Computational Biology and Chemistry最新文献

{"title":"Dynamic comprehensive learning-based dung beetle optimizer using triangular mutation for polyps image segmentation","authors":"Mohamed Abd Elaziz ,&nbsp;Diego Oliva ,&nbsp;Alaa A. El-Bary ,&nbsp;Ahmad O. Aseeri ,&nbsp;Rehab Ali Ibrahim","doi":"10.1016/j.compbiolchem.2025.108474","DOIUrl":"10.1016/j.compbiolchem.2025.108474","url":null,"abstract":"<div><div>The process of early diagnosis of polyps is considered a critical point of preventive healthcare, as it can significantly improve the prognosis and treatment outcomes of patients with colorectal cancer. Since the Polyps are constructed in the colon, they can grow up to be cancerous over time. We present an alternative Polyps image segmentation approach according to multilevel thresholding techniques (MLTs) to ensure effective early polyp diagnosis. The developed MLT Polyps image segmentation method depends on improving the performance of the Dung beetle optimizer (DBO) algorithm based on the operators of Triangular Mutation (TMO), Comprehensive learning (CL), and dynamic update of the search domain. We conducted a comprehensive evaluation of the performance of the DCTDBO using eight Polyps images and compared it with other image segmentation methods, ensuring a rigorous and thorough process. The results show the high ability of the DCTDBO according to performance measures to segment the Polyps images. In terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity (FSIM), DCTDBO performed better than the basic version of the DBO and other methods. For example, the average of DCTDBO overall threshold levels and tested images in terms of FSIM, SSIM, and PSNR is 0.9668, 0.99217, and 28.9338, respectively. This indicates the influence of TMO and CL on enhancing the performance of DBO.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108474"},"PeriodicalIF":2.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DICCA-DTA: Diffusion and Contextualized Capsule Attention guided Factorized Cross-Pooling for Drug-Target Affinity prediction","authors":"Uma E.,&nbsp;Mala T.","doi":"10.1016/j.compbiolchem.2025.108472","DOIUrl":"10.1016/j.compbiolchem.2025.108472","url":null,"abstract":"<div><div>Drug-Target Affinity (DTA) prediction plays a crucial role in the drug discovery process by evaluating the strength of the interaction between a drug and its biological target, which is often a protein. Despite advancements in DTA prediction through deep learning, several fundamental challenges persist: (i) suboptimal information propagation in molecular graphs, limiting the effective representation of complex drug structures, (ii) accurately modeling the complex interactions between drug-binding sites and protein substructures, and (iii) prioritizing critical substructure interactions to enhance both accuracy and interpretability. To address these challenges, the DICCA-DTA framework is introduced, aiming to improve the contextual integration of molecular information and facilitate a more comprehensive representation of drug-target interactions in allopathic research. It employs a Diffused Isomorphic Network (DIN) to extract comprehensive drug features from molecular graphs, capturing both local substructures and global information. Furthermore, a Contextualized Capsule Attention Network (CCAN) module incorporates multi-head attention with capsule networks to capture both local and global protein sequence characteristics. The attention-guided Factorized Cross-Pooling (FCP) mechanism dynamically refines drug-protein interaction modeling by selectively emphasizing critical binding site interactions, thereby enhancing predictive accuracy. Explainable attention maps further reveal the most crucial drug-protein binding site interactions, providing transparent insights into the model’s decision-making process. Comprehensive evaluations across the Davis, KIBA, Metz and BindingDB datasets demonstrate the superior performance of the DICCA-DTA framework over existing state-of-the-art models. A case study on cancer-related protein interactions from the DrugBank database further demonstrates the framework’s precision in identifying key drug-protein affinities, reinforcing its potential to accelerate drug discovery and repurposing.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108472"},"PeriodicalIF":2.