International Journal for Research in Bioscience
International Journal for Research in Bioscience Open Access | Double Blind Peer Review | Green Publications Welcome to IJRB The International Journal for Research in Bioscience (IJRB) is an international, open-access, double-blind peer-reviewed journal dedicated to publishing innovative and impactful research in bioscience and life sciences. The journal serves as a global platform for scientists, researchers, and academicians to share original discoveries, scientific insights, and advancements that contribute to the growth of modern bioscience research. Publication Frequency: Quarterly Publication Submit Your Research We welcome original research articles, review papers from researchers and scholars worldwide. Submit Manuscript
MACHINE LEARNING-BASED CLASSIFICATION OF BREAST CANCER USING GENEEXPRESSION PROFILES
Breast cancer remains one of theleading causes of cancer-related mortality worldwide, highlightingthe need for accurate diagnostic approaches and improved understanding of its molecularmechanisms. Advances in transcriptomic technologies have enabled large-scale analysis of geneexpression profiles, providing valuable opportunities for identifying molecular biomarkers associatedwith cancer development. In this study, gene expression data were analyzed to identify significantgenes differentiating tumor and normal breast tissue samples and to evaluate the effectiveness ofmachine learning models for breast cancer classification. The dataset consisted of 590 samples,including 529 tumor and 61 normal samples, with expression values measured for 17,814 genes.Differential gene expression analysis using a two-sample t-test was performed to identify informativegenes, and the top 500 statistically significant genes were selected as predictive features. Threemachine learning modelsLogistic Regression, Support Vector Machine (SVM), and RandomForestwere developed to classify tumor and normal samples based on the selected gene expression features.The dataset was divided into training and testing subsets using stratified sampling, and modelperformance was evaluated using accuracy and receiveroperating characteristic area under thecurve (ROC–AUC). The results demonstrated strong classification performance, with LogisticRegression and SVM achieving an accuracy of 97.46% and an ROC–AUC of 0.997, while RandomForest achieved an accuracy of 96.61% and an ROC–AUC of 0.994. These findings highlight thepotential of combining gene expression analysis with machine learning techniques for breast cancerclassification and biomarker discovery.
Copyright (c) 2026 International Journal for research in Bioscience
PEST–PREDATOR DYNAMICS AND THEIR EFFECTS ON FLOWER ABORTION IN GREENHOUSETOMATO PLANTS
This is because insect pests often influence greenhouse tomato production, but the interactions amongtheabundance of the pests, the predators, and the damage inflicted by the pests, and their effects onreproductive performance have not been fully studied under controlled cultivation settings. This studyexamined temporal patterns in pest and predator populations and evaluated their associations withplant damage and flower abortion in greenhouse tomato plants. Observational plant-level datacollected from greenhouse compartments over five sampling weeks were analyzed. The final datasetconsisted of 800 observations obtained by integrating pest, predator, and flower datasets using sharedplant identifiers. Descriptive statistics were used to summarize biological variables, while Pearsoncorrelation analysis and multiple linear regression were applied to assessrelationships among pests,predators, plant damage indicators, and reproductive traits. Pest and predator populations werehighest during the early sampling period and declined in later weeks. Necrotic rings showed thestrongest positive correlation withflower abortion (r = 0.403). Regression analysis indicated thatnecrotic rings and sampling week were significant predictors of flower abortion, whereas pestabundance showed weaker associations. These findings suggest that plant damage indicators and temporal dynamics play an important role in reproductive loss in greenhouse tomato systems.
ASSESSMENT OF PHYSICOCHEMICAL WATER QUALITY INDICATORS IN POLLUTEDAQUATIC ECOSYSTEMS
Anthropogenic activities are causing environmental pollution to aquatic ecosystems and this type ofpollution has the potentialto alterthe water quality and stability of the ecosystem considerably. Theanalysis of physicochemical indicators and toxic pollutants is thus needed to study the dynamics ofpollution in the water bodies. The current research examined the parameters of physicochemicalwater quality and heavy metals in the water bodies of aquatic ecosystems based on a dataset of 1000observationstaken in five sites of monitoring. The important parameters analyzed were the pH,turbidity, temperature, dissolvedoxygen, biochemical oxygen demand, and the concentrations of lead,mercury and arsenic. Descriptive and comparative analyses have been doneto assess the variabilityof water quality indicators and how each of them is associated with the level of pollution at themonitoring sites. The findings indicated that the water conditions were mostly neutral and slightlyalkaline withamean pH of 7.25andanaverage turbidity of 10.22 NTU, which is a range of suspendedsolids. Dissolved oxygen was 5.93 mg/L and biochemical oxygen demand was 5.48 mg/L, whichindicated moderate conditions of organic pollution. The traces of heavy metals were also detectedindifferent locations, which indicates a possible level of contamination in the observed environments.The combination of the analysis of physicochemical indicators and metal content gave a full pictureof the pollution trend and the environment. These indicators should be closely kept and monitoredcontinuously to help in the sound management of the environment and the health of aquaticecosystems.
TRANSCRIPTOMIC ANALYSIS OF DIFFERENTIALLY EXPRESSED GENES IN ANTIOXIDANT ENZYME MUTANTS
Reactive oxygen species (ROS) are key regulators of cellularsignallingand stress responses in plants.Antioxidant enzymes maintain redox homeostasis by controlling ROS accumulation. Mutationsaffecting antioxidant pathways can disrupt cellular balance and trigger extensive transcriptionalchanges. Understanding the transcriptional responses associated with antioxidant enzyme mutants isimportant for elucidating molecular mechanisms of oxidative stress regulation. The present studyaimed to identify differentially expressed genes and regulatory mechanisms associated withantioxidant enzyme mutants using transcriptomic data analysis. A secondary transcriptomic datasetobtained from a publicly available repository wasanalysedto evaluate gene expression changesacross antioxidant-related mutants,including vtc1, vtc2, cat2, aox1a, tapx, and sal1. Differentiallyexpressed genes were identified based on fold-change patterns, followed by functional annotation todetermine associated biological processes. Promoter analysis was conducted to identify ROS-responsive transcription factors and potential regulatory motifs controlling candidate geneexpression.The results showed that there are significant changes in the expression of genes inantioxidant enzyme mutants. The genes are related to oxidative stress response, metabolic regulation,signal transduction, and homeostasis. Both unique and common changes were found in the mutants.This implies that there are common oxidative stresssignallingpathways. The promoter analysisshowed that many ROS-responsive transcription factors are related to differentially expressed genes.The results showed that disruption of antioxidant enzyme pathways causes extensive transcriptionalreprogramming related to stress response genes. The results can be used to understand the regulationof genes depending on redox status and can be used to improve stress tolerance in plants.
STATISTICAL EVALUATION OF PHYSICOCHEMICAL WATER QUALITY PARAMETERS FOR ENVIRONMENTAL MONITORING
Water quality assessment is essential for ensuring safe water resources and maintainingenvironmental sustainability. Physicochemical parameters are widely used to evaluate the chemicalcharacteristics of water and to detect possible contamination. The present study examines thestatistical relationships among major physicochemical water quality parameters and their associationwith water potability. The analysis was conducted using a secondary dataset consisting of 10,000water samples that included measurements of pH, hardness, dissolved solids, chloramines, sulfate,conductivity, organic carbon, trihalomethanes, and turbidity along with a potability indicator.Descriptive statistics were used to summarize the distribution of these parameters, while correlationanalysis was performed to explore relationships among them. In addition, a comparative analysis wasconducted to identify differences in parameter values between potable and non-potable watersamples. The results indicated noticeable variability across several parameters, particularly dissolvedsolids and sulfate concentrations. Correlation analysis revealed moderate associations amongmineral related parameters, suggesting that multiple environmental factors influence water chemistry.The comparativeanalysis further showed that certain parameters, including trihalomethanes, sulfate,and organic carbon, differed significantly between potable and non-potable samples. Overall, thefindings demonstrate that statistical evaluation of physicochemical indicators can provide valuableinsights into water quality conditions. Such analyses can support environmental monitoring effortsand contribute to more effective water resource management and public health protection.