Comprehensive Integration Of Information Research Articles

DPMGCDA: Deciphering circRNA-Drug Sensitivity Associations with Dual Perspective Learning and Path-Masked Graph Autoencoder.

Accumulating evidence has indicated that the expression of circular RNAs (circRNAs) can affect the cellular sensitivity to drugs and significantly influence drug efficacy. However, traditional experimental approaches for validating these associations are resource-intensive and time-consuming. To address this challenge, we propose a computational framework termed DPMGCDA leveraging dual perspective learning and path-masked graph autoencoder to predict circRNA-drug sensitivity associations. Initially, we construct circRNA-circRNA fusion similarity networks and drug-drug fusion similarity networks using similarity network fusion, ensuring a comprehensive integration of information. Based on the above, we built the circRNA homogeneous graph, the drug homogeneous graph, and the circRNA-drug heterogeneous graph. Next, we form the initial node features in the circRNA-drug heterogeneous graph from the homogeneous graph-level perspective and the combined feature-level perspective and complete the prediction of potential associations using the path-masked graph autoencoder in both perspectives. The predictions under both perspectives are finally combined to obtain the final prediction score. Transductive setting experiments and inductive setting experiments all demonstrate that our method, DPMGCDA, outperforms state-of-the-art approaches. Additionally, we verify the necessity of employing dual perspective learning through ablation tests and analyze the effective encoding capability of the path-masked graph autoencoder for features through embedding visualization. Moreover, case studies on four drugs corroborate DPMGCDA's ability to identify potential circRNAs associated with new drugs.

An ontology-driven method for urban building energy modeling

The field of urban building energy modeling, embracing diverse aspects such as geography, construction, and materials. There is a lack of a comprehensive information integration framework to streamline cross-domain data in a systematic manner and generate simulation files that are easily calculable. This study addresses this gap by proposing two key ontologies: Building Template Ontology for managing building energy simulation templates and Urban Building Ontology for organizing building physics information. Utilizing this ontology-driven method, this study enhances flexibility in information fusion, exploits logical relationships between simulation inputs, and provides a lightweight solution for structured urban energy performance analysis. Energy simulations were conducted on over 5,000 buildings across three cities, and the impact of energy retrofit measures was further quantified, revealing potential savings of 0.1 % to 4.0 %, 1.2 % to 10.2 %, and 2.5 % to 6.9 % for building envelope, lighting, and air conditioning improvements, respectively. This study empowers urban stakeholders, designers, and managers to streamline the model construction and provides valuable insights into energy consumption patterns. Acknowledging limitations in current ontologies, including restricted building type templates, lack of real-time instance information, and absence of direct energy verification, this study underscores the imperative to address these for future advancements.

Traffic Flow Prediction Based on Information Aggregation and Comprehensive Temporal-Spatial Synchronous Graph Neural Network

It is extremely important to build a reasonable traffic network structure for traffic flow prediction. Owing to the complexity and dynamic of traffic networks, the graph neural network model has become one of the most effective methods for mining the spatial-temporal relationship between traffic flow data. However, most current methods use two components to extract the spatial dependence and time dependence separately and do not consider the auxiliary effect of additional traffic factors on the prediction target. Based on the above problems, this paper proposes a neural network prediction model for a comprehensive spatial-temporal synchronous graph based on information aggregation. The model is composed of a fusion feature attention module, an information aggregation module, and a comprehensive information integration framework. The fusion feature attention module considers the impact of each traffic factor on the traffic flow and strengthens the internal relationship of various traffic features; the information aggregation module synchronously extracts the temporal-spatial dependence of traffic flow; The multi-information combination module combines the traffic flow with the secondary information to mine the hidden relationship between the primary and secondary information. The experimental results on two real-world datasets show that the prediction effect of the model set out in the present paper is significantly better than that of the baseline.

Comprehensive information integration network for left atrium segmentation on LGE CMR images

Automatic and accurate segmentation of the left atrium (LA) is a prerequisite for quantifying the LA, and effective LA segmentation is helpful for the clinical diagnosis of patients with atrial fibrillation. The existing LA segmentation methods are prone to a lack of structural prediction of the pulmonary vein and mitral valve, and the structure of the LA is prone to over- and under-segmentation. To solve these problems, we propose a comprehensive information integration network (CII-Net) to segment the LA from late gadolinium-enhanced (LGE) cardiac magnetic resonance (CMR) images. The CII-Net integrates multiscale information from the input stage, high-level semantic information from the bottleneck stage, interaction information from the encoding and decoding stages, and information from the output stage, which can more completely capture the comprehensive image characteristics. We conducted experiments on 154 LGE CMR cases with atrial fibrillation proposed by the organizer of the 2018 atrial segmentation challenge. Compared with state-of-the-art methods, the proposed CII-Net obtains an average DICE score of 91.9%, average Jaccard score of 85.1%, average Hausdorff distance of 5.924 mm, and average symmetric surface distance of 0.993 mm without post-processing, demonstrating the potential for clinical application.