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification and validation of biomarkers in Alzheimer's disease based on machine learning algorithms and single-cell sequencing analysis","authors":"Yun Fan ,&nbsp;XiaoLong Wang ,&nbsp;Yun Ling ,&nbsp;QiuYi Wang ,&nbsp;XiBin Zhou ,&nbsp;Kai Li ,&nbsp;ChunXiang Zhou","doi":"10.1016/j.compbiolchem.2025.108475","DOIUrl":"10.1016/j.compbiolchem.2025.108475","url":null,"abstract":"<div><h3>Objective</h3><div>Alzheimer's disease (AD) is a complicated neurodegenerative disease with unknown pathogenesis. Identifying possible diagnostic markers of AD is essential to elucidate its mechanisms and facilitate diagnosis.</div></div><div><h3>Methods</h3><div>A total of 295 samples (153 AD and 142 normal) were analyzed from two datasets (GSE122063 and GSE132903) in the Gene Express Omnibus (GEO) database. Differentially expressed genes (DEGs) between groups were identified and dimensionality reduction was applied to identify feature genes (key genes) using three algorithms of machine learning including least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and Random forest (RF). In addition, we obtained sample data from single-cell RNA datasets GSE157827, GSE167490, and GSE174367 to classify cells into different types and examined changes in gene expression and their correlation with AD progression. Immunofluorescence assay was used to verify the expression of key genes in animal experiments.</div></div><div><h3>Results</h3><div>To identify diagnostic genes associated with AD, we analyzed two datasets and identified 379 DEGs which might be related to the onset of AD, and 115 of them were up-regulated and 264 down-regulated. Three algorithms of machine learning were adopted to reduce the dimensions of these DEGs and finally six core DEGs CD86, SCG3, VGF, PRKCG, SPP1, and TPI1 of AD were identified. Diagnostic analyses showed that SCG3 was substantially down-regulated in the AD group, and its AUC was higher in both the training and validation sets (0.845, 0.927, and 0.917, respectively). Transcriptome sequencing results further revealed that SCG3 expression was down-regulated in multiple cell types in the AD group and SCG3 expression in the hippocampus was found significantly reduced in the AD group.</div></div><div><h3>Conclusions</h3><div>This study systematically identified and validated the potential of SCG3 as an early diagnostic biomarker for AD through several technical strategies. The findings provided new biomarkers for early detection of AD and laid a foundation for future clinical applications.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108475"},"PeriodicalIF":2.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Amino acid sequence-based IDR classification using ensemble machine learning and quantum neural networks","authors":"Seok-Jin Kang ,&nbsp;Hongchul Shin","doi":"10.1016/j.compbiolchem.2025.108480","DOIUrl":"10.1016/j.compbiolchem.2025.108480","url":null,"abstract":"<div><div>Biologically traditional methods, such as the Uversky plot, which rely on hydrophobicity and net charge, have inherent limitations in accurately distinguishing intrinsically disordered regions (IDRs) from ordered protein regions. To overcome these constraints, we propose a novel ensemble framework integrating Machine Learning (ML), Deep Neural Networks (DNN), and Quantum Neural Networks (QNN) to enhance IDR classification accuracy. Notably, this study is the first to employ QNNs for IDR classification, leveraging quantum entanglement to model intricate feature interactions. Amino acid sequences were analyzed to extract biophysical features, including charge distribution, hydrophobicity, and structural properties, which served as inputs for the predictive models. ML was utilized for independent feature learning, DNN for hierarchical interaction modeling, and QNN for capturing high-order dependencies. Our meta-model demonstrated an accuracy of 0.85, surpassing individual classifiers and highlighting the importance of buried amino acids and feature interactions between scaled hydrophobicity and large, buried, and charged residues. This study advances computational protein science by demonstrating the applicability of QNNs in bioinformatics and establishing a robust framework for IDR classification.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108480"},"PeriodicalIF":2.