Joint stage recognition and anatomical annotation of drosophila gene expression patterns

Motivation: Staining the mRNA of a gene via in situ hybridization (ISH) during the development of a Drosophila melanogaster embryo delivers the detailed spatio-temporal patterns of the gene expression. Many related biological problems such as the detection of co-expressed genes, co-regulated genes and transcription factor binding motifs rely heavily on the analysis of these image patterns. To provide the text-based pattern searching for facilitating related biological studies, the images in the Berkeley Drosophila Genome Project (BDGP) study are annotated with developmental stage term and anatomical ontology terms manually by domain experts. Due to the rapid increase in the number of such images and the inevitable bias annotations by human curators, it is necessary to develop an automatic method to recognize the developmental stage and annotate anatomical terms

SPEX2: automated concise extraction of spatial gene expression patterns from Fly embryo ISH images

Motivation: Microarray profiling of mRNA abundance is often ill suited for temporal–spatial analysis of gene expressions in multicellular organisms such as Drosophila. Recent progress in image-based genome-scale profiling of whole-body mRNA patterns via in situ hybridization (ISH) calls for development of accurate and automatic image analysis systems to facilitate efficient mining of complex temporal–spatial mRNA patterns, which will be essential for functional genomics and network inference in higher organisms

Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval

abstract: Background Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords. Results In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes. Conclusions We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.The electronic version of this article is the complete one and can be found online at: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-10

Learning Sparse Representations for Fruit Fly Gene Expression Pattern Image Annotation and Retreival

Background: Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords. Results: In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes. Conclusions: We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results

BSQA: integrated text mining using entity relation semantics extracted from biological literature of insects

Text mining is one promising way of extracting information automatically from the vast biological literature. To maximize its potential, the knowledge encoded in the text should be translated to some semantic representation such as entities and relations, which could be analyzed by machines. But large-scale practical systems for this purpose are rare. We present BeeSpace question/answering (BSQA) system that performs integrated text mining for insect biology, covering diverse aspects from molecular interactions of genes to insect behavior. BSQA recognizes a number of entities and relations in Medline documents about the model insect, Drosophila melanogaster. For any text query, BSQA exploits entity annotation of retrieved documents to identify important concepts in different categories. By utilizing the extracted relations, BSQA is also able to answer many biologically motivated questions, from simple ones such as, which anatomical part is a gene expressed in, to more complex ones involving multiple types of relations. BSQA is freely available at http://www.beespace.uiuc.edu/QuestionAnswer

Learning Sparse Representations for Fruit Fly Gene Expression Pattern Image Annotation and Retreival

Background: Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords. Results: In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes. Conclusions: We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results

A Computational Framework for Learning from Complex Data: Formulations, Algorithms, and Applications

Many real-world processes are dynamically changing over time. As a consequence, the observed complex data generated by these processes also evolve smoothly. For example, in computational biology, the expression data matrices are evolving, since gene expression controls are deployed sequentially during development in many biological processes. Investigations into the spatial and temporal gene expression dynamics are essential for understanding the regulatory biology governing development. In this dissertation, I mainly focus on two types of complex data: genome-wide spatial gene expression patterns in the model organism fruit fly and Allen Brain Atlas mouse brain data. I provide a framework to explore spatiotemporal regulation of gene expression during development. I develop evolutionary co-clustering formulation to identify co-expressed domains and the associated genes simultaneously over different temporal stages using a mesh-generation pipeline. I also propose to employ the deep convolutional neural networks as a multi-layer feature extractor to generate generic representations for gene expression pattern in situ hybridization (ISH) images. Furthermore, I employ the multi-task learning method to fine-tune the pre-trained models with labeled ISH images. My proposed computational methods are evaluated using synthetic data sets and real biological data sets including the gene expression data from the fruit fly BDGP data sets and Allen Developing Mouse Brain Atlas in comparison with baseline existing methods. Experimental results indicate that the proposed representations, formulations, and methods are efficient and effective in annotating and analyzing the large-scale biological data sets

Multi-Label Dimensionality Reduction

abstract: Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms.Dissertation/ThesisPh.D. Computer Science 201

The role of the femoral chordotonal organ in motor control, interleg coordination, and leg kinematics in Drosophila melanogaster

Legged locomotion in terrestrial animals is often essential for mating and survival, and locomotor behavior must be robust and adaptable in order to be successful. The behavioral plasticity demonstrated by animals’ ability to locomote across diverse types of terrains and to change their locomotion in a task-dependent manner highlights the flexible and modular nature of locomotor networks. The six legs of insects are under the multi-level control of local networks for each limb and limb joint in addition to over-arching central control of the local networks. These networks, consisting of pattern-generating groups of interneurons, motor neurons, and muscles, receive modifying and reinforcing feedback from sensory structures that encode motor output. Proprioceptors in the limbs monitoring their position and movement provide information to these networks that is essential for the adaptability and robustness of locomotor behavior. In insects, proprioceptors are highly diverse, and the exact role of each type in motor control has yet to be determined. Chordotonal organs, analogous to vertebrate muscle spindles, are proprioceptive stretch receptors that span joints and encode specific parameters of relative movement between body segments. In insects, when leg chordotonal organs are disabled or manipulated, interleg coordination and walking are affected, but the simple behavior of straight walking on a flat surface can still be performed. The femoral chordotonal organ (fCO) is the largest leg proprioceptor and monitors the position and movements of the tibia relative to the femur. It has long been studied for its importance in locomotor and postural control. In Drosophila melanogaster, an ideal model organism due its genetic tractability, investigations into the composition, connectivity, and function of the fCO are still in their infancy. The fCO in Drosophila contains anatomical subgroups, and the neurons within a subgroup demonstrate similar responses to movements about the femur-tibia joint. Collectively, the experiments laid out in this dissertation provide a multi-faceted analysis of the anatomy, connectivity, and functional importance of subgroups of fCO neurons in D. melanogaster. The dissertation is divided into four chapters, representing different aspects of this complex and intriguing system. First, I present a detailed analysis of the composition of the fCO and its connectivity within the peripheral and central nervous systems. I demonstrate that the fCO is made up of anatomically distinct groups of neurons, each with their own unique features in the legs and ventral nerve cord. Second, I investigated the neuropeptide profile of the fCO and demonstrate that some fCO neurons express a susbtance that is known to act as a neuromodulator. Third, I demonstrate the sufficiency of subsets of fCO neurons to elicit reflex responses, highlighting the role of the Drosophila fCO in postural control. Lastly, I take this a step further and look into the functional necessity of these neuronal subsets for intra- and interleg coordination during walking. The importance of the fCO in motor control in D. melanogaster has been considered rather minor, though research into the topic is very limited. In the work laid out herein, I highlight the complexity of the Drosophila fCO and its role in the determination of locomotor behavior