Selected Publications in WoS/PubMed/Scopus (78 + 53 = 131)

Journals (78)

  1. Alymani, M., Almoqhem, L.A., Alabdulwahab, D.A., Alghamdi, A.A., Alshahrani, H., and Raza, K. (2024). Enabling smart parking for smart cities using IoT and machine learning. PeerJ Computer Science, (In Press). DOI (IF 3.5)

  2. Singh, N., Kumar, P., Ahmad, S., Gupta, J., Raza, K. , Hashmi, A.A. (2025). Synthesis, structural elucidation, and antibacterial activities of novel copper(II), cobalt(II), and nickel(II) complexes with a bidentate Schiff base ligand against pathogenic bacteria. Journal of Molecular Structure, Elsevier, 1321 (Part 2): 139874. https://doi.org/10.1016/j.molstruc.2024.139874 (IF 4.0) )

  3. Ahmad, N., Singh, S., AlAjmi, M.F., Hussain, A., and Raza, K. (2024). CropGCNN: color space-based crop disease classification using group convolutional neural network PeerJ Computer Science, 10:e2136. https://doi.org/10.7717/peerj-cs.2136 (IF 3.5)

  4. Ahmad, S., Bano, N., Khanna, K., Gupta, D. & Raza, K. (2024). Reporting multitargeted potency of Tiaprofenic acid against lung cancer: Molecular fingerprinting, MD simulation, and MTT-based cell viability assay studies. International Journal of Biological Macromolecules, Elsevier, 276 (Part 1): 133872. https://doi.org/10.1016/j.ijbiomac.2024.133872 (IF 7.7)

  5. Faloye, K., Ahmad, S., Oyasowo, O., Shalom, E., Bano, N., Olanudun, E., Kelani, T., Aliyu, H., Raza, K., Makinde, B. & Alanzi, A. (2024). Deciphering the influenza neuraminidase inhibitory potential of naturally occurring biflavonoids: An in silico approach. Open Chemistry, Du Gruyter, 22: 20240053. https://doi.org/10.1515/chem-2024-0053 (IF 2.2)

  6. Ahmad, S., Singh, A.P., Bano, N., Raza, K., Singh, J., Medigeshi, G.R., Pandey, R., Gautam, H.K. (2024). Integrative analysis discovers Imidurea as dual multitargeted inhibitor of CD69, CD40, SHP2, lysozyme, GATA3, cCBL, and S-cysteinase from SARS-CoV-2 and M. tuberculosis. International Journal of Biological Macromolecules, Elsevier, 270 (Part 2): 132332. https://doi.org/10.1016/j.ijbiomac.2024.132332 (IF 7.7)

  7. Noori, R., Bano, N., Ahmad, S., Mirza, K., Mazumder, J.A., Perwez, M., Raza, K., Manzoor, N. & Sardar, M. (2024). Microbial Biofilm Inhibition Using Magnetic Cross-Linked Polyphenol Oxidase Aggregates. Mo ACS Applied Bio Materials, 7(5), 3164–3178 https://doi.org/10.1021/acsabm.4c00175 (IF 4.7)

  8. Ahmad, S. & Raza, K. (2024). An Extensive Review on Lung Cancer Therapeutics Using Machine Learning Techniques: State-of-the-art and Perspectives. Journal of Drug Targeting, T&F, 32(6), 635–646. https://doi.org/10.1080/1061186X.2024.2347358 (IF 4.5)

  9. Sonia, D., Anjum, Z., Raza, K., & Verma, S. (2024). A Review on Picrosides Targeting NFκB and its Proteins for Treatment of Breast Cancer. Molecular and Cellular Biochemistry, Springer. https://doi.org/10.1007/s12013-024-01281-1 (IF 4.3)

  10. Rana, M., Ahmedi, S., Mehandi, R., Ahmad, S., Fatima, T., Raza, K., & Manzoor, N. (2024). 2-hydrazinobenzothiazole based derivatives: Synthesis, characterization, antifungal, DNA binding and molecular modelling approaches. Journal of Molecular Structure, Elsevier, 1308, 138051. https://doi.org/10.1016/j.molstruc.2024.138051 (IF 4.0)

  11. Famuyiwa, S.O., Ahmad, S. Olufolabo, O.K., Olanudun, E.A., Bano, N., Oguntimehin, S.A., Adesida, S.A., Oyelekan, E.I., Raza, K. Faloye, K.O. (2023). Investigating the multitargeted anti-diabetic potential of cucurbitane-type triterpenoid from Momordica charantia: An LC-MS, docking-based MM\GBSA and MD Simulation Study. Journal of Biomolecular Structure & Dynamics, T&F. https://doi.org/10.1080/07391102.2023.2291174 (IF 5.235)

  12. Sahu, A., Ahmad, S., Imtiyaz, K., Kumaran, A.K., Islam, M., Raza, K., Easwaran, M., Kunnath, A.K., Rizvi, M.A., & Verma, S. (2023). In-silico and in-vitro study reveals ziprasidone as a potential aromatase inhibitor against breast carcinoma. Scientific Reports, Nature, 13, 16545. https://doi.org/10.1038/s41598-023-43789-1 (IF 4.6)

  13. Rana, M., Hungyo, H., Parashar, P., Ahmad, S., Mehandi, R., Tandon, V., Raza, K., Assiri, M.A., Ali, T.E., El-Bahyf, Z.M., & Rahisuddin (2023). Design, synthesis, X-ray crystal structures, anticancer, DNA binding, and molecular modelling studies of pyrazole–pyrazoline hybrid derivatives. RSC Advances, 13, 26766–26779. https://doi.org/10.1039/D3RA04873J (IF 3.9)

