Charles J. Di Como

3.7k total citations
18 papers, 3.1k citations indexed

About

Charles J. Di Como is a scholar working on Molecular Biology, Oncology and Biotechnology. According to data from OpenAlex, Charles J. Di Como has authored 18 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 9 papers in Oncology and 4 papers in Biotechnology. Recurrent topics in Charles J. Di Como's work include Cancer-related Molecular Pathways (8 papers), Fungal and yeast genetics research (5 papers) and Cancer Research and Treatments (4 papers). Charles J. Di Como is often cited by papers focused on Cancer-related Molecular Pathways (8 papers), Fungal and yeast genetics research (5 papers) and Cancer Research and Treatments (4 papers). Charles J. Di Como collaborates with scholars based in United States, Italy and United Kingdom. Charles J. Di Como's co-authors include Kim Arndt, Carol Prives, Christian Gaiddon, María E. Cárdenas, Joseph Heitman, N. Shane Cutler, Michael Lorenz, Carlos Cordon‐Cardo, Marshall Urist and Axel Hoos and has published in prestigious journals such as Cell, Journal of Biological Chemistry and The Journal of Experimental Medicine.

In The Last Decade

Charles J. Di Como

18 papers receiving 3.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Charles J. Di Como United States 16 2.4k 1.2k 375 367 229 18 3.1k
Jan Kovařík Czechia 25 972 0.4× 1.2k 1.0× 262 0.7× 285 0.8× 130 0.6× 57 2.3k
Jenő Gyuris United States 23 4.0k 1.7× 1.7k 1.4× 315 0.8× 836 2.3× 385 1.7× 56 5.5k
Masatoshi Tagawa Japan 35 2.3k 0.9× 1.7k 1.4× 431 1.1× 242 0.7× 85 0.4× 210 4.6k
Robert E. Hollingsworth United States 32 2.0k 0.8× 1.4k 1.2× 105 0.3× 411 1.1× 109 0.5× 66 3.7k
Eros Lazzerini Denchi United States 29 4.7k 2.0× 1.5k 1.3× 217 0.6× 495 1.3× 416 1.8× 45 5.7k
Kenkichi Masutomi Japan 28 2.0k 0.8× 807 0.7× 259 0.7× 137 0.4× 117 0.5× 57 3.2k
Mark A. Subler United States 32 2.0k 0.8× 1.5k 1.3× 268 0.7× 226 0.6× 66 0.3× 65 3.0k
Laurent Créancier France 21 2.3k 1.0× 1.3k 1.1× 488 1.3× 179 0.5× 53 0.2× 28 3.0k
Gerrit J.P. Dijkgraaf United States 20 2.4k 1.0× 918 0.8× 100 0.3× 216 0.6× 243 1.1× 21 3.0k
Brian Elenbaas United States 17 2.8k 1.2× 2.4k 2.1× 524 1.4× 331 0.9× 67 0.3× 26 4.1k

Countries citing papers authored by Charles J. Di Como

Since Specialization
Citations

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

Fields of papers citing papers by Charles J. Di Como

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charles J. Di Como. 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 Charles J. Di Como. The network helps show where Charles J. Di Como may publish in the future.