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of novel ligands against GSK-3β: Molecular Docking, ADMET filtering, MD Simulations, MM-GBSA studies, and DFT analysis","authors":"Nachiket Joshi,&nbsp;RajaSekhar Reddy Alavala","doi":"10.1016/j.compbiolchem.2025.108478","DOIUrl":"10.1016/j.compbiolchem.2025.108478","url":null,"abstract":"<div><div>Glycogen synthase kinase-3β (GSK-3β) is a key enzyme involved in tau hyperphosphorylation, a key pathological mechanism leading to tau aggregation and thus, Alzheimer’s disease. It also has a significant role in neuroinflammation, induction of BACE1, and amyloid-β aggregation. Such crucial pathological events are involved intricately with the Alzheimer’s disease pathophysiology which makes this enzyme an attractive drug target. Designing a competitive ATP-site inhibitor of GSK-3β is the approach adopted here in an attempt to target tau pathology. Tetrazole is selected as a scaffold taking into account the previously reported inhibitors bearing nitrogen heterocycles which are at the heart of drug design strategies. A library of disubstituted tetrazole analogs was designed and molecular docking was performed. The top 30 molecules were then passed through several ADMET filters with a special preference given to BBB permeability. The hit molecules obtained after this were analyzed for their amino acid interactions. The docked complexes were used to perform MD simulations for 100<!--> <!-->ns and later MM-GBSA studies were performed with staurosporine and co-crystallized ligand as reference molecules. Furthermore, various chemical reactivity parameters and the HOMO-LUMO energy gap were analyzed with DFT studies using B3LYP functional. The best molecule obtained was <strong>TD30</strong>, revealing a docking score of -167.036, an average RMSD of 2.15<!--> <!-->Å for 100<!--> <!-->ns simulation time, average ΔG_Binding energy of -86.55 ± 4.45<!--> <!-->kcal/mol, and an HOMO-LUMO energy gap of 4.044<!--> <!-->eV; all corroborating the potential of <strong>TD30</strong> for future research and enzyme inhibition assays.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108478"},"PeriodicalIF":2.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SGTB: A graph representation learning model combining transformer and BERT for optimizing gene expression analysis in spatial transcriptomics data","authors":"Farong Liu ,&nbsp;Sheng Ren ,&nbsp;Jie Li ,&nbsp;Haoyang Lv ,&nbsp;Fenghui Jiang ,&nbsp;Bin Yu","doi":"10.1016/j.compbiolchem.2025.108482","DOIUrl":"10.1016/j.compbiolchem.2025.108482","url":null,"abstract":"<div><div>In recent years, spatial transcriptomics (ST) has emerged as an innovative technology that enables the simultaneous acquisition of gene expression information and its spatial distribution at the single-cell or regional level, providing deeper insights into cellular interactions and tissue organization, this technology provides a more holistic view of tissue organization and intercellular dynamics. However, existing methods still face certain limitations in data representation capabilities, making it challenging to fully capture complex spatial dependencies and global features. To address this, this paper proposes an innovative spatial multi-scale graph convolutional network (SGTB) based on large language models, integrating graph convolutional networks (GCN), Transformer, and BERT language models to optimize the representation of spatial transcriptomics data. The Graph Convolutional Network (GCN) employs a multi-layer architecture to extract features from gene expression matrices. Through iterative aggregation of neighborhood information, it captures spatial dependencies among cells and gene co-expression patterns, thereby constructing hierarchical cell embeddings. Subsequently, the model integrates an attention mechanism to assign weights to critical features and leverages Transformer layers to model global relationships, refining the ability of learned representations to reflect variations in spatial patterns. Finally, the model incorporates the BERT language model, mapping cell embeddings into textual inputs to exploit its deep semantic representation capabilities for high-dimensional feature extraction. These features are then fused with the embeddings generated by the Transformer, further optimizing feature learning for spatial transcriptomics data. This approach holds significant application value in improving the accuracy of tasks such as cell type classification and gene regulatory network construction, providing a novel computational framework for deep mining of spatial multi-scale biological data.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108482"},"PeriodicalIF":2.