The breast cancer susceptibility gene BRCA1 is well known for its function in double-strand break (DSB) DNA repair. While BRCA1 is also implicated in transcriptional regulation, the physiological significance remains unclear. COBRA1 (also known as NELF-B) is a BRCA1-binding protein that regulates RNA polymerase II (RNAPII) pausing and transcription elongation. Here we interrogate functional interaction between BRCA1 and COBRA1 during mouse mammary gland development. Tissue-specific deletion of Cobra1 reduces mammary epithelial compartments and blocks ductal morphogenesis, alveologenesis and lactogenesis, demonstrating a pivotal role of COBRA1 in adult tissue development. Remarkably, these developmental deficiencies due to Cobra1 knockout are largely rescued by additional loss of full-length Brca1. Furthermore, Brca1/Cobra1 double knockout restores developmental transcription at puberty, alters luminal epithelial homoeostasis, yet remains deficient in homologous recombination-based DSB repair. Thus our genetic suppression analysis uncovers a previously unappreciated, DNA repair-independent function of BRCA1 in antagonizing COBRA1-dependent transcription programme during mammary gland development.

Single Nucleotide polymorphisms are biological markers, helping researchers  to locate genes that are associated with various  diseases. When SNPs occur within a gene or in a regulatory region near a gene, they may play a more direct role in disease by affecting the gene's function. Most SNPs have no effect on health or development.Advancement in the field of genetics has resulted in the application of several techniques of molecular genetics in Pharmacogenomics. Nucleotide Polymorphisms (SNPs) holds the key in defining the risk of an individual’s susceptibility to various illnesses and response to drugs The body of human beings is composed of DNA which is a chemical molecule responsible for imparting phenotypic and genotypic characteristics to the individuals. The most recent advancement of molecular genetics, which has found application in forensic science, is the use of autosomal SNPs because they can provide information about the ancestral genetics of human beings.The primary aim of this research is to explore the significance of autosomal SNPs in forensic science through the identification of humans at a crime scene. A secondary qualitative research design has been selected for conducting this study. This secondary research is based on a systematic review of the studies which have provided an insight in the significance of autosomal SNPs in forensic sciences by using various Data search Engine. SNPs can be used in the forensic investigation for the identification of individuals present at the crime scene.

In this work, we adapt the fine-tuning algorithm of the Naïve Bayesian (FTNB) classifier to make it more suitable for imbalanced datasets. In particular, we boost misclassified instance probability terms by an amount that is disproportional to the harmonic mean of actual and predicted classes. The intuition is that discriminative attributes when the instance is misclassified would have small probability term pair values in both the actual class due to data scarcity and the predicted class due to weak correlation. Conversely, if both values are relatively high, then the attribute has good data coverage (support) and it should not be a cause for misclassification. Since the harmonic average is dominated by the smaller value and we have an imbalanced dataset, we should enact a large update if both or either term probabilities of actual and predicted classes are small. We used several benchmark datasets (60 different balanced and imbalanced datasets) to determine if the poor performance of the NB classifier is due to the scarcity of data and compared the performance of the proposed algorithm with NB, original FTNB, and other relatively new SOTA Ensemble Imbalanced Classifiers. Our empirical results reveal that the new proposed algorithm significantly outperforms all other classifiers.

Naïve Bayes (NB) classification performance degrades if the conditional independence assumption is not satisfied or if the conditional probability estimate is not realistic due to the attributes of correlation and scarce data, respectively. Many works address these two problems, but few works tackle them simultaneously. Existing methods heuristically employ information theory or applied gradient optimization to enhance NB classification performance, however, to the best of our knowledge, the enhanced model generalization capability deteriorated especially on scant data. In this work, we propose a fine-grained boosting of the NB classifier to identify hidden and potential discriminative attribute values that lead the NB model to underfit or overfit on the training data and to enhance their predictive power. We employ the complement harmonic average of the conditional probability terms to measure their distribution divergence and impact on the classification performance for each attribute value. The proposed method is subtle yet significant enough in capturing the attribute values’ inter-correlation (between classes) and intra-correlation (within the class) and elegantly and effectively measuring their impact on the model’s performance. We compare our proposed complement-class harmonized Naïve Bayes classifier (CHNB) with the state-of-the-art Naive Bayes and imbalanced ensemble boosting methods on general and imbalanced machine-learning benchmark datasets, respectively. The empirical results demonstrate that CHNB significantly outperforms the compared methods.