Journal of Computer Science & Systems Biology

ISSN: 0974-7230

Open Access

Citations Report

Journal of Computer Science & Systems Biology : Citations & Metrics Report

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
Important citations

Gundaram, Maithri. "Research & Reviews: Journal of Pharmaceutics and Nanotechnology."

Sapaty, Peter Simon. "Mosaic warfare: from philosophy to model to solutions." ??????????? ?????? ? ??????? (2019).

Sapaty, Peter Simon. Holistic analysis and management of distributed social systems. Springer, 2019.

Sapaty, Peter Simon. Managing Distributed Dynamic Systems with Spatial Grasp Technology. Springer International Publishing, 2017.

Gundaram, Maithri. "Research & Reviews: Journal of Pharmaceutics and Nanotechnology."

ALEMAYEHU, TEFERI GETACHEW, and YOHANNES GEDAMU WONDIFRAW. "MS-FUZZY IDEALS OF MS-ALGEBRAS." Journal of applied mathematics & informatics 39, no. 3_4 (2021): 553-567.

Badawy, Abd El-Mohsen, and Ahmed Gaber. "Complete decomposable MS-algebras." Journal of the Egyptian Mathematical Society 27, no. 1 (2019): 1-11.

Badawy, Abd El-Mohsen, Essam El Seidy, and Ahmed Gaber. "MS-ideals of MS-Algebras." Applied Mathematical Sciences 13, no. 7 (2019): 347-357.

Alaba, Berhanu Assaye, Mihret Alamneh Taye, and Teferi Getachew Alemayehu. "?-fuzzy ideals in MS-algebras." rn 55 (2018): 7.

Yu, Wuhan, Weihua Yu, Yan Yang, and Yang Lü. "Exploring the Key Genes and Identification of Potential Diagnosis Biomarkers in Alzheimer’s Disease Using Bioinformatics Analysis." Frontiers in Aging Neuroscience 13 (2021): 276.

Bantihun, Getachew, and Mulugeta Kebede. "In silico analysis of promoter region and regulatory elements of mitogenome co-expressed trn gene clusters encoding for bio-pesticide in entomopathogenic fungus, Metarhizium anisopliae: strain ME1." Journal of Genetic Engineering and Biotechnology 19, no. 1 (2021): 1-11.

Mahapatra, Rajani Kanta, and Mahin Das. "A computational approach to validate novel drug targets of gentianine from Swertiya chirayita in Plasmodium falciparum." Biosystems 196 (2020): 104175.

Zhang, Bao Long, Xiu Hong Yang, Hui Min Jin, and Xiao Li Zhan. "Identification of differentially expressed genes in diabetic kidney disease by RNA?Seq analysis of venous blood platelets." FEBS Open bio 11, no. 8 (2021): 2095.

Lakshmanan, Vinoth-Kumar, Shreesh Ojha, and Young Do Jung. "A modern era of personalized medicine in the diagnosis, prognosis, and treatment of prostate cancer." Computers in biology and medicine (2020): 104020.

Tomi?, D., D. Davidovi?, V. Jangel, J. Mesari?, K. Skala, and T. Lipi?. "Assessing the effectiveness of Autodock Vina in a large and unstructured environments for virtual drug screening."

Virtual drug screening is one of the most widely used approaches for finding new drugs candidates. The process consists in selecting one or more chemical compounds with the highest binding free energy to target proteins. Given that the empirical space of chemical compounds is extremely large and estimated to has over 50 millions of them, finding the most effective drug is computationally challenging. Furthermore, the vast majority of proteins still lack the experimentally obtained 3D structures, making it hard to accurately calculate their binding free energies with chemical compounds. With this in mind, the aim of our study was to investigate the accuracy of the Autodock Vina program in a virtual drug screening on a set of proteins that do not have experimentally determined structures. To do this, we performed a virtual drug screening with the Autodock Vina on a large set of drug-kinase pairs taken from the IDG-Dream Drug-Kinase Binding Prediction Challenge. The results obtained show that the Autodock Vina can be used effectively in such unstructured environments.

Tomi?, D., B. Pirki?, K. Skala, and L. Kranj?evi?. "Predicting the effectiveness of multi-drug cancer therapies." In 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 375-380. IEEE, 2019.

Susca, Vito AR, Pierpaolo Vivo, and Reimer Kühn. "Second largest eigenpair statistics for sparse graphs." Journal of Physics A: Mathematical and Theoretical 54, no. 1 (2020): 015004.

Tomi?, Draško, Davor Davidovi?, Attila Marcel Szasz, Melinda Rezeli, Boris Pirki?, Jozsef Petrik, Vesna Ba?i? Vrca et al. "The screening and evaluation of potential clinically significant HIV drug combinations against the SARS-CoV-2 virus." Informatics in Medicine Unlocked 23 (2021): 100529.

Purkayastha, Bornali, and Anil Kumar. "Issues and Challenges in Management related to Information Technology." (2019).

Google Scholar citation report
Citations: 2279

Journal of Computer Science & Systems Biology received 2279 citations as per Google Scholar report

Journal of Computer Science & Systems Biology peer review process verified at publons

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