Citation: | HU Xiangang, LI Jiawei, LI Jiaqiang, JIN Hongye, YU Fubo. SCIENTIFIC QUESTIONS ON THE BIOLOGICAL EFFECTS OF NANOMATERIALS BASED ON MACHINE LEARNING[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 171-181. doi: 10.13205/j.hjgc.202206022 |
[1] |
WINKLER D A, BURDEN F R, YAN B, et al. Modelling and predicting the biological effects of nanomaterials[J]. Sar and Qsar in Environmental Research, 2014, 25(1/2/3):161-172.
|
[2] |
WINKLER D A. Role of artificial intelligence and machine learning in nanosafety[J]. Small, 2020, 16(36).
|
[3] |
谢翼飞,李旭东,李福德.生物硫铁纳米材料特性分析及其处理高浓度含铬废水研究[J].环境科学, 2009, 30(4):1060-1065.
|
[4] |
XU P, ZENG G M, HUANG D L, et al. Use of iron oxide nanomaterials in wastewater treatment:a review[J]. Science of the Total Environment, 2012, 424:1-10.
|
[5] |
MAUTER M S, ZUCKER I, PERREAULT F, et al. The role of nanotechnology in tackling global water challenges[J]. Nature Sustainability, 2018, 1(4):166-175.
|
[6] |
HOU X, MU L, CHEN F, et al. Emerging investigator series:design of hydrogel nanocomposites for the detection and removal of pollutants:from nanosheets, network structures, and biocompatibility to machine-learning-assisted design[J]. Environmental Science:Nano, 2018, 5(10):2216-2240.
|
[7] |
张业.纳米材料的电化学制备及其应用[J].环境工程, 2011, 29(3):128-130
,90.
|
[8] |
LIU S, MA C, MA M G, et al. Recent advances in carbon nanomaterials derived from biomass[J]. Science of Advanced Materials, 2019, 11(1):5-17.
|
[9] |
KIM S, KIM K H, BARK C W. Two-dimensional nanomaterials:their structures, synthesis, and applications[J]. Science of Advanced Materials, 2017, 9(9):1441-1457.
|
[10] |
FAN Z X, HUANG X, CHEN Y, et al. Facile synthesis of gold nanomaterials with unusual crystal structures[J]. Nature Protocols, 2017, 12(11):2367-2376.
|
[11] |
韩伟,于艳军,李宁涛,等.纳米复合材料在食品包装中的应用及其安全评价[J].科学通报, 2011, 56(3):198-209.
|
[12] |
郑明彬,赵鹏飞,罗震宇,等.纳米技术在癌症诊疗一体化中的应用[J].科学通报, 2014, 59(31):3009-3024.
|
[13] |
POON W, KINGSTON B R, OUYANG B, et al. A framework for designing delivery systems[J]. Nature Nanotechnology, 2020, 15(10):819-829.
|
[14] |
BENFENATI F, LANZANI G. Clinical translation of nanoparticles for neural stimulation[J]. Nature Reviews Materials, 2021, 6(1):1-4.
|
[15] |
闻雷,宋仁升,石颖,等.炭材料在锂离子电池中的应用及前景[J].科学通报, 2013, 58(31):3157-3171.
|
[16] |
KAH M, TUFENKJI N, WHITE J C. Nano-enabled strategies to enhance crop nutrition and protection[J]. Nature Nanotechnology, 2019, 14(6):532-540.
|
[17] |
GILBERTSON L M, POURZAHEDI L, LAUGHTON S, et al. Guiding the design space for nanotechnology to advance sustainable crop production[J]. Nature Nanotechnology, 2020, 15(9):801-,.
|
[18] |
YANAMALA N, ORANDLE M S, KODALI V K, et al. Supervised machine learning approaches predict and characterize nanomaterial exposures:MWCNT markers in lung lavage fluid[C]//Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics (ACM-BCB), Boston, MA, F 2017.
|
[19] |
张学治,孙红文,张稚妍.鲤鱼对纳米二氧化钛的生物富集[J].环境科学, 2006,27(8):1631-1635.
