Deepali Sachdev

30 papers receiving 2.3k citations

Peers

Deepali Sachdev
Comparison fields: 5 of 99
  • Endocrinology, Diabetes and Metabolism 929
  • Cancer Research 511
  • Oncology 619
  • Molecular Biology 1.4k
  • Genetics 236
Replace Frédéric Troalen with:
Frédéric Troalen France
JEAN SACCUZZO BEEBE United States
Makoto Tsuneoka Japan
Alexei Y. Savinov United States
Denise Ming Tse Yu Australia
Alessandro Porrello United States
Yixing Jiang United States
Sara Alexandra Vinhas Ricardo Portugal
Karen L. Wion United Kingdom
Shusuke Akamatsu Japan
Deepali Sachdev relative to Frédéric Troalen France Frédéric Troalen's profile →
Citations per field
00.5×2×3.4×
Frédéric Troalen · 1×
Citations per year

Countries citing papers authored by Deepali Sachdev

Since Specialization
Citations

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

Fields of papers citing papers by Deepali Sachdev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2007282
2 2001281
3 2008278
4
A chimeric humanized single-chain antibody against the type I insulin-like growth factor (IGF) receptor renders breast cancer cells refractory to the mitogenic effects of IGF-I.
2003168
5 2004140
6 2010111
7 2006102
8 201295
9 201595
10 199889
11 200981
12 200071
13 199862
14 200646
15 200445
16 200839
17 201938
18 200336
19 199934
20 200933

About Deepali Sachdev

Deepali Sachdev is a scholar working on Endocrinology, Diabetes and Metabolism, Cancer Research, Molecular Biology, Oncology and Radiology, Nuclear Medicine and Imaging, having authored 30 papers that have together received 2.3k indexed citations. Recurring topics across this work include Growth Hormone and Insulin-like Growth Factors (17 papers), Metabolism, Diabetes, and Cancer (11 papers), Cancer, Hypoxia, and Metabolism (9 papers), Monoclonal and Polyclonal Antibodies Research (4 papers), Bacterial Genetics and Biotechnology (3 papers), Glycosylation and Glycoproteins Research (2 papers), Protein purification and stability (2 papers) and PI3K/AKT/mTOR signaling in cancer (2 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (929 citations), Cancer Research (511 citations), Oncology (619 citations), Molecular Biology (1.4k citations) and Genetics (236 citations). Deepali Sachdev has collaborated with scholars based in United States, Japan and France. Frequent co-authors include Douglas Yee, John M. Chirgwin, Yoko Fujita‐Yamaguchi, Dedra H. Fagan, Xihong Zhang, Martine Gaillard-Kelly, Adrian V. Lee, Jeffrey S. Miller, Rajeeva Singh and Xianke Zeng. Their work appears in journals such as Oncogene, Endocrine Related Cancer, Journal of Mammary Gland Biology and Neoplasia, Breast Cancer Research and Treatment and Clinical Cancer Research.

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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