  14. Bhati, R., Nigam, A., Ahmad, S., Raza, K., Singh, R. (2023). Structural-functional analysis and Molecular characterization of arsenate reductase from Enterobacter cloacae RSC3 for arsenic biotransformation. 3 Biotech, Springer, 13: 305. https://doi.org/10.1007/s13205-023-03730-9 (IF 2.8)

  15. Singh, A.K., Ahmad, S., Raza, K., Gautam, H.K. (2023). Computational screening and MM\GBSA-based MD simulation studies reveal the high binding potential of FDA-approved drugs against Cutibacterium acnes Sialidase. Journal of Biomolecular Structure & Dynamics, T&F. https://doi.org/10.1080/07391102.2023.2242950 (IF 5.235)

  16. Sahu, A., Pradhan, D., Veer, B., Kumar, S., Singh, R., Raza, K., Rizvi, M.A., Jain, A.K., & Varma, S. (2023). In silico screening, synthesis, characterization and biological evaluation of novel anticancer agents as potential COX-2 inhibitors. DARU Journal of Pharmaceutical Sciences, Springer, 31, 119–133. https://doi.org/10.1007/s40199-023-00467-x (IF 4.088)

  17. Mateev, E., Georgieva, M., Mateeva, A., Zlatkov, A., Ahmad, S., Raza, K., Azevedo, V., & Barh, D. (2023). Structure-based design of novel MAO-B inhibitors: A review. Molecules, MDPI, 28(12), 4814. https://doi.org/10.3390/molecules28124814 (IF 4.6)

  18. Zhang, R., Akhtar, N., Wani, A.K., Raza, K. & Kaushik, V. (2023). Discovering deleterious Single Nucleotide Polymorphisms of human AKT1 oncogene: An in silico study. Life, MDPI, 13(7), 1532. https://doi.org/10.3390/life13071532 (IF 3.253)

  19. Satyam, R., Ahmad, S. & Raza, K. (2023). Comparative genomic assessment of members of Genus Tenacibaculum: An exploratory study. Molecular Genetics and Genomics, Springer, 298, 979–993. https://doi.org/10.1007/s00438-023-02031-3 (IF 3.1)

  20. Rana, M., Ahmedi, S., Fatima, A., Ahmad, S., Nouman, Siddiqui, N., Raza, K., Manzoor, N., Javed, S. & Rahisuddin (2023). Synthesis, Single crystal, TD-DFT, Molecular Dynamics Simulation and DNA binding studies of Carbothioamide Analog. Journal of Molecular Structure, Elsevier, 1287,135701. https://doi.org/10.1016/j.molstruc.2023.135701 (IF 4.0)

  21. Ahmad, S., Singh, V., Gautam, H., & Raza, K. (2023). Multisampling-based docking reveals Imidazolidinyl urea as a multitargeted inhibitor for Lung Cancer: An optimisation followed multi-simulation and In-vitro study. Journal of Biomolecular Structure & Dynamics, T&F, 42(5): 2494-2511. https://doi.org/10.1080/07391102.2023.2209673 (IF 4.4)

  22. Ahmad, S. & Raza, K. (2023). Identification of 5-Nitroindazole as a multitargeted inhibitor for CDK and 1 transferase kinase in Lung Cancer: A Multisampling algorithm-based structural study. Molecular Diversity, Springer. https://doi.org/10.1007/s11030-023-10648-0 (IF 3.364)

  23. Sahu, A., Raza, K., Pradhan, D., Jain, A.K., Verma, S. (2023). COX-2 as a therapeutic target against human breast cancer: A comprehensive review. WIREs Mechanisms of Disease, e1596. https://doi.org/10.1002/wsbm.1596 (IF 7.288)

  24. Yang, L., Bhat, A.M., Qazi, S., & Raza, K. (2023). DLC1 as Druggable Target for Specific Subsets of Gastric Cancer: An RNA-seq-Based Study. Medicina, 59(3): 514. https://doi.org/10.3390/medicina59030514 (IF 2.948)

  25. Shah, A.A., Ahmad, S., Yadav, M.K., Raza, K., Akhtar, S. (2024). Structure-based virtual screening, molecular docking, molecular dynamics simulation, and metabolic reactivity studies of quinazoline derivatives for their anti-EGFR activity against tumour angiogenesis. Current Medicinal Chemistry, 31(5): 595-619. https://doi.org/10.2174/0929867330666230309143711 (IF 4.740)

  26. Famuyiwa, S.O., Ahmad, S., Fakola, E.G., Olusola, A.J., Adesida, S.A., Obagunle, F.O., Raza, K., Ugwo, J.P., Oyelekan, E.I., Faloye, K.O., (2023). Comprehensive Computational Studies of Naturally Occurring Kuguacins as Antidiabetic Agents by Targeting Visfatin. Chemistry Africa, Springer, 6: 1415–1427. https://doi.org/10.1007/s42250-023-00604-8

  27. Pan, S., Gupta, T.K., & Raza, K. (2023). BatTS: a hybrid method for optimizing deep feedforward neural network. PeerJ Computer Science,, 8:e1194. http://dx.doi.org/10.7717/peerj-cs.1194 (IF 3.5)

  28. Qazi, S., Khanna, K., & Raza, K. (2023). Dihydroquercetin (DHQ) has the potential to promote apoptosis in ovarian cancer cells: An in silico and in vitro study. Journal of Molecular Structure, Elsevier, 1271: 134093. https://doi.org/10.1016/j.molstruc.2022.134093 (IF 4.0)

  29. Sahu, A., Verma, S., Pradhan, D., Raza, K., Qazi, S., Jain, A.K. (2023). Computational screening for finding new potent cox-2 inhibitors as anticancer agents. Letters in Drug Design & Discovery, 20(2): 213- 224. http://dx.doi.org/10.2174/1570180819666220128122553 (IF 1.15)

