Forthcoming and Online First Articles

International Journal of Bioinformatics Research and Applications

International Journal of Bioinformatics Research and Applications (IJBRA)

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International Journal of Bioinformatics Research and Applications (6 papers in press)

Regular Issues

  • Development of Predictive Model of Diabetic Using Supervised Machine Learning Classification Algorithm of Ensemble Voting   Order a copy of this article
    by Debabrata Datta, Madhubrata Bhattacharya, Suman Rajest George, Shynu T, R. Regin, S.Silvia Priscila 
    Abstract: Predicting the health status of patients suffering from diabetic is an important task in the health sector because the medical history of diabetic evidenced that it is a slow killer. If data collection is enough, suitable, and noise-free, such difficulties can be predicted accurately. AI-based machine learning algorithms can predict diabetes. Overfitting and underfitting impair the accuracy of classification machine learning models. Individual machine-learning models are weak learners. Hence, the demand is to develop a strong model (overall model) by combining all weak learner models to improve accuracy. Voting creates a robust, accurate model. Voting is soft and hard. Ensemble machines learning models like RF, AdaBoost, and Gboost are integrated with LR, DT and KNN models. Our ensemble voting classifier model combines RF, AdaBoost, Gboost, LR, DT, and KNN. This voting model predicts diabetes with 97+% accuracy. LR, DT, and KNN models estimate precision, recall, and F1. We tested our proposed models on two sets of input datasets with numerical and categorical features and found that categorical features improve prediction accuracy.
    Keywords: diabetic; ensemble voting; classification; K-nearest neighbour; KNN; adaptive boosting; AdaBoost; random forest; RF; logistic regression.
    DOI: 10.1504/IJBRA.2023.10057044
  • A Framework for Dysgraphia Detection in children using Convolutional Neural Network   Order a copy of this article
    by Richa Gupta, Gunjan Goyal, Rakesh Garg, Sidhanth Karwal, Abhishek Goyal, Neetu Singla 
    Abstract: Dysgraphia, a writing disorder in which any human may have difficulty in his writing at any level such as unrecognised letters/numbers or slow writing. This handwriting disorder is mainly observed among 10%40% of school children. In present scenario, dysgraphia is diagnosed by the medical practitioners by analysing the persons written document and staffs impressions. Such diagnosis mechanism is very time consuming that may result in the undiagnosed dysgraphia when a child is having mild symptoms. Many researches have been conducted for the early diagnosis of the dysgraphia using various machine learning algorithms such as decision tree, random forest and support vector machine, etc. In this work, a novel framework using the concept of convolutional neural network is proposed for the accurate detection of dysgraphia. Further, the proposed model is tested on a self-created dataset including hundreds of handwriting images and performs well in terms of accuracy, recall, precision and F1-score.
    Keywords: deep learning; dysgraphia; dysgraphic aid; convolutional neural network; CNN; classification.
    DOI: 10.1504/IJBRA.2023.10057804
  • Bioinformatics analysis of Geo datasets for identifying pathways and hub genes in Psoriasis   Order a copy of this article
    by Masufa Tarannum, Ratna Priya, Shweta Pandey, Buddhi Prakash Jain 
    Abstract: Psoriasis is one of the inflammatory skin diseases where skin cells divide at a faster rate. The present study aims to perform mRNA analysis of geo datasets GSE166388. The differentially expressed genes (DEGs) were selected and gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses by DAVID were performed. mRNA functional enrichment was also done using FunRich. Protein-protein interaction (PPI) was done using STRING and hub genes were found out by Cytoscape. A total 19 up and four downregulated genes were found in the geo datasets. These genes participate in two KEGG pathways; glutathione metabolism (RRM2, CHAC2), and Pyrimidine metabolism (RRM2, CMPK2). PPI analysis and hub genes identifications by STRING and Cytoscape identified 11 hub genes in two modules. The study suggests that these pathways and involved genes could be promising therapeutic targets in Psoriasis treatment.
