SimExact – An Efficient Method to Compute Function Similarity Between Proteins Using Gene Ontology

Background: The rapidly growing protein and annotation databases necessitate the development of efficient tools to process this valuable information. Biologists frequently need to find proteins similar to a given protein, for which BLAST tools are commonly used. With the development of biomedical ontologies, e.g. Gene Ontology, methods were designed to measure function (semantic) similarity between two proteins. These methods work well on protein pairs, but are not suitable for protein query processing. Objective: Our aim is to facilitate searching of similar proteins in an acceptable time. Methods: A novel method SimExact for high speed searching of functionally similar proteins has been proposed. Results: The experiments of this study show that SimExact gives correct results required for protein searching. A fully functional prototype of an online tool (www.datafurnish.com/protsem.php) has been provided that generates a ranked list of the proteins similar to a query protein, with a response time of less than 20 seconds in our setup. SimExact was used to search for protein pairs having high disparity between function similarity and sequence similarity. Conclusion: SimExact makes such searches practical, which would not be possible in a reasonable time otherwise.

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A novel method to quantify gene set functional association based on gene ontology

Journal of The Royal Society Interface ◽

10.1098/rsif.2011.0551 ◽

2011 ◽

Vol 9 (70) ◽

pp. 1063-1072 ◽

Cited By ~ 22

Author(s):

Sali Lv ◽

Yan Li ◽

Qianghu Wang ◽

Shangwei Ning ◽

Teng Huang ◽

...

Keyword(s):

Gene Ontology ◽

Genetic Basis ◽

Protein Complexes ◽

Sequence Similarity ◽

Functional Association ◽

Future Studies ◽

Gene Set ◽

Complex Disorders ◽

Gene Sets ◽

Novel Method

Numerous gene sets have been used as molecular signatures for exploring the genetic basis of complex disorders. These gene sets are distinct but related to each other in many cases; therefore, efforts have been made to compare gene sets for studies such as those evaluating the reproducibility of different experiments. Comparison in terms of biological function has been demonstrated to be helpful to biologists. We improved the measurement of semantic similarity to quantify the functional association between gene sets in the context of gene ontology and developed a web toolkit named Gene Set Functional Similarity (GSFS; http://bioinfo.hrbmu.edu.cn/GSFS ). Validation based on protein complexes for which the functional associations are known demonstