Analysis of high-dimensional data, with more features ($p$) than observations ($N$) ($p>N$), places significant demand in cost and memory computational usage attributes. Feature selection can be used to reduce the dimensionality of the data. We used a graph-based approach, principal component analysis (PCA) and recursive feature elimination to select features for classification from RNAseq datasets from two lung cancer datasets. The selected features were discretized for association rule mining where support and lift were used to generate informative rules. Our results show that the graph-based feature selection improved the performance of sequential minimal optimization (SMO) and multilayer perceptron classifiers (MLP) in both datasets. In association rule mining, features selected using the graph-based approach outperformed the other two feature-selection techniques at a support of 0.5 and lift of 2. The non-redundant rules reflect the inherent relationships between features. Biological features are usually related to functions in living systems, a relationship that cannot be deduced by feature selection and classification alone. Therefore, the graph-based feature-selection approach combined with rule mining is a suitable way of selecting and finding associations between features in high-dimensional RNAseq data.

Background: High-throughput sequencing generates large volumes of biological data that must be interpreted to make meaningful inference on the biological function. Problems arise due to the large number of characteristicsp(dimensions) that describe each record [n] in the database. Feature selection using a subset of variables extracted from the large datasets is one of the approaches towards solving this problem.Methodology: In this study we analyzed the transcriptome ofGlossina morsitans morsitans(Tsetsefly) antennae after exposure to either a repellant (δ-nonalactone) or an attractant (ε-nonalactone). We identified 308 genes that were upregulated or downregulated due to exposure to a repellant (δ-nonalactone) or an attractant (ε-nonalactone) respectively. Weighted gene coexpression network analysis was used to cluster the genes into 12 modules and filter unconnected genes. Discretized and association rule mining was used to find association between genes thereby predicting the putative function of unannotated genes.Results and discussion: Among the significantly expressed chemosensory genes (FDR < 0.05) in response to Ɛ-nonalactone were gustatory receptors (GrIA and Gr28b), ionotrophic receptors (Ir41a and Ir75a), odorant binding proteins (Obp99b, Obp99d, Obp59a and Obp28a) and the odorant receptor (Or67d). Several non-chemosensory genes with no assigned function in the NCBI database were co-expressed with the chemosensory genes. Exposure to a repellent (δ-nonalactone) did not show any significant change between the treatment and control samples. We generated a coexpression network with 276 edges and 130 nodes. Genes CAH3, Ahcy, Ir64a, Or67c, Ir8a and Or67a had node degree values above 11 and therefore could be regarded as the top hub genes in the network. Association rule mining showed a relation between various genes based on their appearance in the same itemsets as consequent and antecedent.

Mental health challenges, particularly among youth, are compounded by stigma and limited access to professional care. This has driven demand for scalable digital solutions like chatbots. This study introduces a sentiment analysis‐based model to assess user satisfaction with mental health chatbots, analyzing 82 102 reviews from six popular apps on Google Play and Apple's App Stores. A multi‐class sentiment classification of positive, negative, and neutral was implemented, enhanced by Synthetic Minority Over‐sampling Technique for class balancing, comparing five traditional machine learning models with Bidirectional Encoder Representations from Transformers, a transformer model. Random Forest achieved 98.18% accuracy among traditional models, while BERT outperformed all with 99.17% accuracy, surpassing prior benchmarks. Aspect‐based analysis revealed that Emotion and Usability drive positive feedback, while Reliability issues fuel negative sentiments, offering actionable insights for developers to enhance chatbot design. This work advances digital mental health research by integrating multi‐class classification, transformer models, and aspect‐based analysis, demonstrating a scalable framework for evaluating user feedback.

Cyberbullying is a deliberate and widespread kind of online harassment, which is hard to detect due to its subtextual nature, sarcastic overtones, and situational expressions. Traditional methods of detection, based on the use of key-word filters, manual filtering, or primitive machine-learning algorithms, are often unsuccessful at detecting these complexities, thus limiting the quality and the overall generalizability of such systems. In this study, an integrated framework to detect cyberbullying is proposed and combines the contextual embeddings, sentiment polarity and emotional cues to enhance the interpretative richness and strength. A curated dataset of 99,990 social media posts from Kaggle was pre-processed so that anonymity and linguistic integrity could be guaranteed. Fine-tuned RoBERTa variants were used to extract emotion, sentiment and contextual representations. The resulting embeddings were subsequently reduced using a Variational Autoencoder to create a 64-dimensional latent representation, which was appended with the emotion and sentiment features to create a unified multimodal feature representation. This fused representation was classified with a regularized deep-neural-network with residual connections, batch normalization and dropout as overfitting mitigation strategies. The model achieved 99.49% accuracy and a macro F1 of 0.9941 in internal validation and 95.91% accuracy and a macro F1 of 0.9412 in external evaluation. The interpretation was maintained by using Integrated Gradients which provided clear attribution to predict results. The system was deployed as a browser-extension which demonstrates scalable, real-time detection and transparent moderation; affirming the utility of emotion-aware and context-rich models for cyberbullying detection.