@article {79,
	title = {BioSig3D: High Content Screening of Three-Dimensional Cell Culture Models},
	journal = {PLoS One},
	volume = {11},
	year = {2016},
	month = {2016},
	pages = {e0148379},
	abstract = {<p>BioSig3D is a computational platform for high-content screening of three-dimensional (3D) cell culture models that are imaged in full 3D volume. It provides an end-to-end solution for designing high content screening assays, based on colony organization that is derived from segmentation of nuclei in each colony. BioSig3D also enables visualization of raw and processed 3D volumetric data for quality control, and integrates advanced bioinformatics analysis. The system consists of multiple computational and annotation modules that are coupled together with a strong use of controlled vocabularies to reduce ambiguities between different users. It is a web-based system that allows users to: design an experiment by defining experimental variables, upload a large set of volumetric images into the system, analyze and visualize the dataset, and either display computed indices as a heatmap, or phenotypic subtypes for heterogeneity analysis, or download computed indices for statistical analysis or integrative biology. BioSig3D has been used to profile baseline colony formations with two experiments: (i) morphogenesis of a panel of human mammary epithelial cell lines (HMEC), and (ii) heterogeneity in colony formation using an immortalized non-transformed cell line. These experiments reveal intrinsic growth properties of well-characterized cell lines that are routinely used for biological studies. BioSig3D is being released with seed datasets and video-based documentation.</p>
},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0148379},
	author = {Bilgin, Cemal Cagatay and Fontenay, Gerald and Cheng, Qingsu and Hang Chang and Ju Han and Parvin, Bahram}
}
@article {289,
	title = {Inference of causal networks from time-varying transcriptome data via sparse coding.},
	journal = {PLoS One},
	volume = {7},
	year = {2012},
	month = {2012},
	pages = {e42306},
	abstract = {<p>Temporal analysis of genome-wide data can provide insights into the underlying mechanism of the biological processes in two ways. First, grouping the temporal data provides a richer, more robust representation of the underlying processes that are co-regulated. The net result is a significant dimensional reduction of the genome-wide array data into a smaller set of vocabularies for bioinformatics analysis. Second, the computed set of time-course vocabularies can be interrogated for a potential causal network that can shed light on the underlying interactions. The method is coupled with an experiment for investigating responses to high doses of ionizing radiation with and without a small priming dose. From a computational perspective, inference of a causal network can rapidly become computationally intractable with the increasing number of variables. Additionally, from a bioinformatics perspective, larger networks always hinder interpretation. Therefore, our method focuses on inferring the simplest network that is computationally tractable and interpretable. The method first reduces the number of temporal variables through consensus clustering to reveal a small set of temporal templates. It then enforces simplicity in the network configuration through the sparsity constraint, which is further regularized by requiring continuity between consecutive time points. We present intermediate results for each computational step, and apply our method to a time-course transcriptome dataset for a cell line receiving a challenge dose of ionizing radiation with and without a prior priming dose. Our analyses indicate that (i) the priming dose increases the diversity of the computed templates (e.g., diversity of transcriptome signatures); thus, increasing the network complexity; (ii) as a result of the priming dose, there are a number of unique templates with delayed and oscillatory profiles; and (iii) radiation-induced stress responses are enriched through pathway and subnetwork studies.</p>
},
	keywords = {Gene Expression Profiling, Genome-Wide Association Study, Humans, Radiation, Ionizing, Transcriptome},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0042306},
	author = {Zhang, Kai and Ju Han and Groesser, Torsten and Fontenay, Gerald and Parvin, Bahram}
}
