Biomedical subjects

Henry Horng-Shing Lu

Publications and source records attributed to Henry Horng-Shing Lu.

3 recordsLinked to original sources

PubMed2005

Statistical methods for identifying yeast cell cycle transcription factors.

Knowing transcription factors (TFs) involved in the yeast cell cycle is helpful for understanding the regulation of yeast cell cycle genes. We therefore developed two methods for predicting (i) individual cell cycle TFs and (ii) synergistic TF pairs. The essential idea is that genes regulated by a cell cycle TF should have higher (lower, if it is a repressor) expression levels than genes not regulated by it during one or more phases of the cell cycle. This idea can also be used to identify synergistic interactions of TFs. Applying our methods to chromatin immunoprecipitation data and microarray data, we predict 50 cell cycle TFs and 80 synergistic TF pairs, including most known cell cycle TFs and synergistic TF pairs. Using these and published results, we describe the behaviors of 50 known or inferred cell cycle TFs in each cell cycle phase in terms of activation/repression and potential positive/negative interactions between TFs. In addition to the cell cycle, our methods are also applicable to other functions.

Cell Cycle↗

PubMed2005

Explore biological pathways from noisy array data by directed acyclic Boolean networks.

We consider the structure of directed acyclic Boolean (DAB) networks as a tool for exploring biological pathways. In a DAB network, the basic objects are binary elements and their Boolean duals. A DAB is characterized by two kinds of pairwise relations: similarity and prerequisite. The latter is a partial order relation, namely, the on-status of one element is necessary for the on-status of another element. A DAB network is uniquely determined by the state space of its elements. We arrange samples from the state space of a DAB network in a binary array and introduce a random mechanism of measurement error. Our inference strategy consists of two stages. First, we consider each pair of elements and try to identify their most likely relation. In the meantime, we assign a score, s-p-score, to this relation. Second, we rank the s-p-scores obtained from the first stage. We expect that relations with smaller s-p-scores are more likely to be true, and those with larger s-p-scores are more likely to be false. The key idea is the definition of s-scores (referring to similarity), p-scores (referring to prerequisite), and s-p-scores. As with classical statistical tests, control of false negatives and false positives are our primary concerns. We illustrate the method by a simulated example, the classical arginine biosynthetic pathway, and show some exploratory results on a published microarray expression dataset of yeast Saccharomyces cerevisiae obtained from experiments with activation and genetic perturbation of the pheromone response MAPK pathway.

Algorithms↗

PubMed2002

Cell-based dual snake model: a new approach to extracting highly winding boundaries in the ultrasound images.

Two common deficiencies of most conventional deformable models are the need to place the initial contour very close to the desired boundary and the incapability of capturing a highly winding boundary for sonographic boundary extraction. To remedy these two deficiencies, a new deformable model (namely, the cell-based dual snake model) is proposed in this paper. The basic idea is to apply the dual snake model in the cell-based deformation manner. While the dual snake model provides an effective mechanism allowing a distant initial contour, the cell-based deformation makes it possible to catch the winding characteristics of the desired boundary. The performance of the proposed cell-based dual snake model has been evaluated on synthetic images with simulated speckles and on the clinical ultrasound (US) images. The experimental results show that the mean distances from the derived to the desired boundary points are 0.9 +/- 0.42 pixels and 1.29 +/- 0.39 pixels for the synthetic and the clinical US images, respectively.

Algorithms↗