| A high-bias, low-variance introduction to machine learning for physicists P Mehta, M Bukov, CH Wang, AGR Day, C Richardson, CK Fisher, ... Physics reports 810, 1-124, 2019 | 1167 | 2019 |
| Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression CK Fisher, P Mehta PloS one 9 (7), e102451, 2014 | 338 | 2014 |
| Visualizing spatially correlated dynamics that directs RNA conformational transitions Q Zhang, AC Stelzer, CK Fisher, HM Al-Hashimi Nature 450 (7173), 1263-1267, 2007 | 305 | 2007 |
| Constructing ensembles for intrinsically disordered proteins CK Fisher, CM Stultz Current opinion in structural biology 21 (3), 426-431, 2011 | 302 | 2011 |
| The transition between the niche and neutral regimes in ecology CK Fisher, P Mehta Proceedings of the National Academy of Sciences 111 (36), 13111-13116, 2014 | 193 | 2014 |
| Modeling intrinsically disordered proteins with bayesian statistics CK Fisher, A Huang, CM Stultz Journal of the American Chemical Society 132 (42), 14919-14927, 2010 | 188 | 2010 |
| Machine learning for comprehensive forecasting of Alzheimer’s Disease progression CK Fisher, AM Smith, JR Walsh Scientific reports 9 (1), 13622, 2019 | 172 | 2019 |
| Explaining the structural plasticity of α-synuclein O Ullman, CK Fisher, CM Stultz Journal of the american chemical society 133 (48), 19536-19546, 2011 | 142 | 2011 |
| The dynamic structure of α-synuclein multimers T Gurry, O Ullman, CK Fisher, I Perovic, T Pochapsky, CM Stultz Journal of the American Chemical Society 135 (10), 3865-3872, 2013 | 127 | 2013 |
| Thermodynamics of statistical inference by cells AH Lang, CK Fisher, T Mora, P Mehta Physical review letters 113 (14), 148103, 2014 | 115 | 2014 |
| Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data AM Smith, JR Walsh, J Long, CB Davis, P Henstock, MR Hodge, ... BMC bioinformatics 21, 1-18, 2020 | 77* | 2020 |
| Comparative studies of disordered proteins with similar sequences: application to Aβ40 and Aβ42 CK Fisher, O Ullman, CM Stultz Biophysical journal 104 (7), 1546-1555, 2013 | 43 | 2013 |
| Protein structure along the order–disorder continuum CK Fisher, CM Stultz Journal of the American Chemical Society 133 (26), 10022-10025, 2011 | 38 | 2011 |
| Generating digital twins with multiple sclerosis using probabilistic neural networks JR Walsh, AM Smith, Y Pouliot, D Li-Bland, A Loukianov, CK Fisher arXiv preprint arXiv:2002.02779, 2020 | 33 | 2020 |
| Bayesian feature selection for high-dimensional linear regression via the Ising approximation with applications to genomics CK Fisher, P Mehta Bioinformatics 31 (11), 1754-1761, 2015 | 28* | 2015 |
| 3T MR imaging of the brain MC DeLano, C Fisher Magnetic Resonance Imaging Clinics 14 (1), 77-88, 2006 | 25 | 2006 |
| Ultrahigh resolution characterization of domain motions and correlations by multialignment and multireference residual dipolar coupling NMR CK Fisher, Q Zhang, A Stelzer, HM Al-Hashimi The Journal of Physical Chemistry B 112 (51), 16815-16822, 2008 | 23 | 2008 |
| Analytically tractable model for community ecology with many species B Dickens, CK Fisher, P Mehta Physical Review E 94 (2), 022423, 2016 | 22 | 2016 |
| Efficient construction of disordered protein ensembles in a Bayesian framework with optimal selection of conformations CK Fisher, O Ullman, CM Stultz Biocomputing 2012, 82-93, 2012 | 22 | 2012 |
| Boltzmann encoded adversarial machines CK Fisher, AM Smith, JR Walsh arXiv preprint arXiv:1804.08682, 2018 | 18 | 2018 |
Pankaj MehtaProfessor, Physics, Boston UniversityVerified email at bu.edu
