Qi Song

2.4k total citations · 1 hit paper
8 papers, 1.6k citations indexed

About

Qi Song is a scholar working on Molecular Biology, Infectious Diseases and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Qi Song has authored 8 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 2 papers in Infectious Diseases and 2 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Qi Song's work include Radiomics and Machine Learning in Medical Imaging (2 papers), RNA and protein synthesis mechanisms (2 papers) and RNA modifications and cancer (1 paper). Qi Song is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (2 papers), RNA and protein synthesis mechanisms (2 papers) and RNA modifications and cancer (1 paper). Qi Song collaborates with scholars based in China, United States and Macao. Qi Song's co-authors include Yi Lu, Kunlin Cao, Junjie Bai, Bin Kong, Youbing Yin, Xin Wang, Shiqin Zhang, Lin Li, Lixin Qin and Qizhong Xu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Radiology and Lab on a Chip.

In The Last Decade

Qi Song

8 papers receiving 1.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy

2020 1.3k citations Lin Li, Lixin Qin et al. Radiology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Qi Song China	5	1.3k	593	315	300	256	8	1.6k
Junjie Bai China	14	1.4k 1.1×	677 1.1×	211 0.7×	327 1.1×	167 0.7×	37	2.0k
Minjie Lin China	6	1.6k 1.3×	411 0.7×	1.0k 3.3×	176 0.6×	232 0.9×	8	2.3k
Zhenghan Fang China	9	1.4k 1.1×	611 1.0×	200 0.6×	300 1.0×	116 0.5×	23	1.6k
Daliang Liu China	5	1.2k 1.0×	585 1.0×	199 0.6×	300 1.0×	88 0.3×	12	1.4k
Lixin Qin China	4	1.3k 1.0×	637 1.1×	235 0.7×	323 1.1×	83 0.3×	9	1.5k
Juan Xia China	4	1.2k 1.0×	583 1.0×	259 0.8×	295 1.0×	80 0.3×	8	1.4k
Xingzhi Xie China	12	1.7k 1.4×	447 0.8×	1.4k 4.4×	138 0.5×	258 1.0×	22	2.7k
Qizhong Xu China	5	1.2k 1.0×	583 1.0×	217 0.7×	296 1.0×	81 0.3×	7	1.3k

Countries citing papers authored by Qi Song

Since Specialization

Citations

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

Fields of papers citing papers by Qi Song

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qi Song

This figure shows the co-authorship network connecting the top 25 collaborators of Qi Song. A scholar is included among the top collaborators of Qi Song based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Qi Song. Qi Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Song, Qi, Yixuan Wang, & Sen Liu. (2024). Subtype‐Specific Transcription Factors Affect Polyamine Metabolism and the Tumor Microenvironment in Breast Cancer. SHILAP Revista de lepidopterología. 4(1). e138–e138. 1 indexed citations

2.

Tong, Xiaoxue, Juan Xie, Xudong Liu, et al.. (2023). An updated dataset and a structure‐based prediction model for protein– RNA binding affinity. Proteins Structure Function and Bioinformatics. 91(9). 1245–1253. 5 indexed citations

3.

Song, Qi, Lingyu Zeng, Qiang Zheng, & Sen Liu. (2023). SCARdock: A Web Server and Manually Curated Resource for Discovering Covalent Ligands. ACS Omega. 8(11). 10397–10402. 4 indexed citations

4.

Zheng, Jinfang, Juan Xie, Xiaoxue Tong, et al.. (2021). RR3DD: an RNA global structure-based RNA three-dimensional structural classification database. RNA Biology. 18(sup2). 738–746. 4 indexed citations

5.

Song, Qi, Xindi Sun, Ziyi Dai, et al.. (2021). Point-of-care testing detection methods for COVID-19. Lab on a Chip. 21(9). 1634–1660. 194 indexed citations

6.

Li, Lin, Lixin Qin, Youbing Yin, et al.. (2020). Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology. 296(2). E65–E71. 1309 indexed citations breakdown →

7.

Kong, Bin, Xin Wang, Junjie Bai, et al.. (2019). Learning tree-structured representation for 3D coronary artery segmentation. Computerized Medical Imaging and Graphics. 80. 101688–101688. 59 indexed citations

8.

Xu, Yanan, Jiumei Hu, Qiangyuan Zhu, Qi Song, & Ying Mu. (2018). Co-detection of ALDH1A1, ABCG2, ALCAM and CD133 in three A549 subpopulations at the single cell level by one-step digital RT-PCR. Integrative Biology. 10(6). 364–369. 9 indexed citations

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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