Daniel G. Chain

1.5k total citations
17 papers, 1.2k citations indexed

About

Daniel G. Chain is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Daniel G. Chain has authored 17 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 5 papers in Cellular and Molecular Neuroscience and 5 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Daniel G. Chain's work include Blood properties and coagulation (4 papers), Alzheimer's disease research and treatments (4 papers) and Ubiquitin and proteasome pathways (4 papers). Daniel G. Chain is often cited by papers focused on Blood properties and coagulation (4 papers), Alzheimer's disease research and treatments (4 papers) and Ubiquitin and proteasome pathways (4 papers). Daniel G. Chain collaborates with scholars based in United States, Israel and United Kingdom. Daniel G. Chain's co-authors include Ashok N. Hegde, James H. Schwartz, Burkhard Pöeggeler, Miguel A. Pappolla, Andrea Casadio, Eric R. Kandel, Blas Frangione, Jorge Ghiso, Rawhi Omar and Shmuel Shaltiel and has published in prestigious journals such as Cell, Journal of Biological Chemistry and Neuron.

In The Last Decade

Daniel G. Chain

17 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel G. Chain United States 12 661 326 219 195 150 17 1.2k
Nobuaki Okumura Japan 26 1.2k 1.8× 346 1.1× 424 1.9× 456 2.3× 113 0.8× 68 2.0k
Mohammed A. Kashem United States 25 858 1.3× 456 1.4× 170 0.8× 120 0.6× 51 0.3× 58 1.8k
Silva Hečimović Croatia 20 818 1.2× 226 0.7× 852 3.9× 199 1.0× 128 0.9× 44 1.6k
Robert C. Cumming Canada 20 1.2k 1.8× 244 0.7× 499 2.3× 226 1.2× 92 0.6× 33 1.9k
Donna Bozyczko‐Coyne United States 25 917 1.4× 481 1.5× 230 1.1× 292 1.5× 101 0.7× 30 1.7k
Daniel B. McClatchy United States 27 1.7k 2.6× 405 1.2× 217 1.0× 306 1.6× 99 0.7× 54 2.3k
Marialaura Amadio Italy 28 1.6k 2.4× 309 0.9× 313 1.4× 177 0.9× 109 0.7× 59 2.4k
Mi-Sung Kim South Korea 16 1.4k 2.1× 193 0.6× 318 1.5× 180 0.9× 232 1.5× 22 1.9k
Emmanuel Canet France 22 717 1.1× 379 1.2× 596 2.7× 82 0.4× 45 0.3× 44 2.0k
Brian Lockhart France 24 956 1.4× 450 1.4× 518 2.4× 56 0.3× 94 0.6× 51 2.0k

Countries citing papers authored by Daniel G. Chain

Since Specialization
Citations

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

Fields of papers citing papers by Daniel G. Chain

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel G. Chain. 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 Daniel G. Chain. The network helps show where Daniel G. Chain may publish in the future.