Co-authorship network of co-authors of Charles J. Di Como

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

All Works

18 of 18 papers shown
1.
Han, Sandra Y., Weiming Gai, Molly Yancovitz, et al.. (2008). Nucleofection is a highly effective gene transfer technique for human melanoma cell lines. Experimental Dermatology. 17(5). 405–411. 11 indexed citations
2.
Como, Charles J. Di, et al.. (2006). The Association of Tap42-Phosphatase Complexes with TORC1 — Another Level of Regulation in Tor Signaling. Cell Cycle. 5(23). 2729–2732. 15 indexed citations
3.
Hedvat, Cyrus V., Julie Teruya‐Feldstein, Pere Puig, et al.. (2005). Expression of p63 in Diffuse Large B-Cell Lymphoma. Applied immunohistochemistry & molecular morphology. 13(3). 237–242. 40 indexed citations
4.
Bernassola, Francesca, Paolo Salomoni, Andrew Oberst, et al.. (2004). Ubiquitin-dependent Degradation of p73 Is Inhibited by PML. The Journal of Experimental Medicine. 199(11). 1545–1557. 95 indexed citations
5.
Puig, Pere, Paola Capodieci, Marija Drobnjak, et al.. (2003). p73 Expression in human normal and tumor tissues: loss of p73alpha expression is associated with tumor progression in bladder cancer.. PubMed. 9(15). 5642–51. 60 indexed citations
6.
Como, Charles J. Di, Marshall Urist, Irina Babayan, et al.. (2002). p63 expression profiles in human normal and tumor tissues.. PubMed. 8(2). 494–501. 432 indexed citations
7.
Urist, Marshall, Charles J. Di Como, Elizabeth Charytonowicz, et al.. (2002). Loss of p63 Expression Is Associated with Tumor Progression in Bladder Cancer. American Journal Of Pathology. 161(4). 1199–1206. 212 indexed citations
8.
Torres‐Rosell, Jordi, Charles J. Di Como, Enrique Herrero, & María Ángeles de la Torre-Ruiz. (2002). Regulation of the Cell Integrity Pathway by Rapamycin-sensitive TOR Function in Budding Yeast. Journal of Biological Chemistry. 277(45). 43495–43504. 118 indexed citations
9.
Hoos, Axel, Alexander Stojadinovic, Stephen Mastorides, et al.. (2001). High Ki-67 proliferative index predicts disease specific survival in patients with high-risk soft tissue sarcomas. Cancer. 92(4). 869–874. 78 indexed citations
10.
Ollmann, Michael, Lynn Young, Charles J. Di Como, et al.. (2000). Drosophila p53 Is a Structural and Functional Homolog of the Tumor Suppressor p53. Cell. 101(1). 91–101. 336 indexed citations
11.
Nath, Kamalendu, et al.. (2000). Effects of ethidium bromide and SYBR® Green I on different polymerase chain reaction systems. Journal of Biochemical and Biophysical Methods. 42(1-2). 15–29. 65 indexed citations
12.
Como, Charles J. Di, Christian Gaiddon, & Carol Prives. (1999). p73 Function Is Inhibited by Tumor-Derived p53 Mutants in Mammalian Cells. Molecular and Cellular Biology. 19(2). 1438–1449. 363 indexed citations
13.
Cárdenas, María E., N. Shane Cutler, Michael Lorenz, Charles J. Di Como, & Joseph Heitman. (1999). The TOR signaling cascade regulates gene expression in response to nutrients. Genes & Development. 13(24). 3271–3279. 482 indexed citations
14.
Como, Charles J. Di & Carol Prives. (1998). Human tumor-derived p53 proteins exhibit binding site selectivity and temperature sensitivity for transactivation in a yeast-based assay. Oncogene. 16(19). 2527–2539. 83 indexed citations
15.
Luke, May M., et al.. (1996). The SAPs, a New Family of Proteins, Associate and Function Positively with the SIT4 Phosphatase. Molecular and Cellular Biology. 16(6). 2744–2755. 136 indexed citations
16.
Como, Charles J. Di & Kim Arndt. (1996). Nutrients, via the Tor proteins, stimulate the association of Tap42 with type 2A phosphatases.. Genes & Development. 10(15). 1904–1916. 431 indexed citations
17.
Como, Charles J. Di, et al.. (1995). Activation of CLN1 and CLN2 G 1 Cyclin Gene Expression by BCK2. Molecular and Cellular Biology. 15(4). 1835–1846. 92 indexed citations
18.
Como, Charles J. Di, Rohit Bose, & Kim Arndt. (1995). Overexpression of SIS2, which contains an extremely acidic region, increases the expression of SWI4, CLN1 and CLN2 in sit4 mutants.. Genetics. 139(1). 95–107. 73 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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