|
[20] |
YU F B, WEI C H, DENG P, et al. Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles[J]. Science Advances, 2021, 7(22):14.
|
[21] |
DEV A, SRIVASTAVA A K, KARMAKAR S. Nanomaterial toxicity for plants[J]. Environmental Chemistry Letters, 2018, 16(1):85-100.
|
[22] |
LUAN F, TANG L L, ZHANG L H, et al. A further development of the QNAR model to predict the cellular uptake of nanoparticles by pancreatic cancer cells[J]. Food and Chemical Toxicology, 2018, 112:571-580.
|
[23] |
BAN Z, YUAN P, YU F B, et al. Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles[J]. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(19):10492-10499.
|
[24] |
YU H J, ZHAO Z L, CHENG F. Predicting and investigating cytotoxicity of nanoparticles by translucent machine learning[J]. Chemosphere, 2021, 276:130164.
|
[25] |
FURXHI I, MURPHY F, MULLINS M, et al. Nanotoxicology data for in silico tools:a literature review[J]. Nanotoxicology, 2020, 14(5):612-637.
|
[26] |
WANG M, WANG T, CAI P Q, et al. Nanomaterials discovery and design through machine learning[J]. Small Methods, 2019, 3(5).
|
[27] |
TO K T, TRUONG L, EDWARDS S, et al. Multivariate modeling of engineered nanomaterial features associated with developmental toxicity[J]. Nanoimpact, 2019, 16:100185.
|
[28] |
PENG T, WEI C H, YU F B, et al. Predicting nanotoxicity by an integrated machine learning and metabolomics approach[J]. Environmental Pollution, 2020, 267:115434.
|
[29] |
MARCHWIANY M E, BIROWSKA M, POPIELSKI M, et al. Surface-related features responsible for cytotoxic behavior of mXenes layered materials predicted with machine learning approach[J]. Materials, 2020, 13(14).
|
[30] |
KOTZABASAKI M I, SOTIROPOULOS I, SARIMVEIS H. QSAR modeling of the toxicity classification of superparamagnetic iron oxide nanoparticles (SPIONs) in stem-cell monitoring applications:an integrated study from data curation to model development[J]. RSC Advances, 2020, 10(9):5385-5391.
|
[31] |
HAN Z W, SALAWU O A, ZENOBIO J E, et al. Emerging investigator series:immobilization of arsenic in soil by nanoscale zerovalent iron:role of sulfidation and application of machine learning[J]. Environmental Science-Nano, 2021, 8(3):619-633.
|
[32] |
GUL G, YILDIRIM R, ILERI-ERCAN N. Cytotoxicity analysis of nanoparticles by association rule mining[J]. Environmental Science:Nano, 2021, 8(4):937-949.
|
[33] |
VARSOU D D, ELLIS L J A, AFANTITIS A, et al. Ecotoxicological read-across models for predicting acute toxicity of freshly dispersed versus medium-aged NMs to Daphnia magna[J]. Chemosphere, 2021, 285:131452.
|
[34] |
SUBRAMANIAN N A, PALANIAPPAN A. NanoTox:development of a parsimonious in silico model for toxicity assessment of metal-oxide nanoparticles using physicochemical features[J]. ACS Omega, 2021, 6(17):11729-11739.
|
[35] |
SIZOCHENKO N, SYZOCHENKO M, FJODOROVA N, et al. Evaluating genotoxicity of metal oxide nanoparticles:application of advanced supervised and unsupervised machine learning techniques[J]. Ecotoxicology and Environmental Safety, 2019, 185:109733.
|
[36] |
SIZOCHENKO N, MIKOLAJCZYK A, JAGIELLO K, et al. How the toxicity of nanomaterials towards different species could be simultaneously evaluated:a novel multi-nano-read-across approach[J]. Nanoscale, 2018, 10(2):582-591.
