MicroPC (μPC): A comprehensive resource for predicting and comparing plant microRNAs

<p>Abstract</p> <p>Background</p> <p>Plant microRNA (miRNA) has an important role in controlling gene regulation in various biological processes such as cell development, signal transduction, and environmental responses. While information on plant miRNAs and their targets is widely available, accessible online plant miRNA resources are limited; most of them are intended for economically important crops or plant model organisms. With abundant sequence data of numerous plants in public databases such as NCBI and PlantGDB, the identification of their miRNAs and targets would benefit researchers as a central resource for the comparative studies of plant miRNAs.</p> <p>Results</p> <p>MicroPC (μPC) is an online plant miRNA resource resulted from large-scale Expressed Sequence Tag (EST) analysis. It consists of 4,006 potential miRNA candidates in 128 families of 125 plant species and 2,995 proteins (4,953 EST sequences) potentially targeted by 78 families of miRNA candidates. In addition, it is incorporated with 1,727 previously reported plant mature miRNA sequences from miRBase. The μPC enables users to compare stored mature or precursor miRNAs and user-supplied sequences among plant species. The search utility allows users to investigate the predicted miRNAs and miRNA targets in detail via various search options such as miRNA family and plant species. To enhance the database usage, the prediction utility provides interactive steps for determining a miRNA or miRNA targets from an input nucleotide sequence and links the prediction results to their homologs in the μPC.</p> <p>Conclusion</p> <p>The μPC constitutes the first online resource that enables users to comprehensively compare and predict plant miRNAs and their targets. It imparts a basis for further research on revealing miRNA conservation, function, and evolution across plant species and classification. The μPC is available at <url>http://www.biotec.or.th/isl/micropc</url>.</p

QoS-Aware Meta-Data Compiler for Ubiquitous Multimedia Applications

The reusability of available multimedia and middleware services brings new challenges for enabling flexible and efficient development and deployment of distributed end-to-end multimedia applications with specific Quality-of-Service(QoS) in ubiquitous environments. The main challenges in reusing available components include understanding and utilizing of domain-specific components and middlewares with various semantics, and enabling their QoS-aware interoperability in ubiquitous environments that resource fluctuations, device and service changes are a common phenomenon. This paper presents a QoS-aware meta-data compiler framework that provides a solution for the challenges. The framework extends standard component construction and composition with QoS-related meta-data. It defines a set of QoS-aware models and meta-data translation models that are essential for modelling QoS consistency. The framework also enables QoS-aware semantics and interfaces for interoperability among connected components forming a QoS-aware multimedia application. Besides the defined models, the framework introduces an architecture that integrates the models with a set of high-level specifications, a meta-data compiler protocol, and a run-time support to form a programming environment, called Q-Compiler. The Q-Compiler helps to automate the development and deployment of a component-based, QoS-aware application, deployable in ubiquitous environments. To validate the viability of the Q-Compiler, we use it to develop a mobile Video-on-Demand application in an active space project. The experimental results show that the introduction of a translator code between connected components does not degrade the overall service quality of the components. Although the main contributions of the framework are validated via multimedia domain, we anticipate that fundamental concepts and design will be applicable to other application domains

Q -Compiler: Metadata Qos-Aware Programming and Compilation Framework

228 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2003.This dissertation presents the Q-Compiler, which is a novel meta-data QoS programming and compilation framework for developing and deploying quality-aware applications. The Q-Compiler extends standard component construction with QoS-related meta-data. It proposes a set of translation models and their compilations which enable semantic interoperability among interoperable components' specific QoS requirements and provisions. It provides a set of high-level, customizable application specifications which hides the complexity of quality-aware programming. It provides a meta-data compilation which validates quality consistency of an application composition, and promotes configurability and efficiency of application deployment. The Q-Compiler's run-time support assists the compilations in preparing required QoS-related information, and uses the compilation result in running an application. In this dissertation, although the main contributions of the framework are validated via multimedia applications, we anticipate that the fundamental concepts and design will be applicable to other application domains.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

iEdgeDTA: integrated edge information and 1D graph convolutional neural networks for binding affinity prediction

