K. Wang

7.0k citations
50 papers · 600 · 1 hit paper · h-index 11

Impact in

Papers in

K. Wang

48 papers receiving 584 citations

K. Wang's Hit Papers

DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants 2022 · 155 citations
1550+1+2Years since publication50100150

Peers

K. Wang
Comparison fields: 5 of 106
  • Atomic and Molecular Physics, and Optics 180
  • Radiation 38
  • Statistical and Nonlinear Physics 55
  • Cancer Research 50
  • Genetics 95
Replace Hin Hark Gan with:
Hin Hark Gan United States
Prabhakar Pradhan United States
J. F. Hu China
Cong Li China
Rick Mukherjee India
Ž. Bajzer Croatia
Jens Decker Germany
Jingzhong Guo United States
Renmin Han China
K. Wang relative to Hin Hark Gan United States Hin Hark Gan's profile →
Citations per field
00.5×7.6×
Hin Hark Gan · 1×
Citations per year

Countries citing papers authored by K. Wang

Since Specialization
Citations

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

Fields of papers citing papers by K. Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 50 papers — load more, or switch the sort, to bring in the rest.

#Work
1
DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants
Hit paper breakdown →
2022155
2 201690
3 201636
4 200931
5 200431
6 201230
7 199528
8 199420
9 199818
10 200812
11 199912
12 202410
13 201810
14 20159
15 20129
16 20079
17 20019
18 20226
19 19966
20 19905

About K. Wang

K. Wang is a scholar working on Atomic and Molecular Physics, and Optics, Statistical and Nonlinear Physics, Electrical and Electronic Engineering, Radiation and Artificial Intelligence, having authored 50 papers that have together received 600 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (6 papers), Quantum and electron transport phenomena (6 papers), Advanced Radiotherapy Techniques (6 papers), Cold Atom Physics and Bose-Einstein Condensates (6 papers), Nonlinear Waves and Solitons (5 papers), Nonlinear Photonic Systems (4 papers), Medical Imaging Techniques and Applications (4 papers) and Semiconductor Quantum Structures and Devices (3 papers). The work is most often cited by research in Atomic and Molecular Physics, and Optics (180 citations), Radiation (38 citations), Statistical and Nonlinear Physics (55 citations), Cancer Research (50 citations) and Genetics (95 citations). K. Wang has collaborated with scholars based in China, United States and India. Frequent co-authors include Muhammad Abid, Awais Rasheed, José Crossa, Sarah Hearne, Huihui Li, Qing‐Hu Chen, Mang Feng, Shaolong Wan, Zhengkuan Jiao and Yuhang Ren. Their work appears in journals such as Physics Letters A, physica status solidi (b), Physical Review A, The European Physical Journal B and International Journal of Radiation Oncology*Biology*Physics.

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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