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ProAttUnet: Advancing protein secondary structure prediction with deep learning via U-Net dual-pathway feature fusion and ESM2 pretrained protein language model","authors":"Long Cheng ,&nbsp;Weizhong Lu ,&nbsp;Yiyi Xia ,&nbsp;Yiming Lu ,&nbsp;Jiyun Shen ,&nbsp;Zhiqiang Hui ,&nbsp;Yixin Xu ,&nbsp;Hongjie Wu ,&nbsp;Jing Chen ,&nbsp;Qiming Fu ,&nbsp;You Lu","doi":"10.1016/j.compbiolchem.2025.108429","DOIUrl":"10.1016/j.compbiolchem.2025.108429","url":null,"abstract":"<div><div>Protein secondary structure prediction remains a pivotal concern within the domain of bioinformatics. In this innovative research, we introduce a novel methodology to further enhance a protein prediction model grounded in single sequences. Our key contribution lies in integrating the state-of-the-art (SOTA) model ESM2, which hails from the field of universal protein language models. By leveraging ESM2, we are able to acquire residual embeddings and contact maps for the protein sequences under study. Regarding the model architecture, we employ a unique dual-way U-Net framework for effective feature fusion. This framework is complemented by the integration of a cross-attention mechanism, enabling the model to capture more comprehensive context information. Furthermore, In accordance with the distinctive characteristics of protein sequences, we incorporate a so-called GCU_SE module into both the encoder and the decoder components of the model. These innovative enhancements enable the ProAttUnet model to outperform the benchmark model SPOT-1D-Single by 1.6%, 3.5%, 1.0%, 4.6%, and 7.2% for ss3, and by 5.5%, 7.8%, 4.1%, 8.1%, and 10.1% for ss8 across five test sets (SPOT-2016, SPOT-2016-HQ, SPOT-2018, SPOT-2018-HQ and TEST2018, respectively). This significant improvement vividly demonstrates the effectiveness and novelty of our proposed model.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108429"},"PeriodicalIF":2.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Convolutional Neural Network approach to classify mitochondrial morphologies","authors":"Soumaya Zaghbani ,&nbsp;Lukas Faber ,&nbsp;Ana J. Garcia-Saez","doi":"10.1016/j.compbiolchem.2025.108477","DOIUrl":"10.1016/j.compbiolchem.2025.108477","url":null,"abstract":"<div><div>The morphology of the mitochondrial network is a major indicator of cellular health and function, with changes often linked to various physiological and pathological conditions. As a result, efficient methods to quickly assess mitochondrial shape in cellular populations from microscopy images in a quantitative manner are of high interest for the health and life sciences. Here, we present MitoClass, a deep learning-based software designed for automated mitochondrial classification. MitoClass employs a classification algorithm that categorizes mitochondrial network shapes into three classes: fragmented, intermediate, and elongated. By leveraging super-resolution images, we curated a comprehensive dataset for training, including both high- and low-resolution representations of mitochondrial networks. Using a Convolutional Neural Network (CNN) architecture, our model effectively distinguishes between different mitochondrial morphologies. Through rigorous training and validation, MitoClass provides a fast, accurate, and user-friendly solution for researchers and clinicians to assess the organization of the mitochondrial network as a proxy for studying organelle health and dynamics.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108477"},"PeriodicalIF":2.6,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The drug discovery candidate for targeting PARP1 with Onosma. Dichroantha compounds in triple-negative breast cancer: A virtual screening and molecular dynamic simulation","authors":"Mohamed J. Saadh ,&nbsp;Hanan Hassan Ahmed ,&nbsp;Radhwan Abdul Kareem ,&nbsp;Muktesh Chandra ,&nbsp;Lokesh Verma ,&nbsp;G.V. Siva Prasad ,&nbsp;Anurag Mishra ,&nbsp;Waam Mohammed Taher ,&nbsp;Mariem Alwan ,&nbsp;Mahmood Jasem Jawad ,&nbsp;Atheer Khdyair Hamad","doi":"10.1016/j.compbiolchem.2025.108471","DOIUrl":"10.1016/j.compbiolchem.2025.108471","url":null,"abstract":"<div><div>Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by the overexpression of poly-ADP ribose polymerase 1 (PARP1), a key enzyme in DNA repair. Targeting PARP1 with inhibitors presents a promising therapeutic strategy, particularly given the limited treatment options for