  30. Ahmad, S., Sayeed, S., Bano, N., Sheikh, K. & Raza, K. (2022). In-silico analysis reveals Quinic acid as a multitargeted inhibitor against Cervical Cancer Journal of Biomolecular Structure & Dynamics, 41(19): 9770-9786. https://doi.org/10.1080/07391102.2022.2146202. (IF 4.4)

  31. Barh, D., Tiwari, S., Rodrigues Gomes, L.G. et al. (2023). SARS-CoV-2 Variants Show a Gradual Declining Pathogenicity and Pro-Inflammatory Cytokine Stimulation, an Increasing Antigenic and Anti-Inflammatory Cytokine Induction, and Rising Structural Protein Instability: A Minimal Number Genome-Based Approach. Inflammation, Springer, 46: 297–312. https://doi.org/10.1007/s10753-022-01734-w (IF 4.657)

  32. Ahmad, S., Kaul, T., Chitkara, P. & Raza, K. (2023). Comparative insight into Rice chloroplasts genome: Mutational Phylogenomics reveals Echinochloa oryzicola as the ongoing progenitor of rice. Genetic Resources and Crop Evolution, Springer, 70: 869–885. https://doi.org/10.1007/s10722-022-01471-x (IF 1.876)

  33. Sharma, N., Kulkarni, G.T., Bhatt, A.N., Satija, S., Singh, L., Sharma, A., Dua, K., Karwasra, R., Khan, A.A., Ahmad, N. & Raza, K. (2022). Therapeutic options for the SARS-CoV-2 virus: Is there a key in herbal medicine? Natural Product Communications, 17(9): 1–10. https://doi.org/10.1177/1934578X221126303 (IF 1.496)

  34. Karwasra, R., Ahmad, S., Bano, N., Qazi, S. Raza, K., Singh, S., & Varma, S. (2022). Macrophage targeted punicalagin nanoengineering to alleviate Methotrexate Induced Neutropenia: A molecular docking, DFT and MD simulation analysis. Molecules, MDPI, 27(18): 6034. https://doi.org/10.3390/molecules27186034 (IF 4.927)

  35. Qazi, S., Jit, B.P., Das, A., Karthikeyan, M., Saxena, A., Ray, M.D., Singh, A.R., Raza, K.,Jayaram, B., Sharma, A. (2022). BESFA: bioinformatics based evolutionary, structural & functional analysis of prostrate, Placenta, Ovary, Testis, and Embryo (POTE) paralogs Heliyon, CellPress, e10476. https://doi.org/10.1016/j.heliyon.2022.e10476 (IF 3.776)

  36. Hou, J., Bhat, A.M., Ahmad, S., Raza, K. & Qazi, S. (2022). In silico Analysis of ACE2 Receptor to Find Potential Herbal Drugs in COVID-19 Associated Neurological Dysfunctions. Natural Product Communications, 17(8): 1–15. https://doi.org/10.1177/1934578X221118549 (IF 1.496)

  37. Ahmad, S., Bano, N, Qazi, S., Yadav, M. Ahmad, N., & Raza, K. (2022). Multitargeted molecular dynamic understanding of Butoxypheser against SARS-CoV-2: An in-silico study. Natural Product Communications, 17(7): 1-13. https://doi.org/10.1177/1934578X221115499 (IF 1.496)

  38. Jabeen, A., Ahmad, N. & Raza, K. (2022). Global Gene Expression and Docking Profiling of COVID-19 Infection. Frontiers in Genetics, 13: 870836. https://doi.org/10.3389/fgene.2022.870836 (IF 4.599)

  39. Singh, N.K., & Raza, K. (2022). Progress in Deep Learning-Based Dental and Maxillofacial Image Analysis: A Systematic Review. Expert Systems with Applications, Elsevier, 199: 116968. https://doi.org/10.1016/j.eswa.2022.116968 (IF 6.954)

  40. Ahmad, S., Pasha K.M., Raza, K., Eswaran, M., Yadav, M.K. (2023). Reporting Dinaciclib and Theodrenaline as a Multitargeted Inhibitor against SARS-CoV-2: An in-silico Study Journal of Biomolecular Structure & Dynamics, 41(9): 4013-4023. https://doi.org/10.1080/07391102.2022.2060308. (IF 5.235)

  41. Khuntia, B., Sharma, V., Wadhawan, M., Chhabra, V., Kidambi, B., Rathore, S., Agrawal, A., Ram, A., Qazi, S., Ahmad, S., Raza, K., Sharma, G. (2022). Antiviral potential of Indian medicinal plants against Influenza and SARS-CoV: A systematic review. Natural Product Communications, 17(3): 1–10. http://dx.doi.org/10.1177/1934578X221086988 (IF 1.496)

  42. Yadav, M.K., Ahmad, S., Raza, K., Kumar, S., Eswaran, M., Pasha KM. (2023). Predictive modeling and therapeutic repurposing of natural compounds against receptor-binding domain of SARS-CoV-2. Journal of Biomolecular Structure & Dynamics, 41(5): 1527-1539. https://doi.org/10.1080/07391102.2021.2021993. (IF 5.235)

  43. Isa, M.A., Mustapha, A., Qazi, S., Raza, K., Allamin, I.A., Ibrahim, M.M., & Mohammed, M.M. (2022). In silico Molecular Docking and Molecular Dynamic Simulation of Potential Inhibitors of 3C-Like Main Proteinase (3CLpro) from Severe Acute Respiratory Syndrome-2 (SARS-CoV-2) using Selected African Medicinal Plants. Advances in Traditional Medicine, Springer, 22, 107–123. https://doi.org/10.1007/s13596-020-00523-w    (IF 0.9)

