Akram Mohammed

691 citations
29 papers · 420 · h-index 12

Impact in

Papers in

    • Sepsis Diagnosis and Treatment 7
    • Machine Learning in Bioinformatics 3
    • Gene expression and cancer classification 2
    • Gut microbiota and health 2
    • RNA and protein synthesis mechanisms 2
    • Bioinformatics and Genomic Networks 2

Akram Mohammed

25 papers receiving 414 citations

Peers

Akram Mohammed
Comparison fields: 5 of 101
  • Family Practice 16
  • Health Informatics 9
  • Epidemiology 127
  • Critical Care and Intensive Care Medicine 12
  • Health Information Management 12
Replace Pooja Pradhan with:
Pooja Pradhan India
Enrico Mossotto United Kingdom
Selena Z. Kuo United States
Lin Guo China
Matthew Zhang United States
Fei Xia China
Zirun Zhao United States
Jeong Hoon Lee South Korea
Hyun Wook Han South Korea
Shuai Chang China
Akram Mohammed relative to Pooja Pradhan India Pooja Pradhan's profile →
Citations per field
00.5×
Pooja Pradhan · 1×
Citations per year

Countries citing papers authored by Akram Mohammed

Since Specialization
Citations

This map shows the geographic impact of Akram Mohammed'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 Akram Mohammed with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akram Mohammed more than expected).

Fields of papers citing papers by Akram Mohammed

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Akram Mohammed. 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 Akram Mohammed. The network helps show where Akram Mohammed may publish in the future.

Co-authors

The 25 scholars most cited alongside Akram Mohammed, 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 Akram Mohammed Line = papers co-authored together Akram Mohammed links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201854
2 202151
3 202035
4 201832
5 201532
6 201930
7 202130
8 201720
9 202017
10 201517
11 202113
12 202112
13 202211
14 202011
15 20239
16 20169
17 20179
18 20118
19 20227
20 20233

About Akram Mohammed

Akram Mohammed is a scholar working on Epidemiology, Molecular Biology, Genetics, Immunology and Surgery, having authored 29 papers that have together received 420 indexed citations. Recurring topics across this work include Sepsis Diagnosis and Treatment (7 papers), Machine Learning in Bioinformatics (3 papers), Hemodynamic Monitoring and Therapy (2 papers), Gene expression and cancer classification (2 papers), Gut microbiota and health (2 papers), RNA and protein synthesis mechanisms (2 papers), Clusterin in disease pathology (2 papers) and Bioinformatics and Genomic Networks (2 papers). The work is most often cited by research in Family Practice (16 citations), Health Informatics (9 citations), Epidemiology (127 citations), Critical Care and Intensive Care Medicine (12 citations) and Health Information Management (12 citations). Akram Mohammed has collaborated with scholars based in United States, India and Vietnam. Frequent co-authors include Rishikesan Kamaleswaran, Robert L. Davis, Anahita Khojandi, Chittibabu Guda, Franco van Wyk, Tomáš Helikar, Nades Palaniyar, Hector R. Wong, Edmon Begoli and Valeria R. Mas. Their work appears in journals such as Scientific Reports, BMC Genomics, International Journal of Molecular Sciences, Oncotarget and Nature Communications.

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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