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 (5 papers in press)

Regular Issues

  • Classification of Cervical Cancer using Machine Learning Techniques: A Review   Order a copy of this article
    by Sanat Jain, Ashish Jain, Mahesh Jangid 
    Abstract: One of the most prevalent and deadliest malignancies in women is cervical malignancy. It is the fourth major cause of death among women worldwide. Life of women can be saved if this malignancy is identified at a precancerous stage. Because it is fully curable at an early stage. The most widely used screening procedure for cervical malignancy is the pap test. As a result, computerized identification, and classification of cervical malignancy from pap images have become essential because it provides a precise, reliable, and fast investigation of the disease's progress. This paper provides a comprehensive overview of the automatic identification of cervical malignancy. It introduces cervical malignancy with its causes, risk factors, and symptoms which are very essential to know for treatment at an early stage. This paper also discusses the following: pap smear test, which is widely used in the screening, computer-aided diagnosis requirement for classification of cervical cells, classes of cervical cells for segmentation as well as classification, and existing techniques for classifying cervical cells. Finally, discuss the previous research's key points and limitations of various methodologies which are used for classification purposes.
    Keywords: Cervical cancer; pap test; segmentation; classification.

  • An illustrative in silico study of Copper oxide nanoparticles (CuNPs) and its interaction with fish liver proteins   Order a copy of this article
    by Hrithik Baradia, KOEL MUKHERJEE, Biplab Sarkar 
    Abstract: Nanotechnology has brought a revolution in the various industries including agriculture, medicine, electronics and textiles. The nanoparticles (NPs) have exceptional physio-chemical properties which varied from their bulk state properties which is the reason for their wide and rapidly increasing applications. The different materials used in NPs have proven to be hazardous in bulk state and some studies have shown that these NPs possess genotoxic and cytotoxic properties. Fishes are integral part of food chain and used as models for various studies. In this article, an in silico study was done to study the effects of CuNPs on various liver proteins of Rohu and Zebra fish. After analyzing the results, it is concluded that carboxylesterase and ligase formed most stable complex with Cu NP while transferases from liver of both the fishes form weak complex with one stable hydrogen bond each.
    Keywords: Nanotechnology; Copper Nanoparticle; Liver proteins; nanotoxicity; rohu; zebrafish.
    DOI: 10.1504/IJBRA.2022.10053484
  • Optimized Deep Neural Network for Cancer Disease Prediction Using Genetic Algorithm   Order a copy of this article
    by Wasiur Rhmann 
    Abstract: Detection of cancer at an early stage is a crucial activity for the Oncologist for proper treatment of the disease. Various machine learning techniques are applied to detect the different types of cancers. However, till date the low accuracy in cancer detection is observed because the limited focus has been given to addressing the datasets imbalance problem for cancer detection. In the present work, a novel Genetic algorithm-based deep neural network (GA-DNN) is proposed to effectively detect the two types of cancers i.e. prostate cancer and breast cancer. Results obtained by GA-DNN are compared with SVM, Random forest, and DNN. Best Results are reported by GA-DNN for prostate cancer and breast cancer Coimbra datasets. It was observed that the Optimized Deep neural network gave the best results when the dataset is large.
    Keywords: Genetic algorithm; Deep learning; Hyper-parameter; Disease datasets,.
    DOI: 10.1504/IJBRA.2022.10053545
  • A Survey of Computer Vision based Liver Cancer Detection   Order a copy of this article
    by Mohammad Anwarul Siddique, Shailendra Kumar Singh 
    Abstract: The liver cancer is sixth most cause for mortality due to cancers. Survival rate of liver cancer is extremely low because of complexities in early diagnosis, hasty progression, and shortage of targeted drugs. Liver cancer detection is challenging because of diversity in morphology, risk factors, micro-environmental discrepancies, and genetic susceptibilities. This paper presents, survey of computer aided liver cancer detection using distinct machine and deep learning techniques. It focuses on the methodology, dataset, evaluation metrics, challenges and constraints of the various machine and deep learning-based liver cancer detection approaches. It presents the future direction of the liver cancer detection for the further enhancement.
    Keywords: Liver Cancer Detection; Liver segmentation; Hepatocellular Carcinoma; Machine Learning; Deep Learning.
    DOI: 10.1504/IJBRA.2022.10053584
  • Computational analysis of plant pathogen Xanthomonas axonopodis pv. punicae genome to manage its bacterial resistance in pomegranate   Order a copy of this article
    by P.V. PARVATI S.A.I. ARUN  
    Abstract: In this work, the alternate drugs and their targets in the proteome of the plant pathogen Xanthomonas axonopodis pv. punicae (Xap) that causes blight disease in pomegranate were identified. The use of traditional antimicrobial agents to manage this disease was not optimal in action and led to antimicrobial resistance due to their continuous usage. To address this issue of antimicrobial resistance, computational techniques were used for the identification of alternate drugs and their targets in this pathogen. The identified drugs can be used as alternate drugs for the existing drugs, for which there is no bacterial resistance yet in the pathogen. For the identification of alternate drugs and their targets, unique proteins of the pathogen were initially identified by using the subtractive genomics method, followed by the prediction of essential virulent proteins. The final data set contains 97 of the essential and virulent proteins of Xanthomonas axonopodis pv. punicae, which are non-homologous to pomegranate and also to humans, but have homologs in other bacterial species.
    Keywords: Pomegranate; Xanthomonas axonopodispv. punicae; blight; alternate drugs; computational genomics; bacterial resistance.
    DOI: 10.1504/IJBRA.2022.10053815