International Journal of Data Mining and Bioinformatics (7 papers in press)

Special Issue on: The Development of Novel Integrative Bioinformatics Based Machine Learning Techniques and Multi Omics Data Integration Part 2

• In silico study discerns PIH1D1 and p53 to be promising prognostic markers for children's brain cancer  
  by Dhiraj Kumar Singh, Prashant Ranjan, Sahar Qazi, Bimal Prasad Jit, Amit Kumar Verma, Riyaz Ahmad Mir 
  Abstract: Genetic alterations in normal brain cells lead to the development of brain tumours (BT). The incidence of newly diagnosed cases is on the rise over time. Understanding the molecular biology of paediatric brain tumours is crucial for advancing novel therapeutic approaches to prevent or effectively manage this disease. The R2TP complex, a conserved co-chaperone from yeast to mammals, including RUVBL1, RUVBL2, PIH1D1, and RPAP3 in humans, plays a crucial role in the assembly and maturation of various multi-subunit complexes. This study evaluates the expression of PIH1D1 and p53 in paediatric brain cancers using The Cancer Genome Atlas (TCGA) data through the UALCAN. Our analysis revealed elevated expression levels of PIH1D1 in paediatric brain tumours across all age groups compared to normal tissues, suggesting its potential as an early detection marker and a prognostic indicator. Additionally, P53 emerged as a promising target for brain tumour treatment, warranting exploration for age-specific applications.
  Keywords: R2TP; PIH1D1; paediatric brain tumour; TCGA; UALCAN; CBTTC.
  DOI: 10.1504/IJDMB.2025.10067136
   
  
• ICEP and ILEP: two new approaches to identify community of complex biological network  
  by Mamata Das, K. Selvakumar , P.J.A. Alphonse 
  Abstract: Understanding the internal modular organization of protein-protein interactions is crucial for deciphering molecular-level biological processes. Recognition of network communities enhances our comprehension of the biological origins of disease pathogenesis. This research introduces two innovative community detection algorithms, Iterative Credit-Edge Pruning (ICEP) and Iterative Load-Based Edge Pruning (ILEP), designed to identify communities within complex biological networks. Our algorithms are evaluated using real-world data from the Omicron dataset, and their performance is compared with four established algorithms: Girvan-Newman, Louvain, Leiden, and the Label Propagation algorithm. Validation of the community structures is achieved through modularity. Among the techniques compared, our proposed method, ICEP, stands out with the highest modularity score of 0.885, outperforming all other approaches. The alternative method, ILEP, also achieves a notable modularity score of 0.698, surpassing the Girvan Newman method. By implementing ICEP and ILEP, we gain profound insights into the structural organization and interconnections within the Omicron virus.
  Keywords: protein interaction network; omicron; community detection; modularity; graphlet; centrality.
  DOI: 10.1504/IJDMB.2025.10067341
   
  
• BMSD-CDE: a robust community detection ensemble method for biomarker identification  
  by Bikash Baruah, Manash P. Dutta, Subhasish Banerjee, Dhruba K. Bhattacharyya 
  Abstract: Community detection algorithms (CDAs) are crucial for identifying cohesive groups within complex networks. However, individual CDAs often fall short of accurately uncovering all hidden communities due to their inherent biases and limitations. These algorithms are typically designed with specific objectives, which may inadvertently lead to the oversight of certain community types, resulting in partial or imprecise outcomes. To address these limitations, we propose BMSD-community detection ensemble (CDE), a novel ensemble method that integrates six prominent CDAs FastGreedy, Infomap, LabelProp, LeadingEigen, Louvain, and Walktrap. By strategically combining the outputs of these diverse algorithms using p-value references and elite genes, BMSD-CDE enhances the accuracy and robustness of community detection. 2 B. Baruah et al. This ensemble approach provides a more reliable foundation for downstream analyses, particularly in identifying potential biomarkers. Applied to esophageal squamous cell carcinoma (ESCC), BMSD-CDE reveals a set of genes F2RL3, ATP6V1C2, CGN, CAD, ANGPT2, ALDH2, CLDN7, and DTX2 as potential biomarkers. These findings are supported by extensive topological and biological analyses across normal and disease conditions using four distinct datasets.
  Keywords: potential biomarker; community detection algorithm; CDA; ensemble algorithm; topological experiment; ESCC; biological validation; community detection ensemble; CDE.
  DOI: 10.1504/IJDMB.2025.10067623
   
