Introduction: Neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease, epilepsy, and multiple sclerosis are particularly challenging due to their complex pathophysiology and the relative lack of effective treatments Methods: Traditional drug discovery tools are costly and time-consuming, which necessitates the adoption of newer approaches. Artificial intelligence (AI) has emerged as a transformative technology in neurotherapeutics, accelerating drug discovery, drug repurposing, and personalized medicine Results: AI-driven strategies leverage extensive genomic, proteomic, and clinical trial data to identify novel drug targets, rationalize molecular design, and predict drug efficacy and toxicity. Deep learning algorithms uncover intricate biological interactions and support the identification and validation of drug candidates. AI-driven natural language processing enables automated extraction of data from the literature, thereby accelerating research. AI also facilitates drug repurposing through comprehensive analysis of large drug target networks, significantly reducing development timelines. AI driven clinical trial optimization improves patient recruitment, protocol design, and real time monitoring through predictive analytics. Discussion: Challenges such as data standardization, regulatory compliance, and model interpretability must be addressed to ensure effective integration of AI into drug development. Continued advances in AI, automation, robotics, and quantum computing are expected to further refine neurological drug discovery and personalized therapeutic approaches. Autonomous laboratories integrating AI with high throughput screening are likely to transform neurodrug development and personalized therapy design. Conclusion: By accelerating treatment development, AI represents a paradigm shift in the fight against neurological diseases through the integration of precision medicine and real time approaches.

Introduction/Objective: Interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) play a critical role in gene regulation and disease mechanisms. However, most existing prediction models rely solely on sequence features, overlooking RNA secondary structures that are essential for accurate interaction prediction. This study introduces LncMiRPath, a Transformer-based framework that integrates both sequence and structural information to enhance predictive performance. Methods: We developed LncMiRPath using a dual-input Transformer architecture that incorporates lncRNA and miRNA sequences alongside their predicted secondary structures. Datasets were obtained from LncBase v3, ENCORI, and miRcode. Secondary structures were inferred using IPknot and represented in dot-bracket notation. We compared three model variants—sequence-only, structure-only, and combined models—using accuracy, precision, recall, and area under the curve (AUC) as performance metrics. Results: LncMiRPath outperformed all baseline models, achieving an AUC of 95% on the curated dataset, demonstrating the effectiveness of integrating structural features. On the independent LncRNASNPv2 dataset, the model maintained strong generalization capability with an AUC of ~91%. Discussion: These results underscore the importance of incorporating RNA secondary structure, a factor often neglected in previous studies. By capturing complementary sequence and structural signals, LncMiRPath not only improves prediction accuracy but also enhances biological interpretability. Although structure inference relies on computational tools such as IPknot, consistent performance across multiple datasets supports the robustness and translational potential of the proposed approach. Future validation with experimental structure data may further strengthen the model. Conclusion: LncMiRPath represents a robust and biologically informed framework for predicting lncRNA–miRNA interactions by jointly leveraging sequence and structural features. This approach advances RNA computational biology and provides a promising tool for RNA-based therapeutic research.

Introduction: Urinary cancer continues to be a public health hazard throughout the world, indicating that new classification methods for the detection in its early phase are necessary. The Surveillance, Epidemiology, and End Results (SEER) urinary cancer dataset presents a unique challenge in part due to heterogeneous data, variation in clinical features, and missing data, which affects patient outcomes. Methods: This study presents a solution to find missing data using a Random Forest Regressor for numerical data and a Random Forest Classifier for categorical data. It then describes a hybrid analysis combining Analysis of Variance (ANOVA) and Recursive Feature Elimination (RFE) for feature selection in large datasets. Urinary cancer survival classification was performed using seven traditional machine learning models, including Logistic Regression, Naive Bayes, LightGBM, XGBoost, CatBoost, Random Forest, and Decision Tree. Results: The experimental results show that the proposed method demonstrates the highest performance when compared to traditional classifiers. The proposed stacked ensemble model of base layers, which include LightGBM, CatBoost, and TabNet with a meta-layer of Logistic Regression, achieved the highest accuracy of 0.9886 and a ROC score of 0.9889. Discussion: The existing techniques have limited predictive accuracy, poor handling of complex data, and a lack of interpretability needed for clinical decisions, and the proposed stacked ensemble model successfully overcomes these limitations by utilizing a hybrid method of feature selection and ensemble learning for greater robustness. Conclusion: To promote model transparency and explainability, we implement a hybrid Shapley Additive explanations (SHAP) - Local Interpretable Model-agnostic Explanations (LIME) explainer. The results demonstrated improvement in classification accuracy and contributed to understanding model decisions. Overall, the framework was effective in predicting and analyzing urinary cancer survival.

