Daniel S. Lieber

7.2k total citations
19 papers, 1.3k citations indexed

About

Daniel S. Lieber is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Daniel S. Lieber has authored 19 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 7 papers in Oncology and 7 papers in Cancer Research. Recurrent topics in Daniel S. Lieber's work include Cancer Genomics and Diagnostics (6 papers), Cancer Immunotherapy and Biomarkers (5 papers) and Metabolism and Genetic Disorders (4 papers). Daniel S. Lieber is often cited by papers focused on Cancer Genomics and Diagnostics (6 papers), Cancer Immunotherapy and Biomarkers (5 papers) and Metabolism and Genetic Disorders (4 papers). Daniel S. Lieber collaborates with scholars based in United States, Australia and Hong Kong. Daniel S. Lieber's co-authors include Yitzhak Pilpel, Moshe Oren, Reut Shalgi, Sarah E. Calvo, Vamsi K. Mootha, Steven G. Hershman, Shangtao Liu, David R. Thorburn, Adrienne Laskowski and John Christodoulou and has published in prestigious journals such as PLoS ONE, Neurology and Cancer Research.

In The Last Decade

Daniel S. Lieber

19 papers receiving 1.3k citations

Peers

Daniel S. Lieber
Thomas Nelius United States
Songtao Yuan China
Bing Jiang China
Hanlin Gao United States
Yisheng Zhong China
Erhan Bilal United States
Zhiqiang Lin China
Hui Kong China
Dawei Luo China
Felix Graßmann Germany
Thomas Nelius United States
Daniel S. Lieber
Citations per year, relative to Daniel S. Lieber Daniel S. Lieber (= 1×) peers Thomas Nelius

Countries citing papers authored by Daniel S. Lieber

Since Specialization
Citations

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

Fields of papers citing papers by Daniel S. Lieber

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel S. Lieber

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

All Works

19 of 19 papers shown
1.
Lee, Jessica, Mehlika Hazar-Rethinam, Brennan Decker, et al.. (2021). The Pan-Tumor Landscape of Targetable Kinase Fusions in Circulating Tumor DNA. Clinical Cancer Research. 28(4). 728–737. 28 indexed citations
2.
Fabrizio, David, Daniel S. Lieber, Christine M. Malboeuf, et al.. (2018). Abstract 5706: A blood-based next-generation sequencing assay to determine tumor mutational burden (bTMB) is associated with benefit to an anti-PD-L1 inhibitor, atezolizumab. Cancer Research. 78(13_Supplement). 5706–5706. 28 indexed citations
3.
Rittmeyer, Achim, David R. Gandara, Marcin Kowanetz, et al.. (2018). Blood-Based Biomarkers for Cancer Immunotherapy: Tumor Mutational Burden in Blood (bTMB) is Associated with Improved Atezolizumab (atezo) Efficacy in 2L+ NSCLC (POPLAR and OAK). Pneumologie. 72(S 01). S49–S50. 11 indexed citations
4.
5.
Gandara, David R., Marcin Kowanetz, Tony Mok, et al.. (2017). Blood-based biomarkers for cancer immunotherapy: Tumor mutational burden in blood (bTMB) is associated with improved atezolizumab (atezo) efficacy in 2L+ NSCLC (POPLAR and OAK). Annals of Oncology. 28. v460–v460. 62 indexed citations
6.
Lieber, Daniel S., Mark Kennedy, Douglas B. Johnson, et al.. (2017). Abstract B16: Validation and clinical feasibility of a Foundation Medicine assay to identify immunotherapy response potential through tumor mutational burden (TMB). Cancer Immunology Research. 5(3_Supplement). B16–B16. 1 indexed citations
7.
Lieber, Daniel S., Mark Kennedy, Douglas B. Johnson, et al.. (2017). Abstract 2987: Validation and clinical feasibility of a comprehensive genomic profiling assay to identify likely immunotherapy responders through tumor mutational burden (TMB). Cancer Research. 77(13_Supplement). 2987–2987. 3 indexed citations
8.
Chennagiri, Niru, Eric J. White, Daniel S. Lieber, et al.. (2016). Orthogonal NGS for High Throughput Clinical Diagnostics. Scientific Reports. 6(1). 24650–24650. 11 indexed citations
9.
Sapkota, Kiran, et al.. (2015). GluN2D N-Methyl-d-Aspartate Receptor Subunit Contribution to the Stimulation of Brain Activity and Gamma Oscillations by Ketamine: Implications for Schizophrenia. Journal of Pharmacology and Experimental Therapeutics. 356(3). 702–711. 50 indexed citations
10.
Lieber, Daniel S., Steven G. Hershman, Nancy G. Slate, et al.. (2014). Next generation sequencing with copy number variant detection expands the phenotypic spectrum of HSD17B4-deficiency. BMC Medical Genetics. 15(1). 30–30. 35 indexed citations
11.
Lieber, Daniel S., et al.. (2013). Wissensentdeckung im industriellen Kontext. Zeitschrift für wirtschaftlichen Fabrikbetrieb. 108(6). 388–393. 3 indexed citations
12.
Lieber, Daniel S., Marco Stolpe, Benedikt Konrad, Jochen Deuse, & Katharina Morik. (2013). Quality Prediction in Interlinked Manufacturing Processes based on Supervised & Unsupervised Machine Learning. Procedia CIRP. 7. 193–198. 83 indexed citations
13.
Lieber, Daniel S., Sarah E. Calvo, Nancy G. Slate, et al.. (2013). Targeted exome sequencing of suspected mitochondrial disorders. Neurology. 80(19). 1762–1770. 116 indexed citations
14.
Lieber, Daniel S., Scott B. Vafai, Laura Horton, et al.. (2012). Atypical case of Wolfram syndrome revealed through targeted exome sequencing in a patient with suspected mitochondrial disease. BMC Medical Genetics. 13(1). 3–3. 25 indexed citations
15.
Deuse, Jochen, Benedikt Konrad, Daniel S. Lieber, Katharina Morik, & Marco Stolpe. (2012). Challenges for Data Mining on Sensor Data of Interlinked Processes. Technische Universität Dortmund Eldorado (Technische Universität Dortmund). 2 indexed citations
16.
Calvo, Sarah E., Alison G. Compton, Steven G. Hershman, et al.. (2012). Molecular Diagnosis of Infantile Mitochondrial Disease with Targeted Next-Generation Sequencing. Science Translational Medicine. 4(118). 118ra10–118ra10. 343 indexed citations
17.
Lieber, Daniel S., Olivier Elemento, & Saeed Tavazoie. (2010). Large-Scale Discovery and Characterization of Protein Regulatory Motifs in Eukaryotes. PLoS ONE. 5(12). e14444–e14444. 27 indexed citations
18.
Shalgi, Reut, Daniel S. Lieber, Moshe Oren, & Yitzhak Pilpel. (2007). Global and Local Architecture of the Mammalian microRNA–Transcription Factor Regulatory Network. PLoS Computational Biology. 3(7). e131–e131. 411 indexed citations
19.
Lieber, Daniel S., Richard L. Lieber, & William C. Adams. (1989). Effects of run-training and swim-training at similar absolute intensities on treadmill VO2max. Medicine & Science in Sports & Exercise. 21(6). 655–655. 31 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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