• Y. Zhao, S. Piekos, T. H. Hoang, and D. G. Shin, “A framework using topological pathways for deeper analysis of transcriptome data,” Bmc genomics, vol. 21, iss. Suppl 1, 2020.
  [Bibtex]

  @article{Zhao2020,
  abstract = {Background: Pathway analysis is one of the later stage data analysis steps essential in interpreting high-throughput gene expression data. We propose a set of algorithms which given gene expression data can recognize which portion of sub-pathways are actively utilized in the biological system being studied. The degree of activation is measured by conditional probability of the input expression data based on the Bayesian Network model constructed from the topological pathway. Results: We demonstrate the effectiveness of our pathway analysis method by conducting two case studies. The first one applies our method to a well-studied temporal microarray data set for the cell cycle using the KEGG Cell Cycle pathway. Our method closely reproduces the biological claims associated with the data sets, but unlike the original work ours can produce how pathway routes interact with each other above and beyond merely identifying which pathway routes are involved in the process. The second study applies the method to the p53 mutation microarray data to perform a comparative study. Conclusions: We show that our method achieves comparable performance against all other pathway analysis systems included in this study in identifying p53 altered pathways. Our method could pave a new way of carrying out next generation pathway analysis.},
  author = {Zhao, Yue and Piekos, Stephanie and Hoang, Tham H. and Shin, Dong Guk},
  doi = {10.1186/s12864-019-6155-6},
  file = {:C$\backslash$:/Users/pmaye/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Zhao et al. - 2020 - A framework using topological pathways for deeper analysis of transcriptome data.pdf:pdf},
  issn = {14712164},
  journal = {BMC Genomics},
  keywords = {Bayesian Network,Depth First Search,Topological Pathway Analysis},
  month = {mar},
  number = {Suppl 1},
  publisher = {BioMed Central Ltd.},
  title = {{A framework using topological pathways for deeper analysis of transcriptome data}},
  volume = {21},
  year = {2020}
  }

• D. W. Rowe, D. J. Adams, S. Hong, C. Zhang, D. Shin, R. C. Rydzik, L. Chen, Z. Wu, G. Garland, D. A. Godfrey, J. P. Sundberg, and C. Ackert-Bicknell, “Screening Gene Knockout Mice for Variation in Bone Mass: Analysis by $\mu$CT and Histomorphometry,” Current osteoporosis reports, vol. 16, iss. 2, p. 77–94, 2018.
  [Bibtex]

  @article{Rowe2018,
  author = {Rowe, David W. and Adams, Douglas J. and Hong, Seung-Hyun and Zhang, Caibin and Shin, Dong-Guk and Rydzik, Renata C. and Chen, Li and Wu, Zhihua and Garland, Gaven and Godfrey, Dana A. and Sundberg, John P. and Ackert-Bicknell, Cheryl},
  doi = {10.1007/s11914-018-0421-4},
  file = {:C$\backslash$:/Users/pmaye/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Rowe et al. - 2018 - Screening Gene Knockout Mice for Variation in Bone Mass Analysis by $\mu$CT and Histomorphometry.pdf:pdf},
  issn = {1544-1873},
  journal = {Current Osteoporosis Reports},
  month = {apr},
  number = {2},
  pages = {77--94},
  publisher = {Springer US},
  title = {{Screening Gene Knockout Mice for Variation in Bone Mass: Analysis by $\mu$CT and Histomorphometry}},
  url = {http://link.springer.com/10.1007/s11914-018-0421-4},
  volume = {16},
  year = {2018}
  }

• N. A. Dyment, X. Jiang, L. Chen, S. H. Hong, D. J. Adams, C. Ackert-Bicknell, D. G. Shin, and D. W. Rowe, “High-throughput, multi-image cryohistology of mineralized tissues,” Journal of visualized experiments, vol. 2016, iss. 115, 2016.
  [Bibtex]

