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Daniel P. Russo
Daniel P. Russo
Post-Doctoral Researcher, Rutgers University
Verified email at scarletmail.rutgers.edu - Homepage
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Cited by
Cited by
Year
Exploiting machine learning for end-to-end drug discovery and development
S Ekins, AC Puhl, KM Zorn, TR Lane, DP Russo, JJ Klein, AJ Hickey, ...
Nature materials 18 (5), 435-441, 2019
4262019
Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets
A Korotcov, V Tkachenko, DP Russo, S Ekins
Molecular pharmaceutics 14 (12), 4462-4475, 2017
3272017
T4 report: Toward good read-across practice (GRAP) guidance
N Ball, MTD Cronin, J Shen, K Blackburn, ED Booth, M Bouhifd, E Donley, ...
Altex 33 (2), 149, 2016
1882016
Comparing multiple machine learning algorithms and metrics for estrogen receptor binding prediction
DP Russo, KM Zorn, AM Clark, H Zhu, S Ekins
Molecular pharmaceutics 15 (10), 4361-4370, 2018
1382018
Analysis of Draize eye irritation testing and its prediction by mining publicly available 2008–2014 REACH data
T Luechtefeld, A Maertens, DP Russo, C Rovida, H Zhu, T Hartung
Altex 33 (2), 123, 2016
1062016
Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery
T Lane, DP Russo, KM Zorn, AM Clark, A Korotcov, V Tkachenko, ...
Molecular pharmaceutics 15 (10), 4346-4360, 2018
952018
Predicting nano–bio interactions by integrating nanoparticle libraries and quantitative nanostructure activity relationship modeling
W Wang, A Sedykh, H Sun, L Zhao, DP Russo, H Zhou, B Yan, H Zhu
ACS nano 11 (12), 12641-12649, 2017
832017
CATMoS: collaborative acute toxicity modeling suite
K Mansouri, AL Karmaus, J Fitzpatrick, G Patlewicz, P Pradeep, D Alberga, ...
Environmental health perspectives 129 (4), 047013, 2021
792021
Nonanimal models for acute toxicity evaluations: Applying data-driven profiling and read-across
DP Russo, J Strickland, AL Karmaus, W Wang, S Shende, T Hartung, ...
Environmental health perspectives 127 (4), 047001, 2019
702019
Global analysis of publicly available safety data for 9,801 substances registered under REACH from 2008–2014
T Luechtefeld, A Maertens, DP Russo, C Rovida, H Zhu, T Hartung
Altex 33 (2), 95, 2016
692016
Analysis of publically available skin sensitization data from REACH registrations 2008–2014
T Luechtefeld, A Maertens, DP Russo, C Rovida, H Zhu, T Hartung
Altex 33 (2), 135, 2016
642016
Abnormal functional relationship of sensorimotor network with neurotransmitter-related nuclei via subcortical-cortical loops in manic and depressive phases of bipolar disorder
M Martino, P Magioncalda, B Conio, L Capobianco, D Russo, ...
Schizophrenia Bulletin 46 (1), 163-174, 2020
592020
Multiple machine learning comparisons of HIV cell-based and reverse transcriptase data sets
KM Zorn, TR Lane, DP Russo, AM Clark, V Makarov, S Ekins
Molecular pharmaceutics 16 (4), 1620-1632, 2019
522019
Analysis of public oral toxicity data from REACH registrations 2008–2014
T Luechtefeld, A Maertens, DP Russo, C Rovida, H Zhu, T Hartung
Altex 33 (2), 111, 2016
492016
Opposing changes in the functional architecture of large-scale networks in bipolar mania and depression
D Russo, M Martino, P Magioncalda, M Inglese, M Amore, G Northoff
Schizophrenia bulletin 46 (4), 971-980, 2020
422020
White matter microstructure alterations correlate with terminally differentiated CD8+ effector T cell depletion in the peripheral blood in mania: combined DTI and immunological …
P Magioncalda, M Martino, S Tardito, B Sterlini, B Conio, V Marozzi, ...
Brain, Behavior, and Immunity 73, 192-204, 2018
412018
Prediction of Nano–Bio Interactions through Convolutional Neural Network Analysis of Nanostructure Images
X Yan, J Zhang, DP Russo, H Zhu, B Yan
ACS Sustainable Chemistry & Engineering 8 (51), 19096-19104, 2020
342020
CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data
DP Russo, MT Kim, W Wang, D Pinolini, S Shende, J Strickland, ...
Bioinformatics 33 (3), 464-466, 2017
332017
Revealing adverse outcome pathways from public high-throughput screening data to evaluate new toxicants by a knowledge-based deep neural network approach
HL Ciallella, DP Russo, LM Aleksunes, FA Grimm, H Zhu
Environmental science & technology 55 (15), 10875-10887, 2021
322021
Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine-and deep-learning approaches
HL Ciallella, DP Russo, LM Aleksunes, FA Grimm, H Zhu
Laboratory investigation 101 (4), 490-502, 2021
312021
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