Man-made structures in the marine environment: A review of stakeholders’ social and economic values and perceptions

Man-made marine structures (MMS) are commonly used to describe any artificial structure in the marine environment, encompassing oil and gas infrastructure and pipelines, artificial reefs, jetties, piers and shipwrecks. MMS are increasingly proposed to address issues facing marine planners, including augmenting fish stocks through the creation of artificial reefs and the repurposing of redundant offshore oil and gas infrastructure (‘rigs to reefs’). Marine spatial planning is a highly contested process, characterised by multiple stakeholders with often divergent priorities due to competing objectives and values. Understanding stakeholder perspectives in relation to MMS is therefore critical in formulating appropriate policies. This review presents the first systematic and comprehensive integration of information from academic journals and ‘grey’ literature relating to social and economic values and perceptions of MMS. The review identifies that, despite advocacy for research on social and economic values of MMS, there are significant gaps in knowledge, in particular relating to comparative assessments of stakeholder values across different types of MMS. Priority areas for future research are highlighted.

Design of cooperative update mechanism of national geographic conditions monitoring and basic surveying and mapping

Abstract. National geographic conditions monitoring and basic surveying and mapping are two important tasks of the surveying and mapping department, and they are similar in production organization and technology realization. In the process of operation, both of them need to carry out internal collection, base map production, field verification and so on. It is operationally feasible to carry out cooperative production of national geographic conditions monitoring and basic surveying and mapping. From the perspective of technical process and method, both of them are carried out by combination internal and field work. Firstly, based on remote sensing images and thematic geographic data, the internal work will perform image interpretation and obtain staged results data. Then, the field verification will be carried out to make judgments and adjustments. Finally, the results of the field verification will be transferred back to the internal work, and the data will be further edited and organized in the internal work to obtain the final data.Basic surveying and mapping focuses on abstract representation of the real world, but lacks comprehensive integration of information and in-depth knowledge mining. National geographic condition monitoring focuses on the spatial distribution, characteristics and interrelations of natural and human geographical elements on the surface. There are many differences between basic surveying and mapping and national geographic conditions monitoring in the content and index of data collection, data stratification and element attribute. But basic surveying and mapping results are the basic data for national geographic conditions monitoring and national geographic conditions monitoring data is an important update data source for basic surveying and mapping.On the one hand, part of the geographic information can be updated on the basis of extracting relevant basic geographic information element data and attribute information, On the other hand, timely basic geographic information data can be used as the direct basis for the collection of geographic information.This paper designs the technical methods and workflow of the cooperative update mechanism based on the relevant technical documents of national geographic conditions monitoring and basic surveying and mapping. It will enable one-time acquisition of data needed for the national geographic conditions monitoring and basic surveying and mapping, "one-time collection, classification and utilization". It will save a lot of time and effort, reduce workload and improve productivity.

Holographic Ensemble Forecasting Method for Short-Term Power Load

In this paper, we newly propose a holographic ensemble forecasting method (HEFM). First, we use the mutual information and statistical method to select feature variables, which is an ensemble of information about the cross-border multi-source data at the dataset level. Then, we generate multiple training sets by performing diversity sampling with bootstrap, which is an ensemble of information about multiple sample sets at the sampling space level. Next, we construct a multi-model using different artificial intelligence and machine-learning algorithms, which is an ensemble of information about multiple nonlinear heterogeneous models at the forecasting model level. Finally, we use the original features, the forecasting load which is output of the multiple heterogeneous models trained in the first learning, and the actual load of the recent period before each forecasted time to generate a new training set, which is used for the online second learning and final forecasting. This is an ensemble of information about online second learning at the decision level. The ensemble of multi-category multi-state information for four levels (dataset, sampling space, forecasting model, and decision) constitutes the framework of HEFM, whose essence is a forecasting method with comprehensive information integration for the whole life cycle of the forecasting process. We study the load in Guangzhou, China, and New England, USA. Compared to the state-of-the-art forecasting methods, the MAPE of HEFM is reduced by 7.69%–65.77%. The results demonstrate that the forecasting performance may not be improved with the number of algorithms, and that there is a need to understand the positive and negative fusion effect between different algorithms and data characteristics.