  
• Multi-epitopes prediction for designing a candidate vaccine against Ebola virus: a reverse vaccinology and immunoinformatics approach  
  by Swati Mohanty, Himanshu Singh 
  Abstract: Over a span of four decades, the Ebola virus disease (EVD) outbreak, has wreaked havoc starting from Central African countries through to different parts of the world including Asian countries. Guinea was the first to witness the catastrophe followed by many African and Asian countries including Liberia and Sierra Leone. In this study, the immunoinformatics approach which would include both B cell and T cell epitopes has been used for candidate vaccine development against EVD. The prediction of B cell and T cell epitopes was done by targeting the glycoprotein (GP) and VP40 proteins of Ebolavirus and an antigenic multi-epitope vaccine construct was designed. The vaccine construct was then docked with human immunogenic Toll-like Receptor 4 (TLR 4) having binding energy 13,883.1 and in silico immune simulation was done to predict the immunogenic potential of the vaccine construct with the CAI of 0.94 and the GC content 54.35 as it showed efficient expression in Escherichia coli (E. coli) K12 strain which produced vaccine in wide scale. The Ebola virus vaccine construct designed through the immunoinformatics approach in this study could be useful in combatting EVD.
  Keywords: Ebola virus; epitope-based vaccine; molecular docking; immunoinformatics; reverse vaccinology.
  DOI: 10.1504/IJDMB.2025.10068508
   
  
• Downregulation of CENPA and CCNB1 as a factor predicting the poor prognosis of acute myeloid leukaemia: a systems biological approach  
  by Mohammad Hossein Shams, Saeid Afshar, Elmira Parto Beiragh, Azin Atabakhsh, Hassan Rafieemehr 
  Abstract: Acute myeloid leukaemia (AML) is a complex hematologic malignancy. The present study takes a novel approach using bioinformatics to identify the primary molecular markers involved in AML pathogenesis. The differential expression of GEO microarray data (LogFC ≤ -1 / ≥1, adj. P-value ≤ 0.01, P-value ≤ 0.01) is analysed, and then the corresponding protein network (PPI) is drawn and examined using Cytoscape 3.6. The findings are validated externally and clinically using the GEPIA database and a survival curve. This study also identified important transcription factors (TF) affecting the expression of hub genes. The key finding is that the downregulation of CENPA and CCNB1 is associated with shorter overall survival in AML, with FOXM1 identified as a potential regulating TF. It is also suggest that disruption in various cellular features such as cell cycle, replication, and cell signalling may play roles in the pathogenesis of AML.
  Keywords: CENPA; CCNB1; systems biology; FOXM1; molecular markers; gene expression profiling.
  DOI: 10.1504/IJDMB.2025.10069104
   
  
• Machine learning approaches for disease genes prediction  
  by Priya Sadana, Isha Kansal, Vikas Khullar 
  Abstract: The identification of genes involved in human hereditary diseases frequently necessitates the examination of a large number of potential candidate genes, which can be time-consuming and expensive. Genome-wide techniques such as association studies and linkage analysis frequently select many hundreds of positional candidates. This work aims to discuss machine learning-based methods for disease susceptibility gene identification. Disease genes are already linked to diseases, while non-disease genes are a random subset of the larger population of unrelated genes. The methodology followed in this paper included a critical review to identify the literature related to title. Here, we try to identify the significant ongoing research in this domain. Earlier binary classification methods used disease-causing and healthy genes as positive and negative training sample sets. Although they could potentially include unknown disease-related genes. Unary and semi-supervised classification are more practical ways to define non-disease genes. Recent advancements, include complex methods like ensemble and deep learning. Then, we evaluated several well-known machine learning-based disease gene prediction algorithms. We concluded by discussing the pros and cons of different methods and their interpretability and reliability.
  Keywords: neurological disorder; gene prediction; binary classification; semi supervised learning; SSL.
  DOI: 10.1504/IJDMB.2025.10069769
   
  
• Skin image analysis for detecting monkeypox disease: utilising new model M-Net, a non-invasive deep learning model  
  by Vinod Kumar Yadav, Rajitha Bakthula 
  Abstract: Skin and skin-related diseases pose a significant public health challenge worldwide, leading to major concerns in medical diagnosis. Various environmental factors, including bacteria, fungi, and viruses, can contribute to these conditions, resulting in a growing number of individuals affected by skin diseases. Most physicians rely on manual biopsy tests for skin disease diagnosis, which can cause delays in timely treatment. Therefore, there is a high demand for automated skin disease classification systems to provide quick and accurate results. Deep learning (DL) has recently shown remarkable effectiveness in image-based classification tasks, such as identifying skin cancer, rosacea, melanocytic nevus, tumour cells, and COVID-19 patients. Consequently, DL can also be adapted to detect monkeypox skin disease. In this article, we propose a novel approach consisting of two phases. First, new HR, UOR, and BR algorithms will be used to preprocess the images. Second, a custom CNN model will be developed for monkeypox classification. The proposed model is compared with existing approaches in the literature and demonstrates superior performance, achieving an accuracy of 95%.
  Keywords: image pre-processing; classification; hair removal; object removal; background removal; data augmentation.
  DOI: 10.1504/IJDMB.2025.10071008