  @article{Dyment2016,
  abstract = {There is an increasing need for efficient phenotyping and histopathology of a variety of tissues. This phenotyping need is evident with the ambitious projects to disrupt every gene in the mouse genome. The research community needs rapid and inexpensive means to phenotype tissues via histology. Histological analyses of skeletal tissues are often time consuming and semi-quantitative at best, regularly requiring subjective interpretation of slides from trained individuals. Here, we present a cryohistological paradigm for efficient and inexpensive phenotyping of mineralized tissues. First, we present a novel method of tape-stabilized cryosectioning that preserves the morphology of mineralized tissues. These sections are then adhered rigidly to glass slides and imaged repeatedly over several rounds of staining. The resultant images are then aligned either manually or via computer software to yield composite stacks of several layered images. The protocol allows for co-localization of numerous molecular signals to specific cells within a given section. In addition, these fluorescent signals can be quantified objectively via computer software. This protocol overcomes many of the shortcomings associated with histology of mineralized tissues and can serve as a platform for high-throughput, high-content phenotyping of musculoskeletal tissues moving forward.},
  author = {Dyment, Nathaniel A. and Jiang, Xi and Chen, Li and Hong, Seung Hyun and Adams, Douglas J. and Ackert-Bicknell, Cheryl and Shin, Dong Guk and Rowe, David W.},
  doi = {10.3791/54468},
  file = {:C$\backslash$:/Users/pmaye/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dyment et al. - 2016 - High-throughput, multi-image cryohistology of mineralized tissues.pdf:pdf},
  issn = {1940087X},
  journal = {Journal of Visualized Experiments},
  keywords = {Cellular biology,Cryosectioning,Cryotape,Fluorescent imaging,Fluorescent proteins,High-throughput,Issue 115,Mineralization labels,Multiphoton imaging},
  month = {sep},
  number = {115},
  pmid = {27684089},
  publisher = {Journal of Visualized Experiments},
  title = {{High-throughput, multi-image cryohistology of mineralized tissues}},
  volume = {2016},
  year = {2016}
  }

• S. Hong, X. Jiang, L. Chen, P. Josh, D. Shin, and D. Rowe, “Computer-Automated Static, Dynamic and Cellular Bone Histomorphometry,” Journal of tissue science & engineering, vol. 05, iss. 01, 2014.
  [Bibtex]

  @article{HyunHong2014,
  abstract = {Dynamic and cellular histomorphometry of trabeculae is the most biologically relevant way of assessing steady state bone health. Traditional measurement involves manual visual feature identification by a trained and qualified professional. Inherent with this methodology is the time and cost expenditure, as well as the subjectivity that naturally arises under human visual inspection. In this work, we propose a rapidly deployable, automated, and objective method for dynamic histomorphometry. We demonstrate that our method is highly effective in assessing cellular activities in distal femur and vertebra of mice which are injected with calcein and alizarin complexone 7 and 2 days prior to sacrifice. The mineralized bone tissues of mice are cryosectioned using a tape transfer protocol. A sequential workflow is implemented in which endogenous fluorescent signals (bone mineral, green and red mineralization lines), tartrate resistant acid phosphatase identified by ELF-97 and alkaline phosphatase identified by Fast Red are captured as individual tiled images of the section for each fluorescent color. All the images are then submitted to an image analysis pipeline that automates identification of the mineralized regions of bone and selection of a region of interest. The TRAP and AP stained images are aligned to the mineralized image using strategically placed fluorescent registration beads. Fluorescent signals are identified and are related to the trabecular surface within the ROI. Subsequently, the pipelined method computes static measurements, dynamic measurements, and cellular activities of osteoclast and osteoblast related to the trabecular surface. Our method has been applied to the distal femurs and vertebrae of 8 and 16 week old male and female C57Bl/6J mice. The histomorphometric results reveal a significantly greater bone turnover rate in female in contrast to male irrespective of age, validating similar outcomes reported by other studies.},
  author = {Hong, Seung-Hyun and Jiang, Xi and Chen, Li and Josh, Pujan and Shin, Dong-Guk and Rowe, David},
  doi = {10.4172/2157-7552.s1-004},
  file = {:C$\backslash$:/Users/pmaye/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hyun Hong - 2014 - Computer-Automated Static, Dynamic and Cellular Bone Histomorphometry.pdf:pdf},
  journal = {Journal of Tissue Science {\&} Engineering},
  number = {01},
  publisher = {OMICS Publishing Group},
  title = {{Computer-Automated Static, Dynamic and Cellular Bone Histomorphometry}},
  volume = {05},
  year = {2014}
  }

bonebase