Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering最新文献

Optogenetics offers a powerful new approach for controlling neural circuits. It has a vast array of applications in both basic and clinical science. For basic science, it opens the door to unraveling circuit operations, since one can perturb specific circuit components with high spatial (single cell) and high temporal (millisecond) resolution. For clinical applications, it allows new kinds of selective treatments, because it provides a method to inactivate or activate specific components in a malfunctioning circuit and bring it back into a normal operating range [1-3]. To harness the power of optogenetics, though, one needs stimulating tools that work with the same high spatial and temporal resolution as the molecules themselves, the channelrhodopsins. To date, most stimulating tools require a tradeoff between spatial and temporal precision and are prohibitively expensive to integrate into a stimulating/recording setup in a laboratory or a device in a clinical setting [4, 5]. Here we describe a Digital Light Processing (DLP)-based system capable of extremely high temporal resolution (sub-millisecond), without sacrificing spatial resolution. Furthermore, it is constructed using off-the-shelf components, making it feasible for a broad range of biology and bioengineering labs. Using transgenic mice that express channelrhodopsin-2 (ChR2), we demonstrate the system's capability for stimulating channelrhodopsin-expressing neurons in tissue with single cell and sub-millisecond precision.

Sequence similarity based protein clustering methods organize proteins into families of similar sequences, a task that continues to be critical for automated protein characterization. However, many protein families cannot be automatically characterized further because little is known about the function of any protein in a family of similar sequences. We present a novel phylogenetic profile comparison (PPC) method called Automated Protein Annotation by Coordinate Evolution (APACE) that facilitates the automated characterization of proteins beyond their homology to other similar sequences. Our method implements a new approach for the normalization of similarity scores among multiple species and automates the characterization of proteins by their patterns of co-evolution with other proteins that do not necessarily share a similar sequence. We demonstrate that our method is able to recapitulate the topology of the latest, unresolved, composite deep eukaryotic phylogeny and is able to quantify the as yet unresolved branch lengths. We further demonstrate that our method is able to detect more functionally related proteins, given the same starting data, than existing methods. Finally, we demonstrate that our method can be successfully applied to much larger comparative genomic problem instances where existing methods often fail.

Rapid development in genomics in recent years has allowed the simultaneous measurement of the expression levels of thousands of genes using DNA microarrays. This has offered tremendous potential for growth in our understanding of the pathophysiology of many diseases. When microarray studies also contain information about an outcome variable such as time to an event or death, one of the goals of an investigator is to understand how the expression levels of genes (covariates) relate to the time-to-event (referred to as survival time) in the course of a disease.In this article, we consider the case where the number of covariates, p, exceeds the number of observations, N, a setting typical of microarray gene expression data. For a given vector of responses representing survival times of N subjects and the corresponding p × N gene expression matrix, we examine the problem of predicting the survival probability when N ≪ p. This is an ill-conditioned problem further compounded by the presence of possibly censored survival times. We propose a model that combines the partial least squares approach for dimensionality reduction with the accelerated failure time model, a widely used log-linear model for linking censored survival time to covariates. We develop parametric methods to account for censoring as well as for predicting patient survival probabilities. We illustrate the applicability of our methods using cancer microarray data and explore the biological relevance of our results using pathway analysis. Finally, we evaluate the performance of our methods using extensive simulation studies.

Protein phosphorylation is a reversible post-translational modification commonly used by cell signaling networks to transmit information about the extracellular environment into intracellular organelles for the regulation of the activity and sorting of proteins within the cell. For this study we reconstructed a literature-based mammalian kinase-substrate network from several online resources. The interactions within this directed graph network connect kinases to their substrates, through specific phosphosites including kinasekinase regulatory interactions. However, the "signs" of links, activation or inhibition of the substrate upon phosphorylation, within this network are mostly unknown. Here we show how we can infer the "signs" indirectly using data from quantitative phosphoproteomics experiments applied to mammalian cells combined with the literature-based kinase-substrate network. Our inference method was able to predict the sign for 321 links and 153 phosphosites on 120 kinases, resulting in signed and directed subnetwork of mammalian kinase-kinase interactions. Such an approach can rapidly advance the reconstruction of cell signaling pathways and networks regulating mammalian cells.

