Vaibhav A. Narayan

Active 1997–2025

Also published as
Vaibhav A Narayan
55
Papers
21,111
Citations
27
h-index
39
i10-index

Citations

Citations per year for Vaibhav A. Narayan1877: 1 citations1930: 1 citations1966: 1 citations1968: 1 citations1973: 2 citations1980: 1 citations1987: 2 citations1993: 1 citations1994: 1 citations1997: 1 citations1998: 4 citations1999: 3 citations2000: 58 citations2001: 398 citations2002: 520 citations2003: 505 citations2004: 454 citations2005: 388 citations2006: 335 citations2007: 348 citations2008: 299 citations2009: 262 citations2010: 246 citations2011: 203 citations2012: 157 citations2013: 192 citations2014: 174 citations2015: 172 citations2016: 163 citations2017: 149 citations2018: 185 citations2019: 263 citations2020: 269 citations2021: 322 citations2022: 275 citations2023: 240 citations2024: 311 citations2025: 186 citations2026: 15 citations1878–1929: no citations, so these years are not shown1931–1965: no citations, so these years are not shown1967: no citations, so this year is not shown1969–1972: no citations, so these years are not shown1974–1979: no citations, so these years are not shown1981–1986: no citations, so these years are not shown1988–1992: no citations, so these years are not shown1995–1996: no citations, so these years are not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 3,522 citing papers, 34.7% of this breakdownUnited Kingdom: 832 citing papers, 8.2% of this breakdownChina: 757 citing papers, 7.4% of this breakdownGermany: 568 citing papers, 5.6% of this breakdownCanada: 454 citing papers, 4.5% of this breakdownFrance: 326 citing papers, 3.2% of this breakdownJapan: 295 citing papers, 2.9% of this breakdownItaly: 272 citing papers, 2.7% of this breakdownSpain: 270 citing papers, 2.6% of this breakdownAustralia: 237 citing papers, 2.3% of this breakdownSwitzerland: 221 citing papers, 2.2% of this breakdownNetherlands: 184 citing papers, 1.8% of this breakdown
0%34.7%Other 21.9%

Fields

  • Biochemistry, Genetics and Molecular Biology63.4%
  • Medicine11.8%
  • Computer Science7%
  • Agricultural and Biological Sciences3.1%
  • Neuroscience2.7%
  • Chemistry2.1%
  • Other9.9%

Topics

  • Bioinformatics and Genomic Networks7.5%
  • Genomics and Phylogenetic Studies4.4%
  • RNA and protein synthesis mechanisms4.3%
  • Protein Structure and Dynamics2.3%
  • Machine Learning in Bioinformatics2.3%
  • Gene expression and cancer classification2.3%
  • Other76.9%

Coauthors

All papers

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  1. The Sequence of the Human Genome

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Arnold J. Levine, Richard J. Roberts, Mel I. Simon, Carolyn W. Slayman, Michael W. Hunkapiller, Randall Bolanos, Arthur L. Delcher, Ian Dew, Daniel Fasulo, Michael J. Flanigan, Liliana Florea, Aaron L. Halpern, Sridhar Hannenhalli, Saul Kravitz, Samuel Lévy, Clark Mobarry, Knut Reinert, Karin Remington, Jane Abu-Threideh, Ellen M. Beasley, Kendra Biddick, Vivien Bonazzi, Rhonda Brandon, Michele Cargill, Ishwar Chandramouliswaran, Rosane Charlab, Kabir Chaturvedi, Zuoming Deng, Valentina Di Francesco, Patrick Dunn, Karen Eilbeck, Carlos Evangelista, Andrei Gabrielian, Weiniu Gan, Wangmao Ge, Fangcheng Gong, Zhiping Gu, Ping Guan, Thomas J. Heiman, Maureen E. Higgins, Rui‐Ru Ji, Zhaoxi Ke, Karen A. Ketchum, Zhongwu Lai, Yiding Lei, Zhenya Li, Jiayin Li, Yong Liang, Xiaoying Lin, Fu Lu, Gennady V. Merkulov, Natalia V. Milshina, Helen M. Moore, Ashwinikumar K. Naik, Vaibhav A. Narayan, Beena Neelam, Deborah Nusskern, Douglas B. Rusch, Steven L. Salzberg, Wei Shao, Bixiong Chris Shue, Jing‐Tao Sun, Zhen Yuan Wang, Aihui Wang, Xin Wang, Jian Wang, Ming-Hui Wei, Ron Wides, Chunlin Xiao, Chunhua Yan and 173 more - Science 2001 cited by 13,699