  44. Rai, A., Qazi, S., & Raza, K. (2022). In silico analysis and comparative molecular docking study of FDA approved drugs with Transforming Growth Factor Beta receptors in Oral Submucous Fibrosis. Indian Journal of Otolaryngology and Head & Neck Surgery, Springer, 74 (Suppl 2), 2111–2121. https://doi.org/10.1007/s12070-020-02014-5    (IF 0.390)

  45. Qazi, S., Sheikh, K., Faheem, M., Khan, A., & Raza, K. (2021). A coadunation of biological and mathematical perspectives on the pandemic COVID-19: a review. Coronaviruses, 2(9), 5-20, e030821190295. https://doi.org/10.2174/2666796702666210114110013

  46. Wani, N., Barh, D. & Raza, K. (2021). Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis. Journal of Integrative Bioinformatics, 18(4): 20210029. https://doi.org/10.1515/jib-2021-0029   (IF 3.321)

  47. Qazi, S. & Raza, K. (2021). In silico approach to understand epigenetics of POTEE in ovarian cancer. Journal of Integrative Bioinformatics, 18(4): 20210028. https://doi.org/10.1515/jib-2021-0028   (IF 3.321)

  48. Satyam, R., Yousef, M., Qazi, S., Bhat, A.M., & Raza, K. (2021). COVIDium: A COVID-19 Resource Compendium. Database, Oxford University Press, 2021: baab057. https://doi.org/10.1093/database/baab057   (IF 3.451)

  49. Khuntia, B., Sharma, V., Qazi, S., Das, S., Sharma, S., Raza, K. & Sharma, G. (2021). Ayurvedic medicinal plants against COVID-19: an in silico analysis Natural Product Communications, 16(11): 1-9. https://doi.org/10.1177/1934578X211056753   (IF 1.496)

  50. Qazi, S., Das, S., Khuntia, B., Sharma, V., Sharma, S., Sharma, G., & Raza, K. (2021). In silico molecular docking and molecular dynamic simulation analysis of phytochemicals from Indian foods as potential inhibitors of SARS-CoV-2 RdRp and 3CLpro. Natural Product Communications, 16(9): 1–12. https://doi.org/10.1177/1934578X211031707   (IF 1.496)

  51. Yang, X., Alam, A., Iqbal, N., & Raza, K. (2021). Repurposing of FDA-Approved Drugs to Predict New Inhibitor Against Key Regulatory Genes in Mycobacterium Tuberculosis. Biocell, 45(6): 1569-1583. https://doi.org/10.32604/biocell.2021.017019   (IF 1.254)

  52. Khan, S., Akrema, Qazi, S., Ahmad, R., Raza, K., Rahisuddin * (2021). In Silico and Electrochemical studies for ZnO-CuO Based Immunosensor for Sensitive and Selective Detection of E. coli. ACS Omega, American Chemical Society, 6(24): 16076-16085. https://doi.org/10.1021/acsomega.1c01959   (IF 3.512)

  53. Zhang, Y., Qazi, S. & Raza, K. (2021). Differential expression analysis in Ovarian Cancer: A functional genomics and systems biology approach. Saudi Journal of Biological Sciences, Elsevier, 28(7): 4069-4081. https://doi.org/10.1016/j.sjbs.2021.04.022   (IF 4.219)

  54. Qazi, S. & Raza, K. (2021). Phytochemicals from Ayurvedic plants as potential medicaments for Ovarian cancer: An in silico analysis. Journal of Molecular Modeling, Springer, 27: 114. https://doi.org/10.1007/s00894-021-04736-x   (IF 1.810)

  55. Qazi, S., Sheikh, K., & Raza, K. (2021). In silico approach to understand the epigenetic mechanism of SARS-CoV-2 and its impact on the environment. VirusDisease, Springer, 32: 286–297. https://doi.org/10.1007/s13337-021-00655-w

  56. Qazi, S., Sharma, A., & Raza, K. (2021). The role of epigenetic changes in Ovarian Cancer: A review. Indian Journal of Gynecologic Oncology, Springer, 19, 27. https://doi.org/10.1007/s40944-021-00505-z

  57. Wani, N., Raza, K.. (2021). MKL-GRNI: A Parallel Multiple Kernel Learning approach for supervised inference of large-scale gene regulatory networks. PeerJ Computer Science, 7:e363. https://doi.org/10.7717/peerj-cs.363    (IF 3.5)

  58. Raza, K. & Singh, N.K. (2021). A Tour of Unsupervised Deep Learning for Medical Image Analysis Current Medical Imaging, Bentham Science, 17(9): 1059-1077. https://doi.org/10.2174/1573405617666210127154257    (IF 0.858)

  59. Karwasra, R., Singh, S., Raza, K., Sharma, N., & Varma, S. (2021). A brief overview on current status of nanomedicines for treatment of pancytopenia: focusing on chemotherapeutic regime. Journal of Drug Delivery Science and Technology, Elsevier, 61, 102159. https://doi.org/10.1016/j.jddst.2020.102159    (IF 3.981)

  60. Karwasra, R., Fatihi, S., Raza, K. , Singh, S., Khanna, K., Sharma, N., Sharma, S., Sharma, D., & Varma, S. (2020). Filgrastim loading in PLGA and SLN nanoparticulate system: A bioinformatics approach. Drug Development and Industrial Pharmacy, Taylor & Francis, 46(8), 1354-1361. https://doi.org/10.1080/03639045.2020.1788071    (IF 6.225)

  61. Mazumder, J., Khan, E., Perwez, M., Gupta, M., Kumar, S., & Raza, K. , Sardar, M. (2020). Exposure of biosynthesized nanoscale ZnO to Brassica juncea crop plant: morphological, biochemical and molecular aspects. Scientific Reports, Nature, 10, 8531. https://doi.org/10.1038/s41598-020-65271-y    (IF 4.6)