Co-authorship network of co-authors of Daniel G. Chain

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

All Works

17 of 17 papers shown
1.
Chain, Daniel G. & Richard Margolin. (2023). AlphaFold 2 predicts opening of the tau hairpin conformation by Mapt mutations and the extent of opening reflects their location. Alzheimer s & Dementia. 19(S24). 1 indexed citations
2.
Chain, Daniel G. & Richard Margolin. (2023). The AI‐based AlphaFold 2 algorithm predicts an open structure for tauC3. Alzheimer s & Dementia. 19(S24). 1 indexed citations
3.
Jiang, Mei, Se‐Eun Jang, Dongrui Ma, et al.. (2022). The APP intracellular domain promotes LRRK2 expression to enable feed-forward neurodegenerative mechanisms in Parkinson’s disease. Science Signaling. 15(748). eabk3411–eabk3411. 8 indexed citations
4.
Imbimbo, Bruno P., Mercedes Fernández, Luciana Giardino, et al.. (2014). O4‐11‐01: RELATIONSHIP BETWEEN CEREBROSPINAL FLUID (CSF) BIOMARKERS AND COGNITIVE PERFORMANCE OF PATIENTS WITH MILD COGNITIVE IMPAIRMENT (MCI) AFTER LONG‐TERM TREATMENT WITH CHF5074. Alzheimer s & Dementia. 10(4S_Part_5). 2 indexed citations
5.
Bendheim, Paul E., et al.. (2002). Development of indole-3-propionic acid (OXIGON™) for alzheimer’s disease. Journal of Molecular Neuroscience. 19(1-2). 213–217. 138 indexed citations
6.
Chain, Daniel G., Andrea Casadio, Samuel Schacher, et al.. (1999). Mechanisms for Generating the Autonomous cAMP-Dependent Protein Kinase Required for Long-Term Facilitation in Aplysia. Neuron. 22(1). 147–156. 144 indexed citations
7.
Chain, Daniel G., James H. Schwartz, & Ashok N. Hegde. (1999). Ubiquitin-mediated proteolysis in learning and memory. Molecular Neurobiology. 20(2-3). 125–142. 48 indexed citations
8.
Yamamoto, Naoki, Ashok N. Hegde, Daniel G. Chain, & James H. Schwartz. (1999). Activation and Degradation of the Transcription Factor C/EBP During Long‐Term Facilitation in Aplysia. Journal of Neurochemistry. 73(6). 2415–2423. 52 indexed citations
9.
Pöeggeler, Burkhard, Rawhi Omar, Daniel G. Chain, et al.. (1999). Potent Neuroprotective Properties against the Alzheimer β-Amyloid by an Endogenous Melatonin-related Indole Structure, Indole-3-propionic Acid. Journal of Biological Chemistry. 274(31). 21937–21942. 330 indexed citations
10.
Hegde, Ashok N., Kaoru Inokuchi, Andrea Casadio, et al.. (1997). Ubiquitin C-Terminal Hydrolase Is an Immediate-Early Gene Essential for Long-Term Facilitation in Aplysia. Cell. 89(1). 115–126. 299 indexed citations
11.
Chain, Daniel G., et al.. (1991). Plasmin cleavage of vitronectin Identification of the site and consequent attenuation in binding plasminogen activator inhibitor‐1. FEBS Letters. 285(2). 251–256. 49 indexed citations
12.
Chain, Daniel G., Beatriz Korc‐Grodzicki, Tamar Kreizman, & Shmuel Shaltiel. (1991). Endogenous cleavage of the Arg-379-Ala-380 bond in vitronectin results in a distinct conformational change which ‘buries’ Ser-378, its site of phosphorylation by protein kinase A. Biochemical Journal. 274(2). 387–394. 30 indexed citations
13.
Chain, Daniel G., Beatriz Korc‐Grodzicki, Tamar Kreizman, & Shmuel Shaltiel. (1990). The phosphorylation of the two‐chain form of vitronectin by protein kinase A is heparin dependent. FEBS Letters. 269(1). 221–225. 30 indexed citations
14.
Korc‐Grodzicki, Beatriz, Daniel G. Chain, Tamar Kreizman, & Shmuel Shaltiel. (1990). An enzymatic assay for vitronectin based on its selective phosphorylation by protein kinase A. Analytical Biochemistry. 188(2). 288–294. 18 indexed citations
15.
Chain, Daniel G., et al.. (1988). Vitronectin is phosphorylated by a cAMP-dependent protein kinase released by activation of human platelets with thrombin. Biochemical and Biophysical Research Communications. 157(3). 1131–1138. 44 indexed citations
16.
Davson, Hugh, et al.. (1986). Steady-state distribution of cycloleucine and α-aminoisobutyric acid between plasma and cerebrospinal fluid. Experimental Neurology. 91(1). 163–173. 7 indexed citations
17.
Zloković, Berislav V., David J. Begley, & Daniel G. Chain. (1983). Blood-brain barrier permeability to dipeptides and their constituent amino acids. Brain Research. 271(1). 65–71. 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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