Introduction: The heterogeneity in tumours poses significant challenges to the accurate prediction of cancer stages, necessitating the expertise of highly trained medical professionals for diagnosis. Over the past decade, the integration of deep learning into medical diagnostics, particularly for predicting cancer stages, has been hindered by the black-box nature of these algorithms, which complicates the interpretation of their decision-making processes. Methods: This study seeks to mitigate these issues by leveraging the complementary attributes found within functional genomics datasets (including mRNA, miRNA, and DNA methylation) and stained histopathology images. We introduced the Extended Squeeze- and-Excitation Multiheaded Attention (ESEMA) model, designed to harness these modalities. This model efficiently integrates and enhances the multimodal features, capturing biologically pertinent patterns that improve both the accuracy and interpretability of cancer stage predictions. Results: Our findings demonstrate that the explainable classifier utilised the salient features of the multimodal data to achieve an area under the curve (AUC) of 0.9985, significantly surpassing the baseline AUCs of 0.8676 for images and 0.995 for genomic data. Conclusion: Furthermore, the extracted genomics features were the most relevant for cancer stage prediction, suggesting that these identified genes are promising targets for further clinical investigation.

Introduction: Multi-omics data integration has transformed personalized medicine, providing a comprehensive understanding of disease mechanisms and informed precision therapeutic options. Multi-omics data generated for the same samples/patients can help in getting insights into the flow of biological information at several levels, thereby providing in-depth information regarding the molecular mechanisms underlying pathological conditions. Multi-omics integration plays a pivotal role in personalized medicine by providing comprehensive insights into the complex biological systems of individual patients. This review provides a comprehensive account of the current and future progress brought into multi-omics methodologies, promising to refine diagnostics and therapeutic strategy by integrating genomic, transcriptomic analyses, proteomics approaches and metabolome screens. Methods: A literature search was performed in PubMed using keywords like genomics, proteomics, transcriptomics, metabolomics, multi-omics, and precision medicine to identify published research articles. A thorough review of all results was then conducted, and their results and conclusions were compiled and summarized. Results: By analyzing various omics layers, such as genomics, transcriptomics, proteomics, and metabolomics, multi-omics approaches enable the identification of patient-specific molecular traits and the discovery of new clinical therapeutics for diseases. Integration of various data types augments diagnostics, optimizes therapeutic regimens and supports personalized medicine according to an individual patient profile. Conclusion: Integration of multi-omics data and its applications in various fields, such as cancer research, helps in optimizing patient-specific treatment and improvement of patient health. With time, as these technologies reach more people, they stand to democratize precision medicine and hopefully bridge health disparities. In conclusion, the present review highlights multiomics data integration as a transformative step towards personalized medicine and ultimately changing patient care from empirical-based to precision or individualized.

An error was made in the research paper titled "Elucidating the Functional Role of Predicted miRNAs in Post- Transcriptional Gene Regulation Along with Symbiosis in Medicago truncatula." There is a problem with the number as it does not match the main manuscript with the abstract, which was published in Current Bioinformatics, 2020, Vol. 15, No. 2 [1]. Details of the error and a correction are provided here. Original: Abstract: Background: microRNAs are small non-coding RNAs which inhibit translational and post-transcriptional processes whereas long non-coding RNAs are found to regulate both transcriptional and post-transcriptional gene expression. Medicago truncatula is a well-known model plant for studying legume biology and is also used as a forage crop. In spite of its importance in nitrogen fixation and soil fertility improvement, little information is available about Medicago noncoding RNAs that play important role in symbiosis. Objective: In this study we have tried to understand the role of Medicago ncRNAs in symbiosis and regulation of transcription factors. Methods: We have identified novel miRNAs by computational methods considering various parameters like length, MFEI, AU content, SSR signatures and tried to establish an interaction model with their targets obtained through psRNATarget server. Results: 149 novel miRNAs are predicted along with their 770 target proteins. We have also shown that 51 of these novel miRNAs are targeting 282 lncRNAs. Conclusion: In this study role of Medicago miRNAs in the regulation of various transcription factors are elucidated. Knowledge gained from this study will have a positive impact on the nitrogen fixing ability of this important model plant, which in turn will improve the soil fertility. Corrected: Abstract: Background: Non-coding RNAs (ncRNAs) are important regulators of gene expression. MicroRNAs (miRNAs) are small ncRNAs, which inhibit translational and post-transcriptional processes, whereas long ncRNAs regulate both transcriptional and post-transcriptional gene expression. Medicago truncatula is a well-known model plant for studying legume biology. Despite its importance in nitrogen fixation, little information is available about Medicago ncRNAs that play an important role in symbiosis. Objective: This study aims to predict the miRNAs and their targets from the genome of M. truncatula and elucidate their roles in symbiosis and transcriptional regulation. Methods: We have developed a computational method to identify miRNAs in M. truncatula. In addition, we have also predicted the targets of these miRNAs involved in various biological processes and established an interaction model for symbiosis. Results: We have predicted 186 miRNAs, of which 165 are novel. Additionally, 770 proteins targeted by these miRNAs are also predicted. We show that 51 of these novel miRNAs target 282 lncRNAs. The symbiosis-related gene regulation is explored through analyzing the interactions between predicted miRNAs and their nodulin target proteins. Conclusion: Knowledge gained from this study will enhance our understanding of the nitrogen-fixing ability of this important model plant, which in turn will improve soil fertility. We regret the error and apologize to readers. The original article can be found online at https://www.eurekaselect.com/article/101155

