Robert Farber
Impact in
- Aging top 10%
- Genetics, Aging, and Longevity in Model Organisms
Papers in
-
- Machine Learning in Bioinformatics 3
- Protein Structure and Dynamics 3
- RNA and protein synthesis mechanisms 2
- DNA Repair Mechanisms 1
- Glycosylation and Glycoproteins Research 1
-
- Neural dynamics and brain function 2
- Co-authors
- Alan S. Lapedes (7 shared papers)Karl Sirotkin (1 shared paper)John M. Dealy (1 shared paper)Peter F. Stadler (1 shared paper)Ivo L. Hofacker (1 shared paper)Shane L. Rea (1 shared paper)Erin Munkácsy (1 shared paper)Harold E. Trease (1 shared paper)
- Journals
- Journal of Theoretical Biology (2 papers)Physica D Nonlinear Phenomena (1 paper)Polymer Engineering and Science (1 paper)Aging (1 paper)Journal of Molecular Biology (1 paper)
- Partner nations
- United StatesCanadaAustria
In The Last Decade
Robert Farber
11 papers receiving 326 citations
Peers
Comparison fields: 5 of 74
- Aging 36
- Fluid Flow and Transfer Processes 23
- Molecular Biology 198
- Modeling and Simulation 11
- Epidemiology 65
Countries citing papers authored by Robert Farber
This map shows the geographic impact of Robert Farber'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 Robert Farber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Farber more than expected).
Fields of papers citing papers by Robert Farber
This network shows the impact of papers produced by Robert Farber. 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 Robert Farber. The network helps show where Robert Farber may publish in the future.
Co-authors
The 8 scholars most cited alongside Robert Farber, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2001 | 85 | |
| 2 | 1992 | 80 | |
| 3 | 1986 | 49 | |
| 4 | 2013 | 40 | |
| 5 | 2001 | 38 | |
| 6 | 1974 | 30 | |
| 7 | Neural net representations of empirical protein potentials. | 1995 | 11 |
| 8 | 1986 | 10 | |
| 9 | Unstructured data analysis of streaming video using parallel, high-throughput algorithms | 2007 | 2 |
| 10 | Neural Network Definitions of Highly Predictable Protein Secondary Structure Classes | 1993 | 1 |
| 11 | 1962 | 1 | |
| 12 | Massively parallel Linux laptops, workstations and clusters with CUDA | 2008 | 0 |
About Robert Farber
Robert Farber is a scholar working on Molecular Biology, Cognitive Neuroscience, Artificial Intelligence, Materials Chemistry and Infectious Diseases, having authored 12 papers that have together received 347 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (3 papers), Protein Structure and Dynamics (3 papers), Enzyme Structure and Function (2 papers), Neural dynamics and brain function (2 papers), Neural Networks and Applications (2 papers), RNA and protein synthesis mechanisms (2 papers), DNA Repair Mechanisms (1 paper) and Glycosylation and Glycoproteins Research (1 paper). The work is most often cited by research in Aging (36 citations), Fluid Flow and Transfer Processes (23 citations), Molecular Biology (198 citations), Modeling and Simulation (11 citations) and Epidemiology (65 citations). Robert Farber has collaborated with scholars based in United States, Canada and Austria. Frequent co-authors include Alan S. Lapedes, Karl Sirotkin, John M. Dealy, Peter F. Stadler, Ivo L. Hofacker, Shane L. Rea, Erin Munkácsy and Harold E. Trease. Their work appears in journals such as Journal of Theoretical Biology, Physica D Nonlinear Phenomena, Polymer Engineering and Science, Aging and Journal of Molecular Biology.
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.