Ken Takasawa

1.2k total citations · 1 hit paper
27 papers, 652 citations indexed

About

Ken Takasawa is a scholar working on Molecular Biology, Cancer Research and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Ken Takasawa has authored 27 papers receiving a total of 652 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 9 papers in Cancer Research and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Ken Takasawa's work include Epigenetics and DNA Methylation (8 papers), Cancer Genomics and Diagnostics (6 papers) and Pluripotent Stem Cells Research (5 papers). Ken Takasawa is often cited by papers focused on Epigenetics and DNA Methylation (8 papers), Cancer Genomics and Diagnostics (6 papers) and Pluripotent Stem Cells Research (5 papers). Ken Takasawa collaborates with scholars based in Japan, United Kingdom and United States. Ken Takasawa's co-authors include Ryuji Hamamoto, Masaaki Komatsu, Syuzo Kaneko, Ken Asada, Norio Shinkai, Kazuma Kobayashi, Satoshi Takahashi, Amina Bolatkan, Hidenori Machino and Ryo Shimoyama and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and The International Journal of Biochemistry & Cell Biology.

In The Last Decade

Ken Takasawa

25 papers receiving 636 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.

Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review

2024 58 citations Satoshi Takahashi, Yusuke Sakaguchi et al. Journal of Medical Systems profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ken Takasawa Japan	15	264	188	142	123	78	27	652
Tao Meng China	17	376 1.4×	257 1.4×	201 1.4×	308 2.5×	48 0.6×	58	1.1k
Jerome Cheng United States	14	117 0.4×	185 1.0×	305 2.1×	58 0.5×	59 0.8×	49	627
Hidenori Machino Japan	15	174 0.7×	263 1.4×	172 1.2×	92 0.7×	134 1.7×	25	719
Jasleen Grewal Canada	11	209 0.8×	87 0.5×	101 0.7×	98 0.8×	28 0.4×	19	490
Eva Krieghoff‐Henning Germany	17	205 0.8×	231 1.2×	270 1.9×	68 0.6×	97 1.2×	25	820
Kevin Boehm United States	5	135 0.5×	164 0.9×	128 0.9×	89 0.7×	47 0.6×	8	433
Pegah Khosravi United States	14	316 1.2×	398 2.1×	439 3.1×	126 1.0×	130 1.7×	25	1.3k
Margaret Guo United States	9	507 1.9×	202 1.1×	72 0.5×	118 1.0×	14 0.2×	15	993
Felipe Giuste United States	11	225 0.9×	82 0.4×	94 0.7×	34 0.3×	72 0.9×	36	519
Benjamin Ulfenborg Sweden	10	193 0.7×	95 0.5×	124 0.9×	62 0.5×	16 0.2×	26	560

Countries citing papers authored by Ken Takasawa

Since Specialization

Citations

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

Fields of papers citing papers by Ken Takasawa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ken Takasawa

This figure shows the co-authorship network connecting the top 25 collaborators of Ken Takasawa. A scholar is included among the top collaborators of Ken Takasawa 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 Ken Takasawa. Ken Takasawa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Horinouchi, Hidehito, Ken Takasawa, Ken Masuda, et al.. (2025). A series of natural language processing for predicting tumor response evaluation and survival curve from electronic health records. BMC Medical Informatics and Decision Making. 25(1). 85–85. 1 indexed citations

Takahashi, Satoshi, Ken Takasawa, Kota Matsui, et al.. (2025). Establishment of a machine learning model for predicting splenic hilar lymph node metastasis. npj Digital Medicine. 8(1). 93–93. 2 indexed citations

Shinkai, Norio, Ken Asada, Hidenori Machino, et al.. (2025). SEgene identifies links between super enhancers and gene expression across cell types. npj Systems Biology and Applications. 11(1). 49–49.

