| Environmental drivers of spatial variation in whole‐tree transpiration in an aspen‐dominated upland‐to‐wetland forest gradient MM Loranty, DS Mackay, BE Ewers, JD Adelman, EL Kruger Water Resources Research 44 (2), 2008 | 82 | 2008 |
| PACE: Probabilistic Assessment for Contributor Estimation—A machine learning-based assessment of the number of contributors in DNA mixtures MA Marciano, JD Adelman Forensic Science International: Genetics 27, 82-91, 2017 | 69 | 2017 |
| Use of temporal patterns in vapor pressure deficit to explain spatial autocorrelation dynamics in tree transpiration JD Adelman, BE Ewers, DS Mackay Tree Physiology 28 (4), 647-658, 2008 | 56 | 2008 |
| A hybrid approach to increase the informedness of CE-based data using locus-specific thresholding and machine learning MA Marciano, VR Williamson, JD Adelman Forensic Science International: Genetics 35, 26-37, 2018 | 19 | 2018 |
| Developmental validation of PACE™: Automated artifact identification and contributor estimation for use with GlobalFiler™ and PowerPlex® fusion 6c generated data MA Marciano, JD Adelman Forensic Science International: Genetics 43, 102140, 2019 | 16 | 2019 |
| Abiotic and biotic controls on local spatial distribution and performance of Boechera stricta KJ Naithani, BE Ewers, JD Adelman, DH Siemens Frontiers in plant science 5, 348, 2014 | 16 | 2014 |
| Automated detection and removal of capillary electrophoresis artifacts due to spectral overlap JD Adelman, A Zhao, DS Eberst, MA Marciano Electrophoresis 40 (14), 1753-1761, 2019 | 11 | 2019 |
| Assessing non-LUS stutter in DNA sequence data O D’Angelo, ACW Vandepoele, J Adelman, MA Marciano Forensic Science International: Genetics 59, 102706, 2022 | 4 | 2022 |
| System and method for inter-species DNA mixture interpretation M Marciano, J Adelman US Patent 10,957,421, 2021 | 3 | 2021 |
| A hybrid machine learning approach to DNA mixture interpretation M Marciano, JD Adelman American Academy of Forensic Sciences Annual Meeting, 2016 | 2 | 2016 |
| Methods and systems for prediction of a DNA profile mixture ratio M Marciano, JD Adelman, LC Haarer US Patent 10,854,316, 2020 | 1 | 2020 |
| Methods and systems for assessing the presence of allelic dropout using machine learning algorithms M Marciano, JD Adelman US Patent App. 16/612,647, 2020 | 1 | 2020 |
| Combinatorics method for probabilistic geolocation WR McKay, JD Adelman US Patent 9,336,490, 2016 | 1 | 2016 |
| Method for pollen-based geolocation JD Adelman | 1 | 2012 |
| Methods and systems for determination of the number of contributors to a DNA mixture M Marciano, JD Adelman US Patent 12,073,923, 2024 | | 2024 |
| Hierarchical optimized detection of relatives M Marciano, JD Adelman US Patent 11,309,062, 2022 | | 2022 |
| Methods and systems for prediction of a dna profile mixture ratio M Marciano, JD Adelman, LC Haarer US Patent App. 17/082,098, 2021 | | 2021 |
| Hierarchical Optimized Detection of Relatives JD Adelman, M Marciano | | 2019 |
| Methods and Systems for Determination of the Number of Contributors to a DNA Mixture J Adelman, M Marciano | | 2018 |
| A Machine Learning Approach to Identify the Number of Contributors in a DNA Profile J Adelman, M Marciano | | 2018 |
Michael MarcianoSyracuse UniversityZweryfikowany adres z syr.edu