  62. Gupta, T.K. & Raza, K. (2020). Optimizing Deep Feedforward Neural Network Architecture: A Tabu Search Based Approach. Neural Processing Letters, Springer, 51: 2855-2870. https://doi.org/10.1007/s11063-020-10234-7    (IF 2.908)

  63. Wani, N. & Raza, K. (2019). iMTF-GRN: Integrative Matrix Tri-factorization for Inference of Gene Regulatory Networks. IEEE Access, IEEE, 7: 126154-126163 https://doi.org/10.1109/ACCESS.2019.2936794    (IF 3.367)

  64. Wani, N. & Raza, K. (2019). Integrative approaches to reconstruct regulatory networks from multi-omics data: A review of state-of-the-art methods. Computational Biology and Chemistry, Elsevier, 83: 107120 https://doi.org/10.1016/j.compbiolchem.2019.107120     (IF 2.877)

  65. Khatoon, N., Alam, H., Khan, A., Raza, K. & Sardar, M. (2019). Ampicillin Silver Nanoformulations against Multidrug resistant bacteria. Scientific Reports, Nature, 9: 6848. https://doi.org/10.1038/s41598-019-43309-0    (IF 4.6)

  66. Raza, K. (2019). Fuzzy logic based approaches for gene regulatory network inference. Artificial Intelligence in Medicine, Elsevier, 97: 189-203. https://doi.org/10.1016/j.artmed.2018.12.004    (IF 5.326)

  67. Kumar, S., Ahmad, S., Siddiqi, M.I. & Raza, K. (2019). Mathematical Model for Plant-Insect Interaction with Dynamic Response to PAD4-BIK1 Interaction and Effect of BIK1 Inhibition. BioSystems , Elsevier, 175(2019): 11-23. https://doi.org/10.1016/j.biosystems.2018.11.005   (IF 1.973)

  68. Manazir, A. & Raza, K. (2019). Recent developments in Cartesian Genetic Programming and its variants. ACM Computing Surveys , 51(6): 122. http://dx.doi.org/10.1145/3275518   (IF 10.282)

  69. Faiza, M., Tanveer, K., Fatihi, S., Wang, Y. & Raza, K. (2019). Comprehensive overview and assessment of miRNA target prediction tools in human and drosophila melanogaster. Current Bioinformatics , 14(5): 432-445. https://doi.org/10.2174/1574893614666190103101033 (IF 3.543)

  70. Raza, K. & Ahmad, S. (2019). Recent Advancement in Next-Generation Sequencing Techniques and its Computational Analysis. International Journal of Bioinformatics Research and Applications, Inderscience, 15(3): 191-220. https://dx.doi.org/10.1504/IJBRA.2019.10022508 (CiteScore 0.50)

  71. Khan, F.N., Qazi, S., Tanveer, K. & Raza, K.. (2017). A Review on the Antagonist Ebola: A Prophylactic Approach. Biomedicine & Pharmacotherapy, Elsevier, 96: 1513-1526. https://doi.org/10.1016/j.biopha.2017.11.103 | (IF 6.529) Download

  72. Raza, K. (2017). Formal Concept Analysis for Knowledge Discovery from Biological Data. International Journal of Data Mining and Bioinformatics, Inderscience, 18(4): 281-300. https://doi.org/10.1504/IJDMB.2017.10009312 | (IF 0.667) Download

  73. Raza, K., & Alam, M. (2016). Recurrent Neural Network Based Hybrid Model for Reconstructing Gene Regulatory Network. Computational Biology and Chemistry, Elsevier, 64: 322-334. http://doi.org/10.1016/j.compbiolchem.2016.08.002 | (IF 2.877) Download

  74. Raza, K. (2016). Reconstruction, Topological and Gene Ontology Enrichment Analysis of Cancerous Gene Regulatory Network Modules. Current Bioinformatics, 11(2): 243-258. http://doi.org/10.2174/1574893611666160115212806 | (IF 3.543) Download

  75. Raza, K. & Hasan, A.N. (2015). A Comprehensive Evaluation of Machine Learning Techniques for Cancer Class Prediction Based on Microarray Data. International Journal of Bioinformatics Research and Applications, Inderscience, 11(5): 397-416. http://doi.org/10.1504/IJBRA.2015.071940 | (CiteScore 0.50) Download

  76. Raza, K. & Jothiprakash, V. (2014). Multi-Output ANN Model for Prediction of Seven Meteorological Parameters in a Weather Station. Journal of The Institution of Engineers (India): Series A, Springer, 95(4): 221-229. http://doi.org/10.1007/s40030-014-0092-9  ;   (Scopus IF 1.10)

  77. Raza, K. . (2014). Clustering Analysis of Cancerous Microarray Data. Journal of Chemical and Pharmaceutical Research, 6(9): 488-493. Download

  78. Ahmad, S., Hamza, A. & Raza, K. (2013). PREs-Clustered Motifs in Drosophila melanogaster. Res. J. Pharm., Biol. Chem. Sci., 4(4): 1100-1110. Download


Book Chapters/Conference Proceedings (53)

  1. Singh, N.K., Faisal, M., Hasan, S., Goswami, G. & Raza, K. (2024). A Single-Stage Deep Learning Approach for Multiple Treatment and Diagnosis in Panoramic X-ray. In Proc. of 23rd International Conference on Intelligent Systems Design and Applications (ISDA 2023), Lecture Notes in Networks and Systems, Springer, 1046, 1–10. https://doi.org/10.1007/978-3-031-64813-7_25 (CORE-2021 Ranking 'C')