Takahashi, Satoshi, Yusuke Sakaguchi, Ken Takasawa, et al.. (2024). Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review. Journal of Medical Systems. 48(1). 84–84. 58 indexed citations breakdown →

Hamamoto, Ryuji, Ken Takasawa, Norio Shinkai, et al.. (2023). Analysis of super-enhancer using machine learning and its application to medical biology. Briefings in Bioinformatics. 24(3). 9 indexed citations

Takahashi, Satoshi, et al.. (2023). 10089-CO-4 DEVELOPMENT OF A PHYSICIAN SUPPORT SYSTEM FOR ANALYSIS OF GENETIC MUTATIONS IN BRAIN TUMORS AND SELECTION OF CLINICAL TRIALS USING LARGE-SCALE LANGUAGE MODELS (LLMS) WITH RETRIEVER. Neuro-Oncology Advances. 5(Supplement_5). v10–v11. 1 indexed citations

Hayashi, Tsutomu, Ken Takasawa, Takaki Yoshikawa, et al.. (2023). A discrimination model by machine learning to avoid gastrectomy for early gastric cancer. Annals of Gastroenterological Surgery. 7(6). 913–921. 3 indexed citations

Shozu, Kanto, Syuzo Kaneko, Norio Shinkai, et al.. (2022). Repression of the PRELP gene is relieved by histone deacetylase inhibitors through acetylation of histone H2B lysine 5 in bladder cancer. Clinical Epigenetics. 14(1). 147–147. 9 indexed citations

Hamamoto, Ryuji, Ken Takasawa, Hidenori Machino, et al.. (2022). Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine. Briefings in Bioinformatics. 23(4). 31 indexed citations

10.

Hamamoto, Ryuji, Takafumi Koyama, Tomohiro Yasuda, et al.. (2022). Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information. Experimental Hematology and Oncology. 11(1). 82–82. 21 indexed citations

11.

Asada, Ken, Syuzo Kaneko, Ken Takasawa, et al.. (2021). Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology. Frontiers in Oncology. 11. 666937–666937. 36 indexed citations

12.

Kaneko, Syuzo, Ken Takasawa, Ken Asada, et al.. (2021). Epigenetic Mechanisms Underlying COVID-19 Pathogenesis. Biomedicines. 9(9). 1142–1142. 7 indexed citations

13.

Takahashi, Satoshi, Ken Asada, Ken Takasawa, et al.. (2020). Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data. Biomolecules. 10(10). 1460–1460. 62 indexed citations

14.

Asada, Ken, Amina Bolatkan, Ken Takasawa, et al.. (2020). Critical Roles of N6-Methyladenosine (m6A) in Cancer and Virus Infection. Biomolecules. 10(7). 1071–1071. 18 indexed citations

15.

Nishino, Koichiro, Ken Takasawa, K. Okamura, et al.. (2020). Identification of an epigenetic signature in human induced pluripotent stem cells using a linear machine learning model. Human Cell. 34(1). 99–110. 11 indexed citations

16.

Nishino, Koichiro, Yoshikazu Arai, Ken Takasawa, et al.. (2018). Epigenetic-scale comparison of human iPSCs generated by retrovirus, Sendai virus or episomal vectors. Regenerative Therapy. 9. 71–78. 16 indexed citations

17.

Shobatake, Ryogo, Ken Takasawa, Hiroyo Ota, et al.. (2017). Up-regulation of POMC and CART mRNAs by intermittent hypoxia via GATA transcription factors in human neuronal cells. The International Journal of Biochemistry & Cell Biology. 95. 100–107. 21 indexed citations

18.

Takasawa, Ken, Yoshikazu Arai, Mayu Yamazaki‐Inoue, et al.. (2017). DNA hypermethylation enhanced telomerase reverse transcriptase expression in human-induced pluripotent stem cells. Human Cell. 31(1). 78–86. 28 indexed citations

19.

Imai, Hiroyuki, Kiyoshi Kanô, Wataru Fujii, et al.. (2015). Tetraploid Embryonic Stem Cells Maintain Pluripotency and Differentiation Potency into Three Germ Layers. PLoS ONE. 10(6). e0130585–e0130585. 9 indexed citations

20.

Takasawa, Ken. (2004). Chemical synthesis and translesion replication of a cis-syn cyclobutane thymine-uracil dimer. Nucleic Acids Research. 32(5). 1738–1745. 28 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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