Asthma is a chronic and airway-induced disease, causing the incidence of bronchus inflammation, breathlessness, wheezing, is drastically becoming life-threatening. Even in the worst cases, it may destroy the quality to lead. Therefore, early detection of asthma is urgently needed, and machine learning can help identify asthma accurately. In this paper, a novel machine learning framework, namely BOMLA ( B ayesian O ptimisation-based M achine L earning framework for A sthma) detector has been proposed to detect asthma. Ten classifiers have been utilized in the BOMLA detector, where Support Vector Classifier (SVC), Random Forest (RF), Gradient Boosting Classifier (GBC), eXtreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) are state-of-the-art classifiers. In contrast, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QLDA), Naive Bayes (NB), Decision Tree (DT), and K-Nearest Neighbor (KNN) are conventional popular classifiers. ADASYN algorithm has also been employed in the BOMLA detector to eradicate the issues created due to the imbalanced dataset. It has even been attempted to delineate how the ADASYN algorithm affects the classification performance. The highest accuracy (ACC) and Matthews’s correlation coefficient (MCC) for an Asthma dataset provide 94.35% and 88.97%, respectively, using BOMLA detector when SVC is adapted, and it has been increased to 96.52% and 93.04%, respectively, when ensemble technique is adapted. The one-way analysis of variance (ANOVA) has also been performed in the 10-fold cross-validation to measure the statistical significance. A decision support system has been built as a potential application of the proposed system to visualize the probable outcome of the patient. Finally, it is expected that the BOMLA detector will help patients in their early diagnosis of asthma.

BRIP1 (Breast Cancer 1 Interacting Helicase 1) is a tumor suppressor gene that has vital function in preserving the genetic stability by repairing DNA damage though have significant associations with the onset of breast cancer (BC) if mutated or overexpressed. In this study, the prognostic value of BRIP1 gene was evaluated and validated through bioinformatics approaches utilizing transcriptomic (mRNA expression) data from several BC databases. To determine the prognostic value, the expression level of mRNA transcript was analyzed in context of comparison between breast tumor and normal tissues regarding clinical features, breast tumor subtypes, promoter methylation status, correlation level, mutation frequency, and survival of BC patients. BRIP1 expression was found to be significantly overexpressed in various BC molecular subtypes (e.g. PAM50, Sorlie’s) and clinical status (estrogen and progesterone receptor) than associated normal tissues which correlated with prognosis. Also, in promoter methylation level, its expression was observed as upregulated-hypomethylated regarding various clinicopathological features. Multiple data mining exhibited positive correlation between BRIP1 and INTS2 (Integrator Complex Subunit 2) expressions in BC. Further, mutation analysis revealed that BRIP1 gene was altered by acquiring both somatic and germline mutations. In addition, a total of 42 mutations; 24 missense, 8 fusion, 7 truncating, and 3 inframe mutations in BC patients was detected in BRIP1 protein. Moreover, higher BRIP1 expression was found to be correlated with poor disease-specific, disease metastasis-free, relapse-free, and overall survivals of BC patients. Since, overexpression of BRIP1 was identified to be associated with different clinical features, breast tumor subtypes, promoter methylation status, and survival of BC patients that may provide a risk of ensuing malignant transformation. Thus, lower expression of BRIP1 might hinder BC prognosis. We consider that this analysis will present a proof for BRIP1 gene to be a noteworthy molecular biomarker for BC prognosis.

Introduction. Anemia is indeed a significant risk factor for children’s health as it affects growth retardation and has severe short and prolonged effects that follow in morbidity and death. Notwithstanding such ways to tackle anemia, the prevalence remains high in India and poses a severe public health concern. Objectives. The primary focus of this study was to find the prevalence and to determine the factors associated with the anemia of children under five years of age in India. Problem Statement. The increasing prevalence of childhood anemia and the life‐threatening consequences for millions of children in India are a major concern. Knowing the relevant associated factors with childhood anemia is essential to reduce the frequency and severity level. Study design. For analysis purposes, this study utilized a cross‐sectional study design. Methodology. Using the Indian Demographic and Health Survey 2015–16 data, we used chi‐squared and gamma tests to find the association. Then, we utilized multinomial logistic regression and ordinal logistic regression to find the better model and the influencing factors of anemia in India. Results. In our study, we have found that children with highly educated mothers were 36.7% less likely (OR = 0.633, P ≤ 0.001, 95% CI: 0.608, 0.658) to be higher anemic than the children with not educated mother. Children with moderate and severe anemic mothers were 163.3% (OR = 2.633, P ≤ 0.001, 95% CI: 2.565, 7.704) more likely to be higher anemic than the children with not anemic mother. Not stunting children were 21.9% (OR = 0.781, P ≤ 0.001, 95% CI: 0 .764, 0.797) less likely to be higher anemic than the stunting children. Children aged 36–59 months were 73.9% (OR = 0.361, P ≤ 0.001, 95% CI: 0.353, 0.369) less likely to be higher anemic than the children aged 6–24 months. Again, the ACI value revealed that ordinal logistic regression was a better‐fitted model for these data. Conclusion and contribution. The variables such as stunting, underweight, wasting, child age, size of the child, and source of drinking water were the most critical indicators for child anemia in India. In summary, our study result indicated the major socioeconomic and demographic factors associated with childhood anemia in India, which can help the policymaker to take quick decision to reduce the severity level.