) of disease, and using it to test whether patient is holder the disease or not. Implementing this novel method as the first way to predict the disease based on mutations in the sequence of the gene that causes the disease shows two decisions are achieved successfully, the first diagnose whether the patient has mutations of cancer or not using bioinformatics techniques the second classifying these mutations are related to breast cancer (e.g. BRCA1 and BRCA2) using backpropagation with mean square rate 0.0000001. Keywords-Gene sequence; Protein; Deoxyribonucleic Acid DNA; Malignant mutation; Bioinformatics; Back-propagation algorithm.
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Topological structure of complex predictions arXiv
Published: 2022-07-28T19:28:05Z
Authors: Meng Liu, Tamal K. Dey, David F. Gleich
Complex prediction models such as deep learning are the output from fitting machine learning, neural networks, or AI models to a set of training data. These are now standard tools in science. A key challenge with the current generation of models is that they are highly parameterized, which makes describing and interpreting the prediction strategies difficult. We use topological data analysis to transform these complex prediction models into pictures representing a topological view. The result is a map of the predictions that enables inspection. The methods scale up to large datasets across different domains and enable us to detect labeling errors in training data, understand generalization in image classification, and inspect predictions of likely pathogenic mutations in the BRCA1 gene.
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