Articles published in Journal of Computer Science & Systems Biology have been cited by esteemed scholars and scientists all around the world.
Journal of Computer Science & Systems Biology has got h-index 23, which means every article in Journal of Computer Science & Systems Biology has got 23 average citations.
Following are the list of articles that have cited the articles published in Journal of Computer Science & Systems Biology.
| 2022 | 2021 | 2020 | 2019 | 2018 | |
|---|---|---|---|---|---|
Year wise published articles |
61 | 60 | 41 | 17 | 29 |
Year wise citations received |
175 | 232 | 220 | 225 | 205 |
| Journal total citations count | 2279 |
| Journal impact factor | 1.73 |
| Journal 5 years impact factor | 5.97 |
| Journal cite score | 5.75 |
| Journal h-index | 23 |
| Journal h-index since 2018 | 17 |
Mookiah MRK, Acharya UR, Fujita H, Koh JE, Tan JH, et al. (2015) Local configuration pattern features for age-related macular degeneration characterization and classification. Comp Biol Med. 63: 208-218. |
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Hermannsson Þ (2017) Bone Model to Optimize Implant Selection in Total Hip Arthroplasty. Doctoral dissertation, Reykjavík University. |
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Ghosh R (2016) Assessment of failure of cemented polyethylene acetabular component due to bone remodeling: A finite element study. J Ortho. 13: 140-147. |
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Kar S (2016) An Overview of Recent Advances in Application of Some Inorganic Materials-Biological and Technological Perspectives. J Biotechnol Biomater. 6: 244. |
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Nitoi D, Milicescu S, Apostolescu Z, Dimitrescu A, Chivu O, et al. (2015) FEM of an Implant Behaviour in a Healthy Bone. Procedia Eng. 100: 1092-1098. |
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Crook PD, Owen JR, Hess SR, Al-Humadi SM, Wayne JS, et al. (2017) Initial stability of cemented versus cementless tibial components under cyclic load. The Journal of Arthroplasty. |
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Bao MH, Feng X, Zhang YW, Lou XY, Cheng Y, et al. (2013) Let-7 in cardiovascular diseases, heart development and cardiovascular differentiation from stem cells. International journal of molecular sciences 14: 23086-23102. |
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Abbas VR (2015) Presented a method based on an evolutionary algorithm to achieve efficient artificial neural network model for prediction of breast tumors. 25: 100-115 |
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Daaraaee M, Vahidi J, Alipour A (2015) Presented a method based on an evolutionary algorithm to achieve efficient artificial neural network model for prediction of breast tumors. J Mazandaran Univer Med Sci. 25: 130. |
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Arun Kumar R, Sathish Kumar D, Nishanth T (2011) Assessment & Effectiveness of Surgeries in Human Safety. J Clinic Experiment Ophthalmol 2: 2. |
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Daaraaee M, Vahidi J, Alipour A (2015) A Method Based on an Evolutionary Algorithm to Achieve an Efficient Artificial Neural Network Model for Prediction of Breast Tumors Status. Journal of Mazandaran University of Medical Sciences 25: 100-115. |
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Khalid A, Noureldien NA (2014) Determining the Efficient Structure of Feed-Forward Neural Network to Classify Breast Cancer Dataset. Editorial Preface 5: 12. |
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Singh G, Sharma D, Singh V, Rani J, Marotta F, et al. (2017) In silico functional elucidation of uncharacterized proteins of Chlamydia abortus strain LLG. Future Science OA 3: FSO169. |
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Mehla K, Ramana J (2015) Novel Drug Targets for Food-Borne Pathogen Campylobacter jejuni: An Integrated Subtractive Genomics and Comparative Metabolic Pathway Study. Omics: a journal of integrative biology 19: 393-406. |
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Rana A, Thakur S, Bhardwaj N, Kumar D, Akhter Y (2016) Excavating the surface-associated and secretory proteome of Mycobacterium leprae for identifying vaccines and diagnostic markers relevant immunodominant epitopes. Pathogens and Disease 74: ftw110. |
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Naqvi SAM (2013) Production, Structural Characterization And Docking Studies Of Antibacterial Compound From Aeribacillus Pallidus Sat4 Against Prioritized Bacterial Targets (Doctoral dissertation, Quaid-i-Azam University, Islamabad. |
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Nishant T, Kumar A, Sathish Kumar D, Vijaya Shanti B (2011) Biological Databases-Integration of Life Science Data J Comput Sci Syst Biol 4: 087-092. |
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Amajala CK, Reddy IB, Zaveri K (2015) Insilico Proteome Screening to Identify Prospective Drug Targets in Bacillus anthracis. International Journal for Computational Biology 4: 21-30. |
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Dora P, Priya VS, SH P, Muddapur UM (2015) Important databases and tools to identify promising drug targets by subtractive genomics approach–A |
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Habib AM, Islam M, Sohel M, Mazumder M, Hasan H, et al. (2016) Mining the Proteome of Fusobacterium nucleatum subsp. nucleatum ATCC 25586 for Potential Therapeutics Discovery: An In Silico Approach. Genomics & Informatics 14: 255-264. |
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Journal of Computer Science & Systems Biology received 2279 citations as per Google Scholar report
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