Open Access
Issue |
Wuhan Univ. J. Nat. Sci.
Volume 28, Number 3, June 2023
|
|
---|---|---|
Page(s) | 257 - 270 | |
DOI | https://doi.org/10.1051/wujns/2023283257 | |
Published online | 13 July 2023 |
- Li F F, Xia H S, Chen Z W. Polypeptide daintain as a new biomarker for detecting breast tumor [J]. Wuhan University Journal of Natural Sciences, 2008, 13(1): 118-122. [CrossRef] [Google Scholar]
- Shull J D, Hadsell D L, et al. Genetic variation in sensitivity to estrogens and breast cancer risk [J]. Mammalian Genome, 2018, 29(1): 24-37. [CrossRef] [PubMed] [Google Scholar]
- Xie G, Liu X, Zhang Y, et al. UTX promotes hormonally responsive breast carcinogenesis through feed-forward transcription regulation with estrogen receptor [J]. Oncogene, 2017, 36(39): 5497-5511. [CrossRef] [PubMed] [Google Scholar]
- Lyndsay V, Rhodes, Zhu Y. Effects of SDF-1-CXCR4 signaling on microRNA expression and tumorigenesis in estrogen receptor-alpha (ER-α)-positive breast cancer cells [J]. Experimental Cell Research, 2011, 317(18): 2573-2581. [CrossRef] [PubMed] [Google Scholar]
- Lambert C T, Lichter J B, Perry A N, et al. Medial amygdala ERα expression influences monogamous behaviour of male prairie voles in the field [J]. Proceedings of the Royal Society B: Biological Sciences, 2021, 288(1956): 20210318. [CrossRef] [PubMed] [Google Scholar]
- Sutherland R, Meeson A, Lowes S. Solute transporters and malignancy: Establishing the role of uptake transporters in breast cancer and breast cancer metastasis [J]. Cancer and Metastasis Reviews, 2020, 39(3): 919-932. [CrossRef] [PubMed] [Google Scholar]
- Chen C, Baumann W T, Xing J H, et al. Mathematical models of the transitions between endocrine therapy responsive and resistant states in breast cancer [J]. Journal of the Royal Society Interface, 2014, 11(96): 20140206. [CrossRef] [PubMed] [Google Scholar]
- Katzer K, Hill J L, McIver K B, et al. Lipedema and the potential role of estrogen in excessive adipose tissue accumulation[J]. International Journal of Molecular Sciences, 2021, 22(21): 11720. [CrossRef] [PubMed] [Google Scholar]
- Shyam Sundar P, Naresh P, Justin A, et al. Dual modulators of p53 and cyclin D in ER alpha signaling by albumin nanovectors bearing zinc chaperones for ER-positive breast cancer therapy[J]. Mini-Reviews in Medicinal Chemistry, 2021, 21(7): 792-802. [CrossRef] [Google Scholar]
- Mutlu Ağardan N B, Değim Z, Ylmaz S, et al. Tamoxifen/raloxifene loaded liposomes for oral treatment of breast cancer[J]. Journal of Drug Delivery Science and Technology, 2020, 57: 101612. [Google Scholar]
- Li J S, Luo D H, Wen T T, et al. Representative feature selection of molecular descriptors in QSAR modeling [J]. Journal of Molecular Structure, 2021, 1244(12): 131249. [NASA ADS] [CrossRef] [Google Scholar]
- Hemmateenejad B, Miri R, Elyasi M. A segmented principal component analysis-regression approach to QSAR study of peptides [J]. Journal of Theoretical Biology, 2012, 305: 37-44. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Martínez M J, Razuc M, Ponzoni I. MoDeSuS: A machine learning tool for selection of molecular descriptors in QSAR studies applied to molecular informatics[J]. BioMed Research International, 2019, 2019: 1-12. [CrossRef] [Google Scholar]
- Krishna J G, Khan K, Roy K. Application of QSARs in identification of mutagenicity mechanisms of nitro and amino aromatic compounds against salmonella typhimurium species [J]. Toxicology in Vitro, 2020, 65: 104768. [CrossRef] [PubMed] [Google Scholar]
- Wang X Z, Perston B, Yang Y, et al. Robust QSAR model development in high-throughput catalyst discovery based on genetic parameter optimisation [J]. Chemical Engineering Research and Design, 2009, 87(10): 1420-1429. [CrossRef] [Google Scholar]
- Yu Q, Deng H F, Yan H, et al. An accurate nonlinear QSAR model for the antitumor activities of chloroethyl nitrosoureas using neural networks [J]. Journal of Molecular Graphics and Modelling, 2011, 29(6): 826-833. [CrossRef] [Google Scholar]
- Hadrup N, Frederiksen M, Wedebye E B, et al. Asthma-inducing potential of 28 substances in spray cleaning products—Assessed by quantitative structure activity relationship (QSAR) testing and literature review [J]. Journal of Applied Toxicology, 2021, 42(3): 130-153. [Google Scholar]
- Ding Q Y, Zu S P, Hou S Y, et al. VISAR: An interactive tool for dissecting chemical features learned by deep neural network QSAR models [J]. Bioinformatics, 2020, 36(11): 3610-3612. [CrossRef] [PubMed] [Google Scholar]
- Chi C T, Lee M H, Weng C F, et al. In silico prediction of PAMPA effective permeability using a two-QSAR approach [J]. International Journal of Molecular Sciences, 2019, 20(13): 3170. [CrossRef] [PubMed] [Google Scholar]
