scholarly journals Integrating Multiple Interaction Networks for Gene Function Inference

Molecules ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 30 ◽  
Author(s):  
Jingpu Zhang ◽  
Lei Deng
Keyword(s):  
State Of The Art ◽  
Interaction Networks ◽  
Dimensional Representation ◽  
Functional Interaction ◽  
High Throughput Data ◽  
Multiple Networks ◽  
The Past ◽  
Proteomic Data ◽  
Multiple Interaction ◽  
Low Dimensional

In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions.

scholarly journals Integrating multi-network topology for gene function prediction using deep neural networks

2019 ◽  
Author(s):  
Hansheng Xue ◽  
Jiajie Peng ◽  
Xuequn Shang
Keyword(s):  
Neural Network ◽  
Gene Function ◽  
State Of The Art ◽  
Feature Learning ◽  
Function Prediction ◽  
Feature Representation ◽  
Superior Performance ◽  
Gene Function Prediction ◽  
Multiple Networks ◽  
Low Dimensional

AbstractMotivationThe emerging of abundant biological networks, which benefit from the development of advanced high-throughput techniques, contribute to describing and modeling complex internal interactions among biological entities such as genes and proteins. Multiple networks provide rich information for inferring the function of genes or proteins. To extract functional patterns of genes based on multiple heterogeneous networks, network embedding-based methods, aiming to capture non-linear and low-dimensional feature representation based on network biology, have recently achieved remarkable performance in gene function prediction. However, existing methods mainly do not consider the shared information among different networks during the feature learning process. Thus, we propose a novel multi-networks embedding-based function prediction method based on semi-supervised autoencoder and feature convolution neural network, named DeepMNE-CNN, which captures complex topological structures of multi-networks and takes the correlation among multi-networks into account.ResultsWe design a novel semi-supervised autoencoder method to integrate multiple networks and generate a low-dimensional feature representation. Then we utilize a convolutional neural network based on the integrated feature embedding to annotate unlabeled gene functions. We test our method on both yeast and human dataset and compare with four state-of-the-art methods. The results demonstrate the superior performance of our method over four state-of-the-art algorithms. From the future explorations, we find that semi-supervised autoencoder based multi-networks integration method and CNN-based feature learning methods both contribute to the task of function prediction.AvailabilityDeepMNE-CNN is freely available at https://github.com/xuehansheng/DeepMNE-CNN

A Macintosh Interface for the Cameca Camebax-Micro Electron Microprobe

1990 ◽  
Vol 48 (1) ◽  
pp. 438-439
Author(s):  
Carl E. Henderson
Keyword(s):  
Electron Microprobe ◽  
Processing Speed ◽  
Word Processing ◽  
State Of The Art ◽  
Computer Control ◽  
Third Party ◽  
Disk Storage ◽  
The Past ◽  
User Facility ◽  
Instrument Control

Over the past few years it has become apparent in our multi-user facility that the computer system and software supplied in 1985 with our CAMECA CAMEBAX-MICRO electron microprobe analyzer has the greatest potential for improvement and updating of any component of the instrument. While the standard CAMECA software running on a DEC PDP-11/23+ computer under the RSX-11M operating system can perform almost any task required of the instrument, the commands are not always intuitive and can be difficult to remember for the casual user (of which our laboratory has many). Given the widespread and growing use of other microcomputers (such as PC’s and Macintoshes) by users of the microprobe, the PDP has become the “oddball” and has also fallen behind the state-of-the-art in terms of processing speed and disk storage capabilities. Upgrade paths within products available from DEC are considered to be too expensive for the benefits received. After using a Macintosh for other tasks in the laboratory, such as instrument use and billing records, word processing, and graphics display, its unique and “friendly” user interface suggested an easier-to-use system for computer control of the electron microprobe automation. Specifically a Macintosh IIx was chosen for its capacity for third-party add-on cards used in instrument control.

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

scholarly journals Inflammation, epigenetics, and metabolism converge to cell senescence and ageing: the regulation and intervention

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Xudong Zhu ◽  
Zhiyang Chen ◽  
Weiyan Shen ◽  
Gang Huang ◽  
John M. Sedivy ◽  
...  
Keyword(s):  
Cell Fate ◽  
Cellular Response ◽  
State Of The Art ◽  
Cell Senescence ◽  
Therapeutic Interventions ◽  
Cell Fate Decision ◽  
The Past ◽  
Fate Decision ◽  
The Individual ◽  
Remarkable Progress

