@article{Zhuang201220,
title = {Multi-view learning via probabilistic latent semantic analysis},
journal = {Information Sciences},
volume = {199},
number = {0},
pages = {20 - 30},
year = {2012},
note = {},
issn = {0020-0255},
doi = {10.1016/j.ins.2012.02.058},
url = {http://www.sciencedirect.com/science/article/pii/S0020025512001788},
author = {Fuzhen Zhuang and George Karypis and \textbf{X. Ning} and Qing He and Zhongzhi Shi},
keywords = {Multi-view learning},
keywords = {Generative model},
keywords = {Probabilistic Latent Semantic Analysis (PLSA)}
}
@article{Ning2011b,
author = {\textbf{X. Ning} and Michael Walters and Karypis, George},
title = {Improved Machine Learning Models for Predicting Selective Compounds},
journal = {Journal of Chemical Information and Modelling},
abstract = {The identification of small potent compounds that selectively bind
to the target under consideration with high affinities is a critical
step towards successful drug discovery. However, there still lacks
efficient and accurate computational methods to predict compound
selectivity properties. In this paper, we propose a set of machine
learning methods to do compound selectivity prediction. In particular,
we propose a novel cascaded learning method and a multi-task learning
method. The cascaded method decomposes the selectivity prediction
into two steps, one model for each step, so as to effectively filter
out non-selective compounds. The multi-task method incorporates both
activity and selectivity models into one multi-task model so as to
better differentiate compound selectivity properties. We conducted
a comprehensive set of experiments and compared the results with
other conventional selectivity prediction methods, and our results
demonstrated that the cascaded and multi-task methods significantly
improve the selectivity prediction performance.},
file = {:/home/grad00/xning/dminers16/docs/personal/Jobs/Homepage/bluebusiness/papers/Ning2011b.pdf:PDF},
owner = {xia},
dio = {10.1021/ci200346b},
url = {http://pubs.acs.org/doi/abs/10.1021/ci200346b},
timestamp = {2011.03.27},
volume = {52},
number = {1},
pages = {38-50},
year = {2012},
eprint = {http://pubs.acs.org/doi/pdf/10.1021/ci200346b},
note = {impact factor: 3.882}
}
@article{Ning2010a,
author = {\textbf{X. Ning} and Karypis, George},
title = {In silico structure-activity-relationship (SAR) models from machine
learning: a review},
journal = {Drug Development Research},
year = {2010},
volume = {72},
note = {impact factor: 1.109},
abstract = {In this article, we review the recent development for in silico Structure-Activity-Relationship
(SAR) models using machine-learning techniques. The review focuses
on the following topics: machine-learning algorithms for computational
SAR models, single-target-oriented SAR methodologies, Chemogenomics,
and future trends. We try to provide the state-of-the-art SAR methods
as well as the most up-to-date advancement, in order for the researchers
to have a general overview at this area.},
doi = {10.1002/ddr.20410},
file = {:/home/grad00/xning/dminers16/docs/personal/Jobs/Homepage/bluebusiness/papers/Ning2010a.pdf:PDF},
issn = {1098-2299},
keywords = {structure-activity-relationship (SAR), machine learning, chemogenomics},
publisher = {Wiley Subscription Services, Inc., A Wiley Company},
url = {http://dx.doi.org/10.1002/ddr.20410}
}
@article{Ning2009,
author = {\textbf{X. Ning} and Rangwala, Huzefa and Karypis, George},
title = {Multi-Assay-Based Structure-Activity-Relationship Models: Improving
Structure-Activity-Relationship Models by Incorporating Activity
Information from Related Targets},
journal = {Journal of Chemical Information and Modeling},
year = {2009},
volume = {49},
pages = {2444-2456},
number = {11},
note = {PMID: 19842624, impact factor: 3.882},
abstract = {Structure-activity relationship (SAR) models are used to inform and
to guide the iterative optimization of chemical leads, and they play
a fundamental role in modern drug discovery. In this paper, we present
a new class of methods for building SAR models, referred to as multi-assay
based, that utilize activity information from different targets.
These methods first identify a set of targets that are related to
the target under consideration, and then they employ various machine
learning techniques that utilize activity information from these
targets in order to build the desired SAR model. We developed different
methods for identifying the set of related targets, which take into
account the primary sequence of the targets or the structure of their
ligands, and we also developed different machine learning techniques
that were derived by using principles of semi-supervised learning,
multi-task learning, and classifier ensembles. The comprehensive
evaluation of these methods shows that they lead to considerable
improvements over the standard SAR models that are based only on
the ligands of the target under consideration. On a set of 117 protein
targets, obtained from PubChem, these multi-assay-based methods achieve
a receiver-operating characteristic score that is, on the average,
7.0 -7.2% higher than that achieved by the standard SAR models. Moreover,
on a set of targets belonging to six protein families, the multi-assay-based
methods outperform chemogenomics based approaches by 4.33%.},
doi = {10.1021/ci900182q},
eprint = {http://pubs.acs.org/doi/pdf/10.1021/ci900182q},
file = {:/home/grad00/xning/dminers16/docs/personal/Jobs/Homepage/bluebusiness/papers/Ning2009.pdf:PDF},
url = {http://pubs.acs.org/doi/abs/10.1021/ci900182q}
}
This file was generated by bibtex2html 1.96.