SemRec: a personalized semantic recommendation method based on weighted heterogeneous information networks

Recently heterogeneous information network (HIN) analysis has attracted a lot of attention, and many data mining tasks have been exploited on HIN. As an important data mining task, recommender system includes a lot of object types (e.g., users, movies, actors, and interest groups in movie recommendation) and the rich relations among object types, which naturally constitute a HIN. The comprehensive information integration and rich semantic information of HIN make it promising to generate better recommendations. However, conventional HINs do not consider the attribute values on links, and the widely used meta path in HIN may fail to accurately capture semantic relations among objects, due to the existence of rating scores (usually ranging from 1 to 5) between users and items in recommender system. In this paper, we introduce the weighted HIN and weighted meta path concepts to subtly depict the path semantics through distinguishing different link attribute values. Furthermore, we propose a semantic path based personalized recommendation method SemRec to predict the rating scores of users on items. Through setting meta paths, SemRec not only flexibly integrates heterogeneous information but also obtains prioritized and personalized weights representing user preferences on paths. Experiments on three real datasets illustrate that SemRec achieves better recommendation performance through flexibly integrating information with the help of weighted meta paths. Moreover, extensive experiments validate the benefits of weighted meta paths.

LncRNAWiki: harnessing community knowledge in collaborative curation of human long non-coding RNAs.

Long non-coding RNAs (lncRNAs) perform a diversity of functions in numerous important biological processes and are implicated in many human diseases. In this report we present lncRNAWiki (http://lncrna.big.ac.cn), a wiki-based platform that is open-content and publicly editable and aimed at community-based curation and collection of information on human lncRNAs. Current related databases are dependent primarily on curation by experts, making it laborious to annotate the exponentially accumulated information on lncRNAs, which inevitably requires collective efforts in community-based curation of lncRNAs. Unlike existing databases, lncRNAWiki features comprehensive integration of information on human lncRNAs obtained from multiple different resources and allows not only existing lncRNAs to be edited, updated and curated by different users but also the addition of newly identified lncRNAs by any user. It harnesses community collective knowledge in collecting, editing and annotating human lncRNAs and rewards community-curated efforts by providing explicit authorship based on quantified contributions. LncRNAWiki relies on the underling knowledge of scientific community for collective and collaborative curation of human lncRNAs and thus has the potential to serve as an up-to-date and comprehensive knowledgebase for human lncRNAs.

INDIGO – INtegrated Data Warehouse of MIcrobial GenOmes with Examples from the Red Sea Extremophiles

BackgroundThe next generation sequencing technologies substantially increased the throughput of microbial genome sequencing. To functionally annotate newly sequenced microbial genomes, a variety of experimental and computational methods are used. Integration of information from different sources is a powerful approach to enhance such annotation. Functional analysis of microbial genomes, necessary for downstream experiments, crucially depends on this annotation but it is hampered by the current lack of suitable information integration and exploration systems for microbial genomes.ResultsWe developed a data warehouse system (INDIGO) that enables the integration of annotations for exploration and analysis of newly sequenced microbial genomes. INDIGO offers an opportunity to construct complex queries and combine annotations from multiple sources starting from genomic sequence to protein domain, gene ontology and pathway levels. This data warehouse is aimed at being populated with information from genomes of pure cultures and uncultured single cells of Red Sea bacteria and Archaea. Currently, INDIGO contains information from Salinisphaera shabanensis, Haloplasma contractile, and Halorhabdus tiamatea - extremophiles isolated from deep-sea anoxic brine lakes of the Red Sea. We provide examples of utilizing the system to gain new insights into specific aspects on the unique lifestyle and adaptations of these organisms to extreme environments. ConclusionsWe developed a data warehouse system, INDIGO, which enables comprehensive integration of information from various resources to be used for annotation, exploration and analysis of microbial genomes. It will be regularly updated and extended with new genomes. It is aimed to serve as a resource dedicated to the Red Sea microbes. In addition, through INDIGO, we provide our Automatic Annotation of Microbial Genomes (AAMG) pipeline. The INDIGO web server is freely available at http://www.cbrc.kaust.edu.sa/indigo.

A generative approach for image-based modeling of tumor growth.

Extensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from different image sources and different time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multimodal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.

Integrative content-driven concepts for bioinformatics “beyond the cell”

Bioinformatics has delivered great contributions to genome and genomics research, without which the world-wide success of this and other global ('omics') approaches would not have been possible. More recently, it has developed further towards the analysis of different kinds of networks thus laying the foundation for comprehensive description, analysis and manipulation of whole living systems in modern "systems biology". The next step which is necessary for developing a systems biology that deals with systemic phenomena is to expand the existing and develop new methodologies that are appropriate to characterize intercellular processes and interactions without omitting the causal underlying molecular mechanisms. Modelling the processes on the different levels of complexity involved requires a comprehensive integration of information on gene regulatory events, signal transduction pathways, protein interaction and metabolic networks as well as cellular functions in the respective tissues / organs.