AbstractRemarkable progress in ageing research has been achieved over the past decades. General perceptions and experimental evidence pinpoint that the decline of physical function often initiates by cell senescence and organ ageing. Epigenetic dynamics and immunometabolic reprogramming link to the alterations of cellular response to intrinsic and extrinsic stimuli, representing current hotspots as they not only (re-)shape the individual cell identity, but also involve in cell fate decision. This review focuses on the present findings and emerging concepts in epigenetic, inflammatory, and metabolic regulations and the consequences of the ageing process. Potential therapeutic interventions targeting cell senescence and regulatory mechanisms, using state-of-the-art techniques are also discussed.

scholarly journals Security assurance cases—state of the art of an emerging approach

2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Mazen Mohamad ◽  
Jan-Philipp Steghöfer ◽  
Riccardo Scandariato
Keyword(s):  
State Of The Art ◽  
Tool Support ◽  
Security Assurance ◽  
The Past ◽  
Depth Analysis ◽  
Structured Argumentation ◽  
Security Properties ◽  
Standards And Regulations ◽  
Assurance Cases ◽  
Security Standards

AbstractSecurity Assurance Cases (SAC) are a form of structured argumentation used to reason about the security properties of a system. After the successful adoption of assurance cases for safety, SAC are getting significant traction in recent years, especially in safety-critical industries (e.g., automotive), where there is an increasing pressure to be compliant with several security standards and regulations. Accordingly, research in the field of SAC has flourished in the past decade, with different approaches being investigated. In an effort to systematize this active field of research, we conducted a systematic literature review (SLR) of the existing academic studies on SAC. Our review resulted in an in-depth analysis and comparison of 51 papers. Our results indicate that, while there are numerous papers discussing the importance of SAC and their usage scenarios, the literature is still immature with respect to concrete support for practitioners on how to build and maintain a SAC. More importantly, even though some methodologies are available, their validation and tool support is still lacking.

scholarly journals The state of the art in soybean transcriptomics resources and gene coexpression networks

2021 ◽  
Author(s):  
Fabricio Almeida-Silva ◽  
Kanhu C Moharana ◽  
Thiago M Venancio
Keyword(s):  
State Of The Art ◽  
The State ◽  
Gene Coexpression Network ◽  
Rna Seq ◽  
Transcriptomic Data ◽  
The Past ◽  
Gene Coexpression ◽  
Genomics Research ◽  
Public Repositories ◽  
Coexpression Networks

Abstract In the past decade, over 3000 samples of soybean transcriptomic data have accumulated in public repositories. Here, we review the state of the art in soybean transcriptomics, highlighting the major microarray and RNA-seq studies that investigated soybean transcriptional programs in different tissues and conditions. Further, we propose approaches for integrating such big data using gene coexpression network and outline important web resources that may facilitate soybean data acquisition and analysis, contributing to the acceleration of soybean breeding and functional genomics research.

scholarly journals 14-3-3 phosphoprotein interaction networks – does isoform diversity present functional interaction specification?

2012 ◽  
Vol 3 ◽  
Author(s):  
Anna-Lisa Paul ◽  
Fiona C. Denison ◽  
Eric R. Schultz ◽  
Agata K. Zupanska ◽  
Robert J. Ferl
Keyword(s):  
Interaction Networks ◽  
Functional Interaction ◽  
Isoform Diversity

scholarly journals Beyond Mindfulness

Open Theology ◽  
2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Thomas G. Plante
Keyword(s):  
Research Evidence ◽  
State Of The Art ◽  
Empirical Studies ◽  
Religion And Spirituality ◽  
Spiritual Values ◽  
Quality Research ◽  
The Past ◽  
Enormous Amount ◽  
Consulting Psychology ◽  
Areas Of Interest

AbstractSince the publication of Bergin’s classic 1980 paper “Psychotherapy and Religious Values” in the Journal of Clinical and Consulting Psychology, an enormous amount of quality research has been conducted on the integration of religious and spiritual values and perspectives into the psychotherapy endeavor. Numerous empirical studies, chapters, books, blogs, and specialty organizations have emerged in the past 35 years that have helped researchers and clinicians alike come to appreciate the value of religion and spirituality in the psychotherapeutic process. While so much has been accomplished in this area of integration, so much more needs to occur in order for the psychotherapeutic world to benefit from the wisdom of the great religious and spiritual traditions and values. While state-of-the-art quality research has and continues to demonstrate how religious and spiritual practices and values can be used effectively to enhance the benefits of behavioral and psychological interventions, too often the field either gets overly focused on particular and perhaps trendy areas of interest (e.g., mindfulness) or fails to appreciate and incorporate the research evidence supporting (or not supporting) the use of certain religiously or spiritually informed assessments and interventions. The purpose of this article is to reflect on where the field integrating religion, spirituality and psychotherapy has evolved through the present and where it still needs to go in the future. In doing so I hope to reflect on the call for integration that Bergin highlights in his classic 1980 paper.

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