Sara Kiani

28 papers receiving 812 citations

Sara Kiani's Hit Papers

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment 2020 · 369 citations
3690+2+4Years since publication100200300

Peers

Sara Kiani
Comparison fields: 5 of 155
  • Health Informatics 53
  • Health Information Management 48
  • Radiology, Nuclear Medicine and Imaging 237
  • Biochemistry 48
  • Modeling and Simulation 34
Replace Himanshu Joshi with:
Himanshu Joshi India
Xiaochun Wan China
Haleema Sadia Pakistan
Yanjie Wei China
Rajnish Kumar India
Khalid Raza India
Aman Chandra Kaushik China
Huiying Zhao China
Yufan Guo China
Qingqing Chen China
Sara Kiani relative to Himanshu Joshi India Himanshu Joshi's profile →
Citations per field
00.5×4.4×
Himanshu Joshi · 1×
Citations per year

Countries citing papers authored by Sara Kiani

Since Specialization
Citations

This map shows the geographic impact of Sara Kiani's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sara Kiani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sara Kiani more than expected).

Fields of papers citing papers by Sara Kiani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sara Kiani. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sara Kiani. The network helps show where Sara Kiani may publish in the future.

Co-authors

The 25 scholars most cited alongside Sara Kiani, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sara Kiani Line = papers co-authored together Sara Kiani links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment
Hit paper breakdown →
2020369
2 2020104
3 201967
4 202343
5 202140
6
A review study of therapeutic effects of Salvia officinalis L
201628
7 201223
8 202420
9 202119
10
Bioactivity of Sesamum indicum: A review study
201618
11 202017
12 202314
13
Astragalus membranaceus : A review study of its anti-carcinoma activities
201612
14 202010
15 20228
16 20246
17 20115
18
Chemical compound and Therapeutic effects of Hypericum perforatum
20165
19 20235
20 20215

About Sara Kiani

Sara Kiani is a scholar working on Molecular Biology, Plant Science, Surgery, Epidemiology and Genetics, having authored 31 papers that have together received 838 indexed citations. Recurring topics across this work include Autophagy in Disease and Therapy (4 papers), Extracellular vesicles in disease (4 papers), Mesenchymal stem cell research (3 papers), Sirtuins and Resveratrol in Medicine (2 papers), Tendon Structure and Treatment (2 papers), Essential Oils and Antimicrobial Activity (2 papers), Sports injuries and prevention (2 papers) and Phytochemicals and Antioxidant Activities (2 papers). The work is most often cited by research in Health Informatics (53 citations), Health Information Management (48 citations), Radiology, Nuclear Medicine and Imaging (237 citations), Biochemistry (48 citations) and Modeling and Simulation (34 citations). Sara Kiani has collaborated with scholars based in Iran, United States and Denmark. Frequent co-authors include Sepideh Miraj, Reza Khodarahmi‬, Mohammad Hosein Farzaei, Kamran Mansouri, Kamran Mansouri, Saeed Roshani, Bahare Mohamadzade, Jakub Talla, Luigi La Spada and Farimah Hadjilooei. Their work appears in journals such as Journal of Pediatric Orthopaedics, Biomedicine & Pharmacotherapy, Molecular Biology Reports, Food Reviews International and Journal of Inflammation.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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