introduction: Esophageal cancer is among the most lethal malignancies of the gastrointestinal tract, posing a serious threat to human health. Network pharmacology has provided novel insights into the pathogenic mechanisms and therapeutic strategies for esophageal cancer. However, accurately and efficiently identifying active compounds using network pharmacology remains challenging, particularly when analyzing complex herbal prescriptions. methods: To address the limitations in current virtual screening algorithms for identifying the active compounds related to esophageal cancer from traditional Chinese medicine (TCM) prescriptions, this study proposes a new algorithm named DS-MCGR, which integrates Dempster-Shafer evidence theory with Molecular Characterization and Graph Representation. This virtual screening algorithm comprises three steps: building a standard database, training the model, and screening active compounds in prescriptions. results: Extensive experiments were conducted using 3-fold, 5-fold, 8-fold, and 10-fold crossvalidation on the collected datasets. The results show that DS-MCGR consistently outperforms 11 classical machine learning algorithms and 7 deep learning models across most evaluation metrics. Under 3-fold cross-validation, DS-MCGR achieved the best performance with a Specificity of 0.9717, a Kappa of 0.9167, an MCC of 0.9173, an F1 score of 0.938, a PR-AUC of 0.9746, and an ROC-AUC of 0.9889. The sensitivity was 0.958, ranking second. discussion: Case studies on pomegranate and Salvia miltiorrhiza further validated the practical utility of DS-MCGR. The model could accurately identify the active compounds for treating esophageal cancer. conclusion: DS-MCGR significantly improves prediction accuracy by integrating chemical and structural features, offering a promising tool for discovering anticancer components from TCM and advancing drug discovery for esophageal cancer.

Introduction: The integration of cell line features and drug features in computational Cancer Drug Response (CDR) prediction methods enables a nuanced understanding of cellular responses and drug effects, which may lead to improvements in drug discovery and precision oncology. It helps identify promising drug candidates for experimental validation, avoid treatments that are unlikely to benefit a patient, and reduce unnecessary exposure to toxic drugs. Methods: In this paper, we propose DCGPert-CDR, which integrates drug structural features, cell line multi-omics data, and target gene perturbation profiles for predicting IC50 responses. The methodology involves the extraction of cell line multi-omics data, including genomics, transcriptomics, and epigenomics, together with the molecular structural features of the drug. The gene perturbation profiles are computed from transcriptional changes of the prioritized target genes before and after the drug treatment. A graph clustering approach, followed by network propagation, is applied to prioritize drug target genes. The resultant feature vectors are concatenated and fed into a prediction module, consisting of a ResNet, which predicts the IC50 values of drugs across various cancer cell lines. Results: DCGPert-CDR produces promising results when compared to similar methods, with Pearson’s correlation rp of 0.841 and Spearman’s correlation rs of 0.786 computed between predicted and actual IC50 values, while for other methods, rp was in the range of 0.768 to 0.8183 and rs was between 0.735 to 0.757. Drugs such as Foretinib, Crizotinib, Tivozanib, SNX-2112, and PHA- 665752 are found to be most sensitive after analyzing the predicted response values across various cancer cell lines. Discussion: The improved results indicate that the proposed method effectively predicts responses that closely match the known IC50 values. Case studies are conducted on 24 TCGA cancer types, also revealing sensitive drugs for each cancer type, which are corroborated with clinical evidence. Dependence on the availability of drug and cell line data, as well as the absence of real-time data validation, remains a key challenge. Conclusion: The method can reliably capture the relationship between drugs and cell lines, indicating its potential utility in predicting drug sensitivity. The method effectively identified the most sensitive drugs among individual cancer types.