|
[37] |
SINGH A V, MAHARJAN R-S, KANASE A, et al. Machine-learning-based approach to decode the influence of nanomaterial properties on their interaction with cells[J]. Acs Applied Materials&Interfaces, 2021, 13(1):1943-1955.
|
[38] |
PAPADIAMANTIS A G, JANES J, VOYIATZIS E, et al. Predicting cytotoxicity of metal oxide nanoparticles using isalos analytics platform[J]. Nanomaterials, 2020, 10(10):2017.
|
[39] |
KOTZABASAKI M, SOTIROPOULOS I, CHARITIDIS C, et al. Machine learning methods for multi-walled carbon nanotubes (MWCNT) genotoxicity prediction[J]. Nanoscale Advances, 2021, 3(11):3167-3176.
|
[40] |
KAR S, PATHAKOTI K, TCHOUNWOU P B, et al. Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli:mechanistic understanding through In Vitro and In Silico studies[J]. Chemosphere, 2021, 264:128428.
|
[41] |
HALDER A K, MELO A, CORDEIRO M N D S. A unified in silico model based on perturbation theory for assessing the genotoxicity of metal oxide nanoparticles[J]. Chemosphere, 2020, 244:125489.
|
[42] |
FURXHI I, MURPHY F, MULLINS M, et al. Machine learning prediction of nanoparticle in vitro toxicity:a comparative study of classifiers and ensemble-classifiers using the Copeland Index[J]. Toxicology Letters, 2019, 312:157-166.
|
[43] |
CHOI J S, TRINH T X, YOON T H, et al. Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials[J]. Chemosphere, 2019, 217:243-249.
|
[44] |
CHAU Y T, YAP C W. Quantitative Nanostructure-Activity Relationship modelling of nanoparticles[J]. Rsc Advances, 2012, 2(22):8489-8496.
|
[45] |
CONCU R, KLEANDROVA V V, SPECK-PLANCHE A, et al. Probing the toxicity of nanoparticles:a unified in silico machine learning model based on perturbation theory[J]. Nanotoxicology, 2017, 11(7):891-906.
|
[46] |
FINDLAY M R, FREITAS D N, MOBED-MIREMADI M, et al. Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties[J]. Environmental Science:Nano, 2018, 5(1):64-71.
|
[47] |
LIM G P, SOON C F, MA N L, et al. Cytotoxicity of MXene-based nanomaterials for biomedical applications:a mini review[J]. Environmental Research, 2021, 201:111592.
|
[48] |
SIMEONE F C, COSTA A L. Assessment of cytotoxicity of metal oxide nanoparticles on the basis of fundamental physical-chemical parameters:a robust approach to grouping[J]. Environmental Science:Nano, 2019, 6(10):3102-3112.
|
[49] |
CHEN G C, VIJVER M G, XIAO Y L, et al. A review of recent advances towards the development of (quantitative) structure-activity relationships for metallic nanomaterials[J]. Materials, 2017, 10(9):1013.
|
[50] |
SASON H, SHAMAY Y. Nanoinformatics in drug delivery[J]. Israel Journal of Chemistry, 2020, 60(12):1108-1117.
|
[51] |
MA Y, WANG J L, WU J Y, et al. Meta-analysis of cellular toxicity for graphene via data-mining the literature and machine learning[J]. Science of the Total Environment, 2021, 793:148532.
|
[52] |
HAYAT H, NUKALA A, NYAMIRA A, et al. A concise review:the synergy between artificial intelligence and biomedical nanomaterials that empowers nanomedicine[J]. Biomedical Materials, 2021, 16(5).
|
[53] |
SHEEHAN B, MURPHY F, MULLINS M, et al. Hazard screening methods for nanomaterials:a comparative study[J]. International Journal of Molecular Sciences, 2018, 19(3):649.
|
[54] |
KARATZAS P, MELAGRAKI G, ELLIS L J A, et al. Development of deep learning models for predicting the effects of exposure to engineered nanomaterials on Daphnia magna[J]. Small, 2020, 16(36).
