Statistical learning approaches for predicting pharmacological properties of pharmaceutical agents

Statistical learning approaches for predicting pharmacological properties of pharmaceutical agents

Ph.D

Saved in:

Bibliographic Details
Main Author:	LI HU
Other Authors:	PHARMACY
Format:	Theses and Dissertations
Language:	English
Published:	2010
Subjects:	Statistical Learning Methods, Support Vector Machine, Pharmacological property prediction, Computer aided drug design, Drug discovery
Online Access:	http://scholarbank.nus.edu.sg/handle/10635/13435
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	National University of Singapore
Language:	English

Similar Items

Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries
by: Kumar, P., et al.
Published: (2014)

Protein function and inhibitor prediction by statistical learning approach
by: HAN LIANYI
Published: (2010)

Does drug-target have a likeness?
by: Xu, H., et al.
Published: (2014)

Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties
by: Cui, J., et al.
Published: (2014)

Application of support vector machines to in silico prediction of cytochrome P450 enzyme substrates and inhibitors
by: Yap, C.W., et al.
Published: (2014)

Methods to Improve Virtual Screening of Potential Drug Leads for Specific Pharmacodynamic and Toxicological Properties
by: LIEW CHIN YEE
Published: (2012)

Learning the drug target-likeness of a protein
by: Xu, H., et al.
Published: (2014)

A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor
by: Han, L.Y., et al.
Published: (2014)

Database development and machine learning prediction of pharmaceutical agents
by: LIU XIANGHUI
Published: (2010)

NEW METHODS FOR OMICS AND PHARMACEUTICAL DEEP LEARNING
by: SHEN WANXIANG
Published: (2022)

Computational study of therapeutic targets and ADME-Associated proteins and application in drug design
by: ZHENG CHANJUAN
Published: (2014)

Identifying novel type ZBGs and nonhydroxamate HDAC inhibitors through a SVM based virtual screening approach
by: Liu, X.H., et al.
Published: (2014)

Prediction of pathogenic and antigenic proteins and peptides by machine learning approach
by: CUI JUAN
Published: (2019)

Knowledge discovery in biomedical research and drug design : The development and application of biological databases
by: JI ZHILIANG
Published: (2010)

Privacy-preserving outsourced support vector machine design for secure drug discovery
by: LIU, Ximeng, et al.
Published: (2020)

Advances in exploration of machine learning methods for predicting functional class and interaction profiles of proteins and peptides irrespective of sequence homology
by: Cui, J., et al.
Published: (2014)

Machine learning-based prediction of survival prognosis in cervical cancer
by: Ding, Dongyan, et al.
Published: (2022)

Identification of small molecule aggregators from large compound libraries by support vector machines
by: Rao, H., et al.
Published: (2014)

Medicinal plants of the Mien (Yao) in Northern Thailand and their potential value in the primary healthcare of postpartum women
by: Panyaphu,K., et al.
Published: (2015)

In vitro anti-proliferative activity on colon cancer cell line (HT-29) of Thai medicinal plants selected from Thai/Lanna medicinal plant recipe database "MANOSROI III"
by: Manosroi,A., et al.
Published: (2015)

Investigation of anti-inflammatory, antinociceptive and antipyretic activities of Stahlianthus involucratus rhizome ethanol extract
by: Pingsusaen,P., et al.
Published: (2015)

Acidophilic actinomycetes from rhizosphere soil: diversity and properties beneficial to plants
by: Poomthongdee,N., et al.
Published: (2015)

Antibacterial dihydrobenzopyran and xanthone derivatives from Garcinia cowa stem barks
by: Siridechakorn I., et al.
Published: (2015)

Neolignans from leaves of Miliusa mollis
by: Sawasdee,K., et al.
Published: (2015)

Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries
by: Han, B., et al.
Published: (2014)

Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods
by: Li, H., et al.
Published: (2014)

Combinatorial support vector machines approach for virtual screening of selective multi-target serotonin reuptake inhibitors from large compound libraries
by: Shi, Z., et al.
Published: (2014)

Prediction of relatedness in stack overflow: Deep learning vs. SVM: A reproducibility study
by: XU, Bowen, et al.
Published: (2018)

Mixed learning algorithms and features ensemble in hepatotoxicity prediction
by: Liew, C.Y., et al.
Published: (2014)

Development of Virtual Screening and In Silico Biomarker Identification Model for Pharmaceutical Agents
by: ZHANG JINGXIAN
Published: (2012)

Computer aided drug design: Drug target directed in silico approaches
by: CHEN XIN
Published: (2010)

Model assessment through renormalization group in statistical learning
by: Wang, Q.-G., et al.
Published: (2014)

Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic, or toxicological properties of pharmaceutical agents
by: Li, H., et al.
Published: (2014)

Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries
by: Ma, X.H., et al.
Published: (2011)

Virtual screening of selective multitarget kinase inhibitors by combinatorial support vector machines
by: Ma, X.H., et al.
Published: (2014)

Inhibitor prediction by machine learning approaches
by: YAO LIXIA
Published: (2010)

High performance computational virtual screening tools: development and application to the discovery of kinase inhibitors
by: MA XIAOHUA
Published: (2011)

Splice Site Prediction Using Support Vector Machines with a Bayes Kernel
by: ZHANG, Ya, et al.
Published: (2006)

Current trends in modern pharmaceutical analysis for drug discovery
by: Koh, H.-L., et al.
Published: (2014)

Trends in the exploration of therapeutic targets for the treatment of endocrine, metabolic and immune disorders
by: Chen, X., et al.
Published: (2014)