Narayan, Vaibhav A.

The sequence of the human genome

Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A., et al. (2001) · Science

Genomics and Phylogenetic StudiesGenomic variations and chromosomal abnormalities

Cited by 13,909

A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae

Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., et al. (2000) · Nature

Bioinformatics and Genomic NetworksFungal and yeast genetics research

Cited by 4,706

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

Sun, S., Folarin, A. A., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., Cummins, N., Matcham, F., Dalla Costa, G., Simblett, S., et al. (2020) · Journal of Medical Internet Research

Human Mobility and Location-Based AnalysisCOVID-19 Digital Contact Tracing

Cited by 256

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

Matcham, F., Leightley, D., Siddi, S., Lamers, F., White, K. M., Annas, P., de Girolamo, G., Difrancesco, S., Haro, J. M., Horsfall, M., et al. (2022) · BMC Psychiatry

Digital Mental Health InterventionsMental Health Research Topics

Cited by 110

Mobile and pervasive computing technologies and the future of Alzheimer’s clinical trials

Doraiswamy, P. M., Narayan, V. A., & Manji, H. K. (2018) · npj Digital Medicine

Mobile Health and mHealth ApplicationsDementia and Cognitive Impairment Research

Cited by 97

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

Raghavan, N., Samtani, M. N., Farnum, M., Yang, E., Novak, G., Grundman, M., Narayan, V., DiBernardo, A., & Initiative, A. D. N. (2013) · Alzheimer's & Dementia

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 96

Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study

Zhang, Y., Folarin, A. A., Sun, S., Cummins, N., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., Laiou, P., Matcham, F., et al. (2021) · JMIR mHealth and uHealth

Mental Health Research TopicsDigital Mental Health Interventions

Cited by 68

Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study

Nie, Z., Vairavan, S., Narayan, V. A., Ye, J., & Li, Q. S. (2018) · PLOS ONE

Treatment of Major DepressionArtificial Intelligence in Healthcare

Cited by 62

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

Siddi, S., Bailon, R., Giné-Vázquez, I., Matcham, F., Lamers, F., Kontaxis, S., Laporta, E., Garcia, E., Lombardini, F., Annas, P., et al. (2023) · Psychological Medicine

Heart Rate Variability and Autonomic ControlMental Health Research Topics

Cited by 31

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

Vairavan, S., Rashidisabet, H., Li, Q. S., Ness, S., Morrison, R. L., Soares, C. N., Uher, R., Frey, B. N., Lam, R. W., Kennedy, S. H., et al. (2023) · Scientific Reports

Mental Health Research TopicsContext-Aware Activity Recognition Systems

Cited by 14

A Framework for Recruiting into a Remote Measurement Technologies (RMTs) study: Experiences from a major depressive disorder cohort

Oetzmann, C., White, K. M., Ivan, A., Julie, J., Leightley, D., Lavelle, G., Lamers, F., Siddi, S., Annas, P., Garcia, S. A., et al. (2021)

Digital Mental Health InterventionsMobile Health and mHealth Applications

Cited by 5