Hong Fang

Publications (95) View all

• Article: Meta-analysis of pulsed-field gel electrophoresis fingerprints based on a constructed salmonella database.

  Wen Zou, Hung-Chia Chen, Kelley B Hise, Hailin Tang, Steven L Foley, Joe Meehan, Wei-Jiun Lin, Rajesh Nayak, Joshua Xu, Hong Fang, James J Chen
  [show abstract] [hide abstract]
  ABSTRACT: A database was constructed consisting of 45,923 Salmonella pulsed-field gel electrophoresis (PFGE) patterns. The patterns, randomly selected from all submissions to CDC PulseNet during 2005 to 2010, included the 20 most frequent serotypes and 12 less frequent serotypes. Meta-analysis was applied to all of the PFGE patterns in the database. In the range of 20 to 1100 kb, serotype Enteritidis averaged the fewest bands at 12 bands and Paratyphi A the most with 19, with most serotypes in the 13-15 range among the 32 serptypes. The 10 most frequent bands for each of the 32 serotypes were sorted and distinguished, and the results were in concordance with those from distance matrix and two-way hierarchical cluster analyses of the patterns in the database. The hierarchical cluster analysis divided the 32 serotypes into three major groups according to dissimilarity measures, and revealed for the first time the similarities among the PFGE patterns of serotype Saintpaul to serotypes Typhimurium, Typhimurium var. 5-, and I 4,[5],12:i:-; of serotype Hadar to serotype Infantis; and of serotype Muenchen to serotype Newport. The results of the meta-analysis indicated that the pattern similarities/dissimilarities determined the serotype discrimination of PFGE method, and that the possible PFGE markers may have utility for serotype identification. The presence of distinct, serotype specific patterns may provide useful information to aid in the distribution of serotypes in the population and potentially reduce the need for laborious analyses, such as traditional serotyping.
  PLoS ONE 01/2013; 8(3):e59224. · 4.09 Impact Factor
• Article: Rat α-Fetoprotein Binding Affinities of a Large Set of Structurally Diverse Chemicals Elucidated the Relationships between Structures and Binding Affinities.

  Huixiao Hong, William S Branham, Stacey L Dial, Carrie L Moland, Hong Fang, Jie Shen, Roger Perkins, Daniel Sheehan, Weida Tong
  [show abstract] [hide abstract]
  ABSTRACT: Endocrine disrupting chemicals interfere with the endocrine system in animals, including humans, to exert adverse effects. One of the mechanisms of endocrine disruption is through the binding of receptors such as the estrogen receptor (ER) in target cells. The concentration of any chemical in serum is important for its entry into the target cells to bind the receptors. α-Fetoprotein (AFP) is a major transport protein in rodent serum that can bind with estrogens and thus change a chemical's availability for entrance into the target cell. Sequestration of an estrogen in the serum can alter the chemical's potential for disrupting estrogen receptor-mediated responses. To better understand endocrine disruption, we developed a competitive binding assay using rat amniotic fluid, which contains very high levels of AFP, and measured the binding to the rat AFP for 125 structurally diverse chemicals, most of which are known to bind ER. Fifty-three chemicals were able to bind the rat AFP in the assay, while 72 chemicals were determined to be nonbinders. Observations from closely examining the relationship between the binding data and structures of the tested chemicals are rationally explained in a manner consistent with proposed binding regions of rat AFP in the literature. The data reported here represent the largest data set of structurally diverse chemicals tested for rat AFP binding. The data assist in elucidating binding interactions and mechanisms between chemicals and rat AFP and, in turn, assist in the evaluation of the endocrine disrupting potential of chemicals.
  Chemical Research in Toxicology 09/2012; · 3.78 Impact Factor
• Article: Investigating drug repositioning opportunities in FDA drug labels through topic modeling.

  Halil Bisgin, Zhichao Liu, Reagan Kelly, Hong Fang, Xiaowei Xu, Weida Tong
  [show abstract] [hide abstract]
  ABSTRACT: Drug repositioning offers an opportunity to revitalize the slowing drug discovery pipeline by finding new uses for currently existing drugs. Our hypothesis is that drugs sharing similar side effect profiles are likely to be effective for the same disease, and thus repositioning opportunities can be identified by finding drug pairs with similar side effects documented in U.S. Food and Drug Administration (FDA) approved drug labels. The safety information in the drug labels is usually obtained in the clinical trial and augmented with the observations in the post-market use of the drug. Therefore, our drug repositioning approach can take the advantage of more comprehensive safety information comparing with conventional de novo approach. A probabilistic topic model was constructed based on the terms in the Medical Dictionary for Regulatory Activities (MedDRA) that appeared in the Boxed Warning, Warnings and Precautions, and Adverse Reactions sections of the labels of 870 drugs. Fifty-two unique topics, each containing a set of terms, were identified by using topic modeling. The resulting probabilistic topic associations were used to measure the distance (similarity) between drugs. The success of the proposed model was evaluated by comparing a drug and its nearest neighbor (i.e., a drug pair) for common indications found in the Indications and Usage Section of the drug labels. Given a drug with more than three indications, the model yielded a 75% recall, meaning 75% of drug pairs shared one or more common indications. This is significantly higher than the 22% recall rate achieved by random selection. Additionally, the recall rate grows rapidly as the number of drug indications increases and reaches 84% for drugs with 11 indications. The analysis also demonstrated that 65 drugs with a Boxed Warning, which indicates significant risk of serious and possibly life-threatening adverse effects, might be replaced with safer alternatives that do not have a Boxed Warning. In addition, we identified two therapeutic groups of drugs (Musculo-skeletal system and Anti-infective for systemic use) where over 80% of the drugs have a potential replacement with high significance. Topic modeling can be a powerful tool for the identification of repositioning opportunities by examining the adverse event terms in FDA approved drug labels. The proposed framework not only suggests drugs that can be repurposed, but also provides insight into the safety of repositioned drugs.
  BMC Bioinformatics 09/2012; 13 Suppl 15:S6. · 2.75 Impact Factor
• Article: In silico drug repositioning - what we need to know.

  Zhichao Liu, Hong Fang, Kelly Reagan, Xiaowei Xu, Donna L Mendrick, William Slikker, Weida Tong
  [show abstract] [hide abstract]
  ABSTRACT: Drug repositioning, exemplified by sildenafil and thalidomide, is a promising way to explore alternative indications for existing drugs. Recent research has shown that bioinformatics-based approaches have the potential to offer systematic insights into the complex relationships among drugs, targets and diseases necessary for successful repositioning. In this article, we propose the key bioinformatics steps essential for discovering valuable repositioning methods. The proposed steps (repurposing with a purpose, repurposing with a strategy and repurposing with confidence) are aimed at providing a repurposing pipeline, with particular focus on the proposed Drugs of New Indications (DNI) database, which can be used alongside currently available resources to improve in silico drug repositioning.
  Drug discovery today 08/2012; · 6.63 Impact Factor
• Source

  Available from: Hong Fang

  Article: atBioNet- an integrated network analysis tool for genomics and biomarker discovery.

  Yijun Ding, Minjun Chen, Zhichao Liu, Don Ding, Yanbin Ye, Min Zhang, Reagan Kelly, Li Guo, Zhenqiang Su, Stephen C Harris, Feng Qian, Weigong Ge, Hong Fang, Xiaowei Xu, Weida Tong
  [show abstract] [hide abstract]
  ABSTRACT: Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.
  BMC Genomics 07/2012; 13:325. · 4.07 Impact Factor