  2. Bano, N., Sajid, I., Faizi, S.A.A., Mutshembele, A., Barh, D. & Raza, K. (2024). Computational Intelligence Methods for Biomarkers Discovery in Autoimmune Diseases: Case Studies. Studies in Computational Intelligence, Springer, 1133, 303–323. https://doi.org/10.1007/978-981-99-9029-0_15

  3. Faizi, S.A.A., Singh, N.K., Kamal, A., Raza, K. (2024). Generative adversarial networks in protein and ligand structure generation: a case study. Deep Learning Applications in Translational Bioinformatics, Elsevier, 15: 231-248. https://doi.org/10.1016/B978-0-443-22299-3.00014-1

  4. Ahmad, S., Aslam, D., Ansari, A., Bhat, A.M., Raza, K. (2024). Deep learning in computer-aided drug design: a case study. Deep Learning Applications in Translational Bioinformatics, Elsevier, 15: 191-210. https://doi.org/10.1016/B978-0-443-22299-3.00012-8

  5. Yadav, M.K., Bhutani, K., Ahmad, S., Raza, K., Singh, A. & Kumar, S. (2024). Application of machine learning-based approaches in stem cell research. Computational Biology for Stem Cell Research, Elsevier, 65-84. https://doi.org/10.1016/B978-0-443-13222-3.00007-1

  6. Singh, N.K., Faisal, M., Hasan S., Goshwani, G., & Raza, K. (2023). Dental Treatment Type Detection in Panoramic X-Rays Using Deep Learning. In Proc. of 22nd International Conference Intelligent Systems Design and Applications (ISDA-2022), December 12-14, 2022. Lecture Notes in Networks and Systems, Springer, 716: 25–33 https://doi.org/10.1007/978-3-031-35501-1_3 (CORE-2021 Ranking 'C')

  7. Singh, N.K. & Raza, K. (2023). TeethU2Net: A Deep Learning-Based Approach for Tooth Saliency Detection in Dental Panoramic Radiographs. 29th International Conference on Neural Information Processing (ICONIP 2022). M. Tanveer et al. (Eds.): CCIS 1794, Springer, 1794:224–234. https://doi.org/10.1007/978-981-99-1648-1_19 (CORE-2021 Ranking 'B')

  8. Ahmad, S., Khan, F.N., Ramlal, A., Begum, S., Qazi S., & Raza, K. (2023). Nanoinformatics and Nanomodeling: Recent Developments in Computational Nanodrug Design and Delivery Systems. Emerging Nanotechnologies for Medical Applications, Elsevier, 1-36. https://doi.org/10.1016/B978-0-323-91182-5.00001-2

  9. Manazir, A. & Raza, K. (2022). pCGP: A Parallel Implementation of Cartesian Genetic Programming for Combinatorial Circuit Design and Time-Series Prediction. 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), Prague, Czech Republic, IEEE, 1-4. https://doi.org/10.1109/ICECET55527.2022.9872630

  10. Ahmad, S. et al. (2022). Illustrious Implications of Nature-Inspired Computing Methods in Therapeutics and Computer-Aided Drug Design. Nature-Inspired Intelligent Computing Techniques in Bioinformatics, Studies in Computational Intelligence, Springer, 1066: 293–308. https://doi.org/10.1007/978-981-19-6379-7_15

  11. Qazi, S., Khanam, A. & Raza, K. (2022). Potential Role of the Nature-Inspired Algorithms for Classification of High-Dimensional and Complex Gene Expression Data. Nature-Inspired Intelligent Computing Techniques in Bioinformatics, Studies in Computational Intelligence, Springer, 1066: 89–102. https://doi.org/10.1007/978-981-19-6379-7_5

  12. Qazi, W., Qazi, S., Iqbal, N. & Raza, K. (2022). The Scope and Applications of Nature-Inspired Computing in Bioinformatics. Nature-Inspired Intelligent Computing Techniques in Bioinformatics, Studies in Computational Intelligence, Springer, 1066: 3–18. https://doi.org/10.1007/978-981-19-6379-7_1

  13. Qazi, S. & Raza, K. (2022). Integrative Analysis of Ovarian Serious Adenocarcinoma to Understand Disease Network Biology. In Lecture Notes in Bioinformatics, Springer, 13347: 1-15. https://doi.org/10.1007/978-3-031-07802-6_1

  14. Gupta, T.K., & Raza, K. (2022). Optimization of Artificial Neural Network: A bat algorithm-based approach. In Proc. of 21st International Conference Intelligent Systems Design and Applications, December 13-15, 2021. Lecture Notes in Networks and Systems, Springer, 418: 286–295. https://doi.org/10.1007/978-3-030-96308-8_26 (CORE-2021 Ranking 'C')

  15. Manazir, A., & Raza, K. (2022). Comparative Evaluation of Genetic Operators in Cartesian Genetic Programming. In Proc. of 21st International Conference Intelligent Systems Design and Applications, December 13-15, 2021. Lecture Notes in Networks and Systems, Springer, 418: 765–774. https://doi.org/10.1007/978-3-030-96308-8_71 (CORE-2021 Ranking 'C')

  16. Qazi, S., Iqbal, N. & Raza, K. (2022). Fuzzy Logic-Based Hybrid Models for Clinical Decision Support Systems in Cancer. Computational Intelligence in Oncology, Studies in Computational Intelligence (SCI), Springer , 1016: 1-13. https://doi.org/10.1007/978-981-16-9221-5_12

  17. Sahu, A., Qazi, S., Raza, K., Singh, A., Verma, S. (2022). Machine Learning-Based Approach for Early Diagnosis of Breast Cancer Using Biomarkers and Gene Expression Profiles. Computational Intelligence in Oncology, Studies in Computational Intelligence (SCI), Springer, 1016: 285–306. https://doi.org/10.1007/978-981-16-9221-5_17