Drug repurposing, where an existing low-risk drug is applied for new indications, becomes more attractive in drug development as drug discovery is very costly and time-consuming. However, the wet-lab testing process to find a drug candidate for a new purpose from its possible binding to a protein is still expensive and laborious due to the available vast quantity of drugs and target proteins. This study aims to leverage artificial intelligence to aid drug repurposing by utilizing drug-protein interaction data and estimating their binding affinity. In this work, we propose an estimation approach that employs a graph-based deep learning technique and enhances prediction accuracy by incorporating the compound\u27s edge information as a multi-dimensional feature. In addition, we used a pre-trained model for protein embedding and graph operation over a 1D protein sequence to overcome a fixed-length problem in the language model task and also incorporated the global feature of the protein. We evaluated the performance of our model in the same benchmark datasets using a variety of matrices, and the results show that our model can achieve the best prediction result compared to other state-of-the-art models while at the same time requiring no contact-map information compared to recent graph-based works Availability: https://github.com/cucpbioinfo/iEdgeDT

Distributed QoS Compilation and Runtime Instantiation

The rapid growth and coexistence of different application domains, such as multimedia and electronic commerce, present a significant challenge to the provision of their Quality of Service (QoS). To solve this challenge, we need a unified QoS framework, which allows flexibility and reconfigurability. In this paper, we present a reconfigurable component-based QoS framework, called 2K Q , which solves the challenge by partitioning the end-to-end QoS setup process into distributed QoS compilation and runtime QoS instantiation phases for different types of applications. Entities, services and protocols of this framework, such as application-to-component translator and component-to-resources translators, achieve the distributed QoS compilation and prepare all necessary QoS structures for the end-to-end QoS setup. Other capabilities of this framework, such as a reconfigurable middleware and functional adaptation, achieve the runtime instantiation of the end-to-end QoS setup. We have imple..

Mol-Zero-GAN: Zero-Shot Adaptation of Molecular Generative Adversarial Network for Specific Protein Targets

Drug discovery is a process that finds new potential drug candidates for curing diseases and is also vital to improving the wellness of people. Enhancing deep learning approaches, e.g., molecular generation models, increases the drug discovery process\u27s efficiency. However, there is a problem in this field in creating drug candidates with desired properties such as the quantitative estimate of druglikeness (QED), synthesis accessibility (SA), and binding affinity (BA), and there is a challenge for training generative model for specific protein targets that has less pharmaceutical data. In this research, we present Mol-Zero-GAN, a framework that aims to solve the problem based on Bayesian optimization (BO) to find the model optimal weights\u27 singular values, factorized by singular value decomposition, and can generate drug candidates with desired properties with no additional data. The proposed framework can produce drugs with the desired properties on protein targets of interest by optimizing the model\u27s weights. Our framework outperforms the state-of-the-art methods sharing the same objectives. Mol-Zero-GAN is publicly available at https://github.com/cucpbioinfo/Mol-Zero-GA

Visual QoS Programming Environment for Ubiquitous Multimedia Services

The provision of distributed multimedia services is becoming mobile and ubiquitous. Different multimedia services require application-specific Quality of Service (QoS). In this paper, we present QoSTalk, a unified component-based programming environment that allows application developers to specify different application-specific QoS requirements easily. In QoSTalk, we adopt a hierarchical approach to model application configuration graphs for different distributed multimedia services. We design and implement the XML-based Hierarchical QoS Markup Language, called HQML, to describe the hierarchical configuration graph as well as other application-specific QoS requirements and policies. QoSTalk promotes the separation of concerns in developing QoS-aware ubiquitous multimedia applications and thus enables easy programming of QoS-aware applications, running on top of a unified QoS-aware middleware framework. We have prototyped the QoSTalk in Java and CORBA. Our case studies with several multimedia applications show that QoSTalk effectively fills the gap for application developers between the very general facilities provided by the QoS-aware middleware and different kinds of distributed multimedia applications