| Year |
Citation |
Score |
| 2024 |
Ascencio-Medina E, He S, Daghighi A, Iduoku K, Casanola-Martin GM, Arrasate S, González-Díaz H, Rasulev B. Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR). Polymers. 16. PMID 39408442 DOI: 10.3390/polym16192731 |
0.597 |
|
| 2024 |
Iduoku K, Ngongang M, Kulathunga J, Daghighi A, Casanola-Martin G, Simsek S, Rasulev B. Phenolic Acid-β-Cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine Learning-Based QSAR Study. Foods (Basel, Switzerland). 13. PMID 38998653 DOI: 10.3390/foods13132147 |
0.602 |
|
| 2024 |
Daghighi A, Casanola-Martin GM, Iduoku K, Kusic H, González-Díaz H, Rasulev B. Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling. Environmental Science & Technology. PMID 38797941 DOI: 10.1021/acs.est.4c01017 |
0.605 |
|
| 2023 |
Sueker M, Daghighi A, Akhbardeh A, MacKinnon N, Bearman G, Baek I, Hwang C, Qin J, Tabb AM, Roungchun JB, Hellberg RS, Vasefi F, Kim M, Tavakolian K, Kashani Zadeh H. A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy. Sensors (Basel, Switzerland). 23. PMID 38005450 DOI: 10.3390/s23229062 |
0.335 |
|
| 2022 |
Daghighi A, Casanola-Martin GM, Timmerman T, Milenković D, Lučić B, Rasulev B. In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach. Toxics. 10. PMID 36548579 DOI: 10.3390/toxics10120746 |
0.742 |
|
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