    Keywords: psoriasis; functional enrichment; gene ontology; geo datasets; protein-protein interaction; PPI; hub genes; differentially expressed genes; DEGs.
    DOI: 10.1504/IJBRA.2023.10058215
  • Enhancing Multiclass Classification of Knee Osteoarthritis Severity Grades Using oneDNN   Order a copy of this article
    by Akshay Bhuvaneswari Ramakrishnan, Shriram K. Vasudevan, Murugesh TS, Sini Raj Pulari 
    Abstract: Osteoarthritis of the knee (OA) is a degenerative joint condition affecting around 23% of adult patients in the USA and globally. To diagnose OA early and assess severity grades, knee images were classified into five severity categories using the Knee Osteoarthritis Dataset with Severity Grading dataset from Kaggle. The highest severity grade was grade 4. Pre-processing processes, including data reduction and augmentation, were required to correct the uneven distribution of class information. As the dataset had an uneven distribution of class information, pre-processing processes including data reduction and augmentation were required to correct the problem In addition, the classification process was carried out with the assistance of three widely used convolutional neural network models. We proposed a new architecture and have used three of the following models for classification EfficientNetB5, DenseNet201, and Inception V3. Additionally, all these models are optimised with oneDNN library using oneAPI.
    Keywords: osteoarthritis; convolutional neural network; CNN; oneDNN; oneAPI; medical AI.
    DOI: 10.1504/IJBRA.2023.10058695
  • Identification of potent bioactive molecules to prevent white spot syndrome virus infection in shrimps (Penaeus monodon) through computational analysis   Order a copy of this article
    by Mayukh Mitra, Ajoy Mallik 
    Abstract: White spot syndrome virus (WSSV), the main causative agent of white spot disease (WSD), is prevalent in aquatic crustaceans but exhibits the most virulent effects in penaeid shrimps. A highly ubiquitous interaction takes place between the viral envelope protein VP28 of WSSV and Rab7 of Penaeus monodon which helps WSSV to escape from the late endosomal vesicle before it becomes too acidic for it to survive. Through in-sillico molecular docking analysis we identified that some residues of the effector loop of Rab7 play a major role in its interaction with VP28. Three bioactive molecules were identified by computational analysis in AutoDock Vina which target the effector loop or residues interacting with it and thus might hinder the escape route of WSSV leading to reduction of the virulence of the virus.
    Keywords: white spot syndrome virus; WSSV; black tiger shrimp; Penaeus monodon; VP28; envelope protein; Rab7; AutoDock; interaction; effector loop.
    DOI: 10.1504/IJBRA.2023.10059166
  • Detection and Classification of COVID-19 using Supervised Deep Learning on MRI Images   Order a copy of this article
    by Chinna Babu Jyothi, Mudassir Khan, Mallikharjuna Rao Nuka, Ch. Nagaraju 
    Abstract: Healthcare services in many parts of the world, but especially in emerging countries, have been made aware of the risks presented by the COVID-19 pandemic. In areas where bulk traditional testing is not practical, new computer-assisted diagnosis methods are clearly needed to provide speedy and cost-effective screening. Pulmonary ultrasonography can be used to diagnose lung disease since it is portable; easy to clean; inexpensive; and non-invasive. In recent years, computer-assisted analysis of lung ultrasound images has showed considerable promise for identifying respiratory disorders, including COVID-19 screening and diagnosis. Detecting COVID-19 infections from lung ultrasound images using deep-learning algorithms and comparing their results. It was possible to use a variety of pre-trained deep learning architectures to this problem. There are 3,326 lung ultrasound images in the POCUS dataset, which we used to train and fine-tune our algorithm. Computed tomography (CT) proved useful in the diagnosis of corona virus infection particularly in the pandemic of new corona virus (COVID-19). Radiation from patients who underwent CT scans experienced alterations that were comparable to those seen in MRI scans. A chest MRI should be performed if a CT scan is unavailable, according to the study's findings.
    Keywords: COVID-19; deep learning; DL; supervised learning; machine learning.
    DOI: 10.1504/IJBRA.2023.10059270