Classifying chemical compounds is an active topic in drug design and other cheminformatics applications. Graphs are general tools for organizing information from heterogenous sources and have been applied in modelling many kinds of biological data. With the fast accumulation of chemical structure data, building highly accurate predictive models for chemical graphs emerges as a new challenge.In this paper, we demonstrate a novel technique called Graph Pattern Matching kernel (GPM). Our idea is to leverage existing frequent pattern discovery methods and explore their application to kernel classifiers (e.g. support vector machine) for graph classification. In our method, we first identify all frequent patterns from a graph database. We then map subgraphs to graphs in the database and use a diffusion process to label nodes in the graphs. Finally the kernel is computed using a set matching algorithm. We performed experiments on 16 chemical structure data sets and have compared our methods to other major graph kernels. The experimental results demonstrate excellent performance of our method.

Phagocyte transmigration is the initiation of a series of phagocyte responses that are believed important in the formation of fibrotic capsules surrounding implanted medical devices. Understanding the molecular mechanisms governing phagocyte transmigration is highly desired in order to improve the stability and functionality of the implanted devices. A hybrid computational model that combines control theory and kinetics Monte Carlo (KMC) algorithm is proposed to simulate and predict phagocytes responses at molecular level. In order to mimic various biological knockout experiments, a general external control scenario is designed. The stochastic nature inherent to phagocyte transmigration is captured by KMC. A new formula is derived to calculate the transition rates as inputs to KMC. This formulation might quantify biological interactions in a general manner which is beyond the scope of the traditional chemical reaction kinetics.

Imaging mass spectrometry is a method for understanding the molecular distribution in a two-dimensional sample. This method is effective for a wide range of molecules, but generates a large amount of data. It is difficult to extract important information from these large datasets manually and automated methods for discovering important spatial and spectral features are needed. Independent component analysis and non-negative matrix factorization are explained and explored as tools for identifying underlying factors in the data. These techniques are compared and contrasted with principle component analysis, the more standard analysis tool. Independent component analysis and non-negative matrix factorization are found to be more effective analysis methods. A mouse cerebellum dataset is used for testing.

During the analysis of microscopy images, researchers locate regions of interest (ROI) and extract relevant information within it. Identifying the ROI is mostly done manually and subjectively by pathologists. Computer algorithms could help in reducing their workload and improve reproducibility. In particular, we want to assess the validity of the folic acid receptor as a biomarker for head and neck cancer. We are only interested in folic acid receptors appearing in cancerous tissue. Therefore, the first step is to segment images into cancerous and noncancerous regions. We propose to use a spiral intensity profile for segmentation of light microscopy images. Many algorithms identify objects in an image by considering pixel intensity and spatial information separately. Our algorithm integrates intensity and spatial information by considering the change, or profile, of pixel intensity in a spiral fashion. Using a spiral intensity profile can also perform segmentation at different scales from cancer regions to nuclei cluster to individual nuclei. We compared our algorithm with manually segmented image and obtained a specificity of 83.7% and sensitivity of 61.1%. Spiral intensity profiles can be used as a feature to improve other segmentation algorithms. Segmentation of cancerous images at different scales allows effective quantification of folic acid receptor inside cancerous regions, nuclei clusters, or individual cells.

In this paper, we present an improved automated system for classification of pathological image data of renal cell carcinoma. The task of analyzing tissue biopsies, generally performed manually by expert pathologists, is extremely challenging due to the variability in the tissue morphology, the preparation of tissue specimen, and the image acquisition process. Due to the complexity of this task and heterogeneity of patient tissue, this process suffers from inter-observer and intra-observer variability. In continuation of our previous work, which proposed a knowledge-based automated system, we observe that real life clinical biopsy images which contain necrotic regions and glands significantly degrade the classification process. Following the pathologist's technique of focusing on selected region of interest (ROI), we propose a simple ROI selection process which automatically rejects the glands and necrotic regions thereby improving the classification accuracy. We were able to improve the classification accuracy from 90% to 95% on a significantly heterogeneous image data set using our technique.

Recently, High-content screening (HCS) has been combined with RNA interference (RNAi) to become an essential image-based high-throughput method for studying genes and biological networks through RNAi-induced cellular phenotype analyses. However, a genome-wide RNAi-HCS screen typically generates tens of thousands of images, most of which remain uncategorized due to the inadequacies of existing HCS image analysis tools. Until now, it still requires highly trained scientists to browse a prohibitively large RNAi-HCS image database and produce only a handful of qualitative results regarding cellular morphological phenotypes. For this reason we have developed intelligent interfaces to facilitate the application of the HCS technology in biomedical research. Our new interfaces empower biologists with computational power not only to effectively and efficiently explore large-scale RNAi-HCS image databases, but also to apply their knowledge and experience to interactive mining of cellular phenotypes using Content-Based Image Retrieval (CBIR) with Relevance Feedback (RF) techniques.