TNBC. This study employed in silico methodologies to evaluate the pharmacokinetic and inhibitory potential of FDA-approved drugs and compounds derived from <em>Onosma. dichroantha</em> root extracts against PARP1. Virtual screening and molecular docking identified Midazolam, Olaparib, Beta-sitosterol, and 1-Hexyl-4-nitrobenzene as top candidates, exhibiting strong binding affinities of −10.6 kcal/mol, −9.9 kcal/mol, −6.83 kcal/mol, and −5.53 kcal/mol respectively. Molecular dynamics simulations (MDS) over 100 nanoseconds revealed that Beta-sitosterol formed the most stable complex with PARP1, demonstrating minimal structural deviations and robust hydrogen bonding. The Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) analysis further confirmed Beta-Sitosterol and Olaparib superior binding free energy (ΔG_bind= −175.43 kcal/mol and –180.8 kcal/mol respectively), highlighting its potential as a potent PARP1 inhibitor. ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling indicated that Beta-Sitosterol adheres to Lipinski's Rule of Five, with high intestinal absorption (95.88 %) and blood-brain barrier permeability (0.824), despite low water solubility. Protein-protein interaction analysis identified key PARP1-associated proteins, including CASP3, CASP7, and XRCC1, suggesting broader therapeutic implications. These findings underscore the potential of Beta-Sitosterol as a novel PARP1 inhibitor for TNBC treatment, combining computational validation with favorable pharmacokinetic properties. The study also highlights the utility of drug repurposing and plant-derived compounds in developing targeted therapies for TNBC, paving the way for further preclinical and clinical investigations.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108471"},"PeriodicalIF":2.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PathwayMind: An innovative tool and database for pathway perturbation analysis, uncovering critical pathways for drugs and target protein sets","authors":"Om Prakash Sharma","doi":"10.1016/j.compbiolchem.2025.108466","DOIUrl":"10.1016/j.compbiolchem.2025.108466","url":null,"abstract":"<div><div>Pathway enrichment analysis is a valuable tool for researchers aiming to understand the mechanisms underlying any specific drug or disease associated gene lists derived from biological assays or large-scale genome (omics) experiments. By employing this method, researchers can pinpoint biological pathways that exhibit a higher level of enrichment for a given set of genes than would be anticipated by random chance. These biological pathways play a crucial role in understanding the disease pathophysiology, therapeutic strategy, polypharmacological effects, synergistic mechanisms, and target engagement. However many of the available tools do not fulfill this requirement as most of the available tools consider a flat hierarchy of protein involvement in the pathway and do not consider topological information or importance of molecules in the pathway. Here we propose a novel method to enrich the molecular pathways and prioritize them based on their importance and crucial role in the biological function using the graph and evidence-based approach and customized datasets called PathwayMind. It includes 2648 pathways, 4539 biological events, 2465847 protein-protein interactions and 124717 gene-to-pathway relationships and the role of 3510 unique initial genes in 11,992 molecular pathways. The current manuscript comprises three major steps: The first step is about the data extraction and datasets creation for pathway enrichment, and the second steps comprises pathway perturbation analysis to identify most perturbed biological pathways and the third steps includes validation of this approach along with standalone tools and visualization algorithms which disclose the molecular involvement and improve the interpretability of the results. The end-to-end pathway analysis can be performed in a few minutes to provide complete insights of your target or drug of interest. The current PathwayMind tool and its datasets could be very useful for the molecular scientists and system biologists who are interested to understand the therapeutic effects of their drugs or understanding the involvement of biological pathways for specific gene sets which does not require any prior bioinformatics training.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108466"},"PeriodicalIF":2.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}