- Wang H, Jiang M Y, Li S J, et al. Design of cinnamaldehyde amino acid Schiff base compounds based on the quantitative structure-activity relationship [J]. Royal Society Open Science, 2017, 4(9): 170516. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Tseng Y J, Hopfinger A J, Esposito E X. The great descriptor melting pot: Mixing descriptors for the common good of QSAR models [J]. Journal of Computer-Aided Molecular Design, 2012, 26(1): 39-43. [Google Scholar]
- Vu O, Mendenhall J, Altarawy D, et al. BCL: Mol2D—A robust atom environment descriptor for QSAR modeling and lead optimization [J]. Journal of Computer-Aided Molecular Design, 2019, 33(5): 477-486. [Google Scholar]
- Williams K, Bilsland E, Sparkes A, et al. Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases [J]. Journal of the Royal Society, Interface, 2015, 12(104): 20141289. [CrossRef] [PubMed] [Google Scholar]
- Dong J, Wang N N, Yao Z J, et al. ADMETlab: A platform for systematic ADMET evaluation based on a comprehensively collected ADMET database [J]. Journal of Cheminformatics, 2018, 10(1): 1-11. [CrossRef] [PubMed] [Google Scholar]
- Pires D E V, Blundell T L, Ascher D B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures [J]. Journal of Medicinal Chemistry, 2015, 58(9): 4066-4072. [CrossRef] [PubMed] [Google Scholar]
- Rogers D, Hahn M. Extended-connectivity fingerprints[J]. Journal of Chemical Information and Modeling, 2010, 50(5): 742-754. [CrossRef] [PubMed] [Google Scholar]
- Teramoto R, Kato T. Transfer learning for cytochrome p450 isozyme selectivity prediction[J]. Journal of Bioinformatics and Computational Biology, 2011, 9(4): 521-540. [CrossRef] [Google Scholar]
- Shi T T, Yang Y W, Huang S H, et al. Molecular image-based convolutional neural network for the prediction of ADMET properties [J]. Chemometrics and Intelligent Laboratory Systems, 2019, 194: 103853. [CrossRef] [Google Scholar]
- Ferreira L L, Andricopulo A D. ADMET modeling approaches in drug discovery [J]. Drug Discovery Today, 2019, 24(5): 1157-1165. [CrossRef] [PubMed] [Google Scholar]
- Jo J, Kwak B, Choi H S, et al. The message passing neural networks for chemical property prediction on SMILES [J]. Methods, 2020, 179: 65-72. [CrossRef] [PubMed] [Google Scholar]
- Sorkun M C, Khetan A, Er S. AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diver se set of compounds [J]. Scientific Data, 2019, 6(1): 143. [Google Scholar]
- Shroff T, Aina K O, Maass C, et al. Studying metabolism with multi-organ chips: New tools for disease modelling, pharmacokinetics and pharmacodynamics [J]. Open Biology, 2022, 12(3): 210333. [CrossRef] [PubMed] [Google Scholar]
- China Post-graduate Mathematical Contest in Modeling. "Huawei Cup" the 18th china post-graduate mathematical contest in modeling [EB/OL]. [2022-10-14]. https://cpipc.acge.org.cn/cw/hp/4. [Google Scholar]
- Xu Q W, Xu K L, Li L, et al. Mine safety assessment based on basic event importance: Grey relational analysis and bowtie model [J]. Royal Society Open Science, 2018, 5(8): 180397. [CrossRef] [PubMed] [Google Scholar]
- Zhang H, Liu Q D, Sun X R, et al. Integrated bioinformatics and machine learning algorithms analyses highlight related pathways and genes associated with Alzheimer's disease [J]. Current Bioinformatics, 2022, 17(3): 284-295. [CrossRef] [Google Scholar]
- Breiman L. Random forests [J]. Machine Learning, 2001, 45(1): 5-32. [NASA ADS] [CrossRef] [Google Scholar]
- Breiman L. Bagging predictors [J]. Machine Learning, 1996, 24(2): 123-140. [Google Scholar]
- Xu Z H. Research on demand forecast of elderly beds based on multiple regression model [J]. Journal of Shenyang University (Social Science), 2022, 24(1): 52-61(Ch). [Google Scholar]
- Zhang C Y, Zhang R R, Dai Z H, et al. Prediction model for the water jet falling point in fire extinguishing based on a GA-BP neural network [J]. PLoS One, 2019, 14(9): 0221729. [Google Scholar]
- Xu B B, Wang T Z, Luo K, et al. A fault diagnosis method based on wavelet singular entropy and SVM for VSC-HVDC converter[J]. Wuhan University Journal of Natural Sciences, 2020, 25(4): 359-368. [Google Scholar]
- Yadav D K, Kumar S, Saloni S, et al. Molecular docking, QSAR and ADMET studies of withanolide analogs against breast cancer[J]. Drug Design, Development and Therapy, 2017, 11: 1859-1870. [CrossRef] [Google Scholar]
- Zhou Y, Li S J. BP neural network modeling with sensitivity analysis on monotonicity based Spearman coefficient [J]. Chemometrics and Intelligent Laboratory Systems, 2020, 200(1): 103977. [CrossRef] [Google Scholar]
- Savari C, Sotudeh-Gharebagh R, Kulah G, et al. Detecting stability of conical spouted beds based on information entropy theory [J]. Powder Technology, 2019, 343: 185-193. [CrossRef] [Google Scholar]
- Das K, Ranjith K G, Madhusudhan R K, et al. Sensitivity and elasticity analysis of novel corona virus transmission model: A mathematical approach [J]. Sensors International, 2021, 2(1): 100088. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.