  18. Khan, F.N., Yousef, M. & Raza, K. (2022). Machine Learning-Based Models in the Diagnosis, Prognosis and Effective Cancer Therapeutics: Current State-of-the-Art. Computational Intelligence in Oncology, Studies in Computational Intelligence (SCI), Springer , 1016: 17-52. https://doi.org/10.1007/978-981-16-9221-5_2

  19. Raza, K., Qazi, S., Sahu, A., & Varma, S. (2022). Computational Intelligence in Oncology: Past, Present, and Future. Computational Intelligence in Oncology, Studies in Computational Intelligence (SCI), Springer , 1016: 1-16. https://doi.org/10.1007/978-981-16-9221-5_1

  20. Qazi, S., Khanam, A. & Raza, K. (2021). Ebola Virus: Overview, Genome Analysis and Its Antagonists. Human Viruses: Diseases, Treatments and Vaccines, Springer, 123-142. https://doi.org/10.1007/978-3-030-71165-8_6

  21. Alam, M.T. & Raza, K. (2021). Blockchain technology in healthcare: making digital healthcare reliable, more accurate, and revolutionary. Translational Bioinformatics in Healthcare and Medicine, Elsevier, 81-96. https://doi.org/10.1016/B978-0-323-89824-9.00007-0

  22. Alam, A., Rashid, I., & Raza, K. (2021). Application, functionality, and security issues of data mining techniques in healthcare informatics. Translational Bioinformatics in Healthcare and Medicine, Elsevier, 149-156. https://doi.org/10.1016/B978-0-323-89824-9.00012-4

  23. Ahmad, S., Qazi, S. & Raza, K. (2021). Translational bioinformatics methods for drug discovery and drug repurposing. Translational Bioinformatics in Healthcare and Medicine, Elsevier, 127-139. https://doi.org/10.1016/B978-0-323-89824-9.00010-0

  24. Qazi, S. & Raza, K. (2021). Translational bioinformatics in healthcare: past, present, and future. Translational Bioinformatics in Healthcare and Medicine, Elsevier, 1-12. https://doi.org/10.1016/B978-0-323-89824-9.00001-X

  25. Qazi, S. & Raza, K. (2021). Fuzzy logic-based hybrid knowledge systems for the detection and diagnosis of childhood autism. Handbook of Decision Support Systems for Neurological Disorders, Elsevier, 55-69. https://doi.org/10.1016/B978-0-12-822271-3.00016-5

  26. Sheikh, K. & Raza, K. (2021). Viroinformatics and Viral Diseases: A New Era of Interdisciplinary Science for a Thorough Apprehension of Virology. Translational Bioinformatics Applications in Healthcare, CRC Press, 109-132. https://doi.org/10.1201/9781003146988-8

  27. Khan, F.N., Ahmad, S., Raza, K. (2021). Clinical Applications of Next-Generation Sequence Analysis in Acute Myelogenous Leukemia. Translational Bioinformatics Applications in Healthcare, CRC Press, 41-66. https://doi.org/10.1201/9781003146988-4

  28. Qazi, S., Iqbal, N., Raza, K. (2021). Artificial Intelligence in Medicine (AIM): Machine Learning in Cancer Diagnosis, Prognosis and Therapy. Artificial Intelligence for Data-Driven Medical Diagnosis, De Gruyter, 103-126. https://doi.org/10.1515/9783110668322-005

  29. Singh, N.K. & Raza, K. (2021). Medical Image Generation Using Generative Adversarial Networks: A Review. , Health Informatics: A Computational Perspective in Healthcare, Studies in Computational Intelligence, Springer 932: 77-96. https://doi.org/10.1007/978-981-15-9735-0_5

  30. Alam, A., Qazi, S., Iqbal, N., Raza, K. (2020). Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use. In Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications, IEEE-Wiley, 1-26. https://doi.org/10.1002/9781119670087.ch1

  31. Sahu, A., Qazi, S., Raza, K., Varma, S. (2020). COVID-19: Hard Road to Find Integrated Computational Drug and Repurposing Pipeline. Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence (SCI), Springer , 923: 295-309. https://doi.org/10.1007/978-981-15-8534-0_15

  32. Qazi, S., Ahmad, S. Raza, K.. (2020). Using Computational Intelligence for Tracking COVID-19 Outbreak in Online Social Networks. Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence (SCI), Springer , 923: 47-59. https://doi.org/10.1007/978-981-15-8534-0_3

  33. Raza, K., Maryam, Qazi, S. (2020). An Introduction to Computational Intelligence for COVID-19: Surveillance, Prevention, Prediction, and Diagnosis. Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence (SCI), Springer , 923: 3-18. https://doi.org/10.1007/978-981-15-8534-0_1

  34. Raza, K. (2020). Artificial intelligence against COVID-19: A meta-analysis of current research. Big Data Analytics Intelligence Against COVID-19: Innovation Vision and Approach, Studies in Big Data, Springer, 78: 165-176. https://doi.org/10.1007/978-3-030-55258-9_10

  35. Sahu, A., Pradhan, D., Raza, K., Qazi, S., Jain, A.K., & Verma, S. (2020). In silico library design, screening and MD simulation of COX-2 inhibitors for anticancer activity. In Proc. of 12th International Conference on Bioinformatics and Computational Biology (BICOB-2020), San Francisco, USA, March 23-25, 2020. EPiC Series in Computing, 70: 21-32. https://doi.org/10.29007/z2wx

  36. Qazi, S. & Raza, K.. (2020). Towards a VIREAL platform: Virtual reality in cognitive and behavioural training for Autistic individuals. Advanced Computational Intelligence Techniques for Virtual Reality in Healthcare, Studies in Computational Intelligence SCI, Springer, 875: 25-47. https://doi.org/10.1007/978-3-030-35252-3_2