|
[55] |
GOMES S I L, AMORIM M J B, POKHREL S, et al. Machine learning and materials modelling interpretation of in vivo toxicological response to TiO2 nanoparticles library (UV and non-UV exposure)[J]. Nanoscale, 2021, 13(35):14666-14678.
|
[56] |
GOUSIADOU C, ROBINSON R L M, KOTZABASAKI M, et al. Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish[J]. Nanotoxicology, 2021, 15(4):446-476.
|
[57] |
BAN Z, ZHOU Q X, SUN A Q, et al. Screening priority factors determining and predicting the reproductive toxicity of various nanoparticles[J]. Environmental Science&Technology, 2018, 52(17):9666-9676.
|
[58] |
YANAMALA N, DESAI I C, MILLER W, et al. Grouping of carbonaceous nanomaterials based on association of patterns of inflammatory markers in BAL fluid with adverse outcomes in lungs[J]. Nanotoxicology, 2019, 13(8):1102-1016.
|
[59] |
PAPA E, DOUCET J P, SANGION A, et al. Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches[J]. Sar and Qsar in Environmental Research, 2016, 27(7):521-538.
|
[60] |
HELMA C, RAUTENBERG M, GEBELE D. Nano-Lazar:read across predictions for nanoparticle toxicities with calculated and measured properties[J]. Frontiers in Pharmacology, 2017, 8.
|
[61] |
WEI L F, ZHANG Q, HOU X W, et al. Species-dependent eco-corona dictates the aggregation of black phosphorus nanosheets:the role of protein and calcium[J]. Environmental Science:Nano, 2021, 8(11):3098-30109.
|
[62] |
CHAKRABORTY D, ETHIRAJ K R, CHANDRASEKARAN N, et al. Mitigating the toxic effects of CdSe quantum dots towards freshwater alga Scenedesmus obliquus:role of eco-corona[J]. Environmental Pollution, 2021, 270:116049.
|
[63] |
FADARE O O, WAN B, LIU K Y, et al. Eco-corona vs protein corona:effects of humic substances on corona formation and nanoplastic particle toxicity in Daphnia magna[J]. Environmental Science&Technology, 2020, 54(13):8001-8019.
|
[64] |
JIA Y Y, HOU X, WANG Z W, et al. Machine learning boosts the design and discovery of nanomaterials[J]. ACS Sustainable Chemistry&Engineering, 2021, 9(18):6130-6147.
|
[65] |
TAO H C, WU T Y, ALDEGHI M, et al. Nanoparticle synthesis assisted by machine learning[J]. Nature Reviews Materials, 2021, 6(8):701-716.
|
[66] |
KARAKUS C O, WINKLER D A. Overcoming roadblocks in computational roadmaps to the future for safe nanotechnology[J]. Nano Futures, 2021, 5(2).
|
[67] |
LI Y, PU Q, LI S, et al. Machine learning methods for research highlight prediction in biomedical effects of nanomaterial application[J]. Pattern Recognition Letters, 2019, 117:111-118.
|
[68] |
BASEI G, HRISTOZOV D, LAMON L, et al. Making use of available and emerging data to predict the hazards of engineered nanomaterials by means of in silico tools:a critical review[J]. Nanoimpact, 2019, 13:76-99.
|
[69] |
YAN X, ZHANG J, RUSSO D P, et al. Prediction of nano-bio interactions through convolutional neural network analysis of nanostructure images[J]. ACS Sustainable Chemistry&Engineering, 2020, 8(51):19096-19104.
|
[70] |
TO K T, TRUONG L, EDWARDS S, et al. Multivariate modeling of engineered nanomaterial features associated with developmental toxicity (vol 16C, 100185, 2019)[J]. Nanoimpact, 2021, 21.
|
[71] |
YAN X L, SEDYKH A, WANG W Y, et al. Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations[J]. Nature Communications, 2020, 11(1):2519.
|
[72] |
SUN S, DENG P, MU L, et al. Bionanoscale recognition underlies cell fate and therapy[J]. Advanced Healthcare Materials, 2021, 10(22).
|