  2. A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae

    Authors: , , , , , , , , , , , , , , , , , , , - Nature 2000 cited by 4,745

  3. Modeling disease progression via multi-task learning

    Authors: , , , - NeuroImage 2013 cited by 217

  4. Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data

    Authors: , , , , - NeuroImage 2012 cited by 190

  5. Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , - Journal of Medical Internet Research 2020 cited by 201

  6. Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Zulqarnain Rashid, Nick Cummins, Nikolay V. Manyakov, Srinivasan Vairavan, Matthew Hotopf - BMC Psychiatry 2022 cited by 116

  7. Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol

    Authors: , , , , , , , , , , , , , , , , , , , , , , - BMC Psychiatry 2019 cited by 177

  8. Remote monitoring technologies in Alzheimer’s disease: design of the RADAR-AD study

    Authors: , , , , , , , , , , , , , , , , , , , , , , - Alzheimer s Research & Therapy 2021 cited by 67

  9. Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study

    Authors: , , , , , , , , , , , , , , , , , , , , , , - JMIR mhealth and uhealth 2021 cited by 117

  10. Mobile and pervasive computing technologies and the future of Alzheimer's clinical trials

    Authors: , , - npj Digital Medicine, npj Digit. Medicine 2018 cited by 99

  11. Digital endpoints in clinical trials of Alzheimer’s disease and other neurodegenerative diseases: challenges and opportunities

    Authors: , , , , , , , , , , , , , , , , , - Frontiers in Neurology 2023 cited by 31

  12. Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , - npj Digital Medicine, npj Digit. Medicine 2023 cited by 56

  13. Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Richard Dobson - Journal of Medical Internet Research 2023 cited by 42

  14. Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Richard Dobson - JMIR Mental Health 2022 cited by 58

  15. The ADAS‐Cog revisited: Novel composite scales based on ADAS‐Cog to improve efficiency in MCI and early AD trials

    Authors: , , , , , , , , - Alzheimer s & Dementia 2012 cited by 97

  16. The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Richard Dobson, Josep María Haro - Psychological Medicine 2023 cited by 35

  17. The association between persistent cognitive difficulties and depression and functional outcomes in people with major depressive disorder

    Authors: , , , , , , , , , , , , , , , , , , - Psychological Medicine 2022 cited by 32

  18. The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , - Computer Methods and Programs in Biomedicine, Comput. Methods Programs Biomed. 2022 cited by 17

  19. Modeling disease progression via fused sparse group lasso

    Authors: , , , - SIGKDD international conference on Knowledge discovery and data mining 2012 cited by 186

  20. Analysis of 23andMe antidepressant efficacy survey data: implication of circadian rhythm and neuroplasticity in bupropion response

    Authors: , , , , - Translational Psychiatry 2016 cited by 75

  21. Clinical Utility of Wearable Sensors and Patient-Reported Surveys in Patients With Schizophrenia: Noninterventional, Observational Study

    Authors: , , , , - JMIR Mental Health 2021 cited by 21

  22. Personalized relapse prediction in patients with major depressive disorder using digital biomarkers

    Authors: , , , , , , , , , , , , - Scientific Reports 2023 cited by 15

  23. Classifying depression symptom severity: Assessment of speech representations in personalized and generalized machine learning models

    Authors: , , , , , , , , , , , , , , , , - INTERSPEECH 2023 cited by 9

  24. Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data

    Authors: , , , , - SIGKDD international conference on Knowledge discovery and data mining 2012 cited by 52