Introduction: Emerging evidence highlights the critical role of gut microbes in the pathogenesis of autism spectrum disorder through interactions within the microbiota-gut-brain axis. Methods: This study conducted systematic bioinformatics analysis on 16S rRNA (V3-V4 region) sequencing data from 78 children with autism spectrum disorder and 68 neurotypical children from the NCBI database. Results: Microbial composition analysis revealed significant changes between patients with autism spectrum disorder and healthy controls across phylum-level and genus-level classifications. Elevated Bacteroidota/Firmicutes ratio was found in children with autism spectrum disorder compared to healthy controls. Microbial diversity analysis showed significant intergroup divergence: autism spectrum disorder subjects exhibited significantly elevated α-diversity, mainly reflected in the Chao1 index. Moreover, significant structural separation was observed between these two groups. LefSe analysis demonstrated distinct enrichment of Bacteroidota and Actinobacteriota at the phylum level, and Bacteroides, Prevotella, and Megamonas at the genus level in children with autism spectrum disorder. Conversely, control-associated enrichment of Firmicutes (phylum), along with genera Ruminococcus and Escherichia-Shigella, showed depletion in children with autism spectrum disorder. Functional prediction via PICRUSt2 demonstrated autism spectrum disorder-specific upregulation of cofactor and vitamin metabolism, contrasting with enrichment of terpenoid, polyketide, and xenobiotic degradation pathways in healthy controls. Discussion: These findings substantiate the microbiota-gut-brain axis hypothesis in the pathogenesis of autism spectrum disorder, revealing microbial composition and functional shifts potentially linked to nutritional compensation and inflammatory modulation. Conclusion: Gut microbial dysbiosis in autism spectrum disorder, characterized by altered microbial diversity and community structure, impairs xenobiotic metabolism, leading to neurotoxin accumulation that exacerbates core symptoms.

Introduction: Immune Checkpoint Inhibitors (ICIs) have revolutionized cancer therapy, offering clinical benefits across multiple cancer types. However, patient responses remain highly variable, underscoring the urgent need for robust biomarkers to characterize immunotherapy outcomes. Methods: To capture variations in immunotherapy responses, we developed a pathway-masked approach to construct a Phenotype-Specific Network (PSN) for biomarker discovery. The method consists of two key steps: pathway ranking and selection. First, we applied an improved strategy, Concordance Enrichment Analysis (CEA), to rank Reactome pathways, benchmarking its performance via network modularity and gene set coverage. Next, gene set distance and pathway semantic similarity were integrated to define thresholds for selecting phenotype-specific pathways. Genes from these pathways were used as a mask to extract a PSN, represented as a subnetwork of Protein- Protein Interactions (PPIs). Topological analysis and network propagation were then performed to identify critical molecular determinants, which, along with their neighbors, were developed into informative gene signatures. Results: The proposed CEA outperforms established pathway ranking methods. Two pathways, selected based on gene-set distance and pathway semantic similarity, were used to construct a PSN comprising 148 genes. Within this network, ZAP70 emerged as a gene with high betweenness centrality, associated with multiple T-cell receptor modules. Heat diffusion analysis centered on PD1 and CTLA4 consistently highlighted ICOSLG, a key ligand in the ICOS–ICOSLG axis, which is involved in T-cell costimulation. ZAP70 and ICOS, together with their neighboring genes, were used to construct gene signatures. These signatures showed moderate predictive performance for immunotherapy response, with the ICOS-related signature notably outperforming others in predicting progression- free survival. Discussion: This study highlights a pathway-masked PSN strategy that integrates transcriptomic data with biological networks to capture phenotype-specific insights into the immunotherapy response. By combining pathway semantics and network topology, this approach provides a conceptually novel framework for biomarker discovery. Taken together, this strategy contributes to a systems-level perspective with strong translational relevance, offering new opportunities to inform clinically meaningful applications. Conclusion: This pathway-masked framework enables the identification of the phenotype-specific network and key molecular regulators associated with immunotherapy response variations. Based on these findings, we have derived gene signatures that hold potential for improving clinical decisionmaking in immunotherapy.