  37. Ahmad, N., Jabeen, N. & Raza, K. (2020). Machine Learning Based Outlook for the Analysis of SNP-SNP Interaction for Biomedical Big Data. Lecture Notes in Electrical Engineering, Springer, 601: 13-22. https://doi.org/10.1007/978-981-15-1420-3_2

  38. Qazi, S. & Raza, K. (2020). Smart Biosensors for an efficient Point of Care (PoC) Health Management. Smart Biosensors in Medical Care, Elsevier, 65-85. https://doi.org/10.1016/B978-0-12-820781-9.00004-8

  39. Jabeen, A., Ahmad, N. & Raza, K.. (2019). Differential Expression Analysis of ZIKV Infected Human RNA Sequence Reveals Potential Genetic Biomarkers. Lecture Notes in Bioinformatics, Springer, 11465: 1-12. https://doi.org/10.1007/978-3-030-17938-0_26

  40. Raza, K. & Qazi, S. (2019). Nanopore Sequencing Technology and Internet of Living Things: A Big Hope for U-Healthcare. Sensors for Health Monitoring, Elsevier, 5: 95-116. https://doi.org/10.1016/B978-0-12-819361-7.00005-1

  41. Farooqi, M.R., Iqbal, N., Singh, N.K., Affan, M. & Raza, K. (2019). Wireless Sensor Networks towards convenient infrastructure in Health care industry: A systematic study. Sensors for Health Monitoring, Elsevier, 5: 31-46 https://doi.org/10.1016/B978-0-12-819361-7.00002-6

  42. Qazi, S., Tanveer, K., El-bahnasy, K. & Raza, K. (2019). From Telediagnosis to Teletreatment: The Role of Computational Biology and Bioinformatics in Tele-based Healthcare. Telemedicine Technologies, Elsevier, 153-169. https://doi.org/10.1016/B978-0-12-816948-3.00010-6

  43. Wani, N. & Raza, K. (2019). Raw Sequence to Target Gene Prediction: An Integrated Inference Pipeline for ChIP-seq and RNA-seq Datasets. In: Malik H., Srivastava S., Sood Y., Ahmad A. (eds) Applications of Artificial Intelligence Techniques in Engineering. Advances in Intelligent Systems and Computing, Springer, 697: 557-568. https://doi.org/10.1007/978-981-13-1822-1_52    bioRxiv 220152

  44. Gupta, T.K. & Raza, K. (2019). Optimization of ANN Architecture: A Review on Nature-Inspired Techniques. In Machine Learning in Bio-Signal Analysis and Diagnostic Imaging, Elsevier, 159-182. https://doi.org/10.1016/B978-0-12-816086-2.00007-2   

  45. Raza, K.. (2019). Improving the Prediction Accuracy of Heart Disease with Ensemble Learning and Majority Voting Rule. In In Advances in Ubiquitous Sensing Applications for Healthcare, U-Healthcare Monitoring Systems: Design and Applications, Academic Press, Elsevier, 179-196. https://doi.org/10.1016/B978-0-12-815370-3.00008-6

  46. Jabeen, A., Ahmad, N. & Raza, K. (2018). Machine Learning-based State-of-the-art Methods for the Classification of RNA-Seq Data. In: Dey N., Ashour A., Borra S. (eds) Classification in BioApps. Lecture Notes in Computational Vision and Biomechanics, Springer, 26: 133-172. https://doi.org/10.1007/978-3-319-65981-7_6 | Download

  47. Wani, N. & Raza, K. (2018). Multiple Kernel Learning Approach for Medical Image Analysis. In: Dey N, Ashour A, Shi F, Balas E (eds), Soft Computing Based Medical Image Analysis, Elsevier, 31-47. https://doi.org/10.1016/B978-0-12-813087-2.00002-6 | Download

  48. Raza, K. (2017). Protein Features Identification for Machine Learning-based Prediction of Protein-Protein Interactions. In Proc. of Communications in Computer and Information Science, Springer, 750: 305-317. https://dx.doi.org/10.1007/978-981-10-6544-6_28 | Download

  49. Raza, K.. (2016). Analysis of Microarray Data Using Artificial Intelligence Based Techniques. Handbook of Research on Computational Intelligence Applications in Bioinformatics, IGI Global, USA, 216-239. http://doi.org/10.4018/978-1-5225-0427-6.ch011 | Download

  50. Raza, K.(2015). M5 Model Tree and Gene Expression Programming for the Prediction of Metrological Parameters. In Proc. of 2015 International Conference on Computers, Communications, and Systems (ICCCS-2015), 47-51, IEEE. http://dx.doi.org/10.1109/CCOMS.2015.7562850 | Download

  51. Raza, K. & Kohli, M. (2015). Ant Colony Optimization for Inferring Key Gene Interactions. In Proc. of 9th INDIACom-2015, 2nd International Conference on Computing for Sustainable Global Development, 1242-1246, IEEE. [arXiv] Download

  52. Raza, K. & Parveen, R. (2013). Soft Computing Approach for Modeling Genetic Regulatory Networks. Advances in Intelligent Systems and Computing, Springer, 178: 1-11. http://doi.org/10.1007/978-3-642-31600-5_1 Download

  53. Raza, K. & Parveen, R. (2013). Reconstruction of Gene Regulatory Network of Colon Cancer Using Information Theoretic Approach". In Proc. of 4th International Conference (CONFLUENCE-2013): The Next Generation Information Technology Summit 2013, p. 461-466. http://doi.org/10.1049/cp.2013.2357 | [arXiv]


List of Pre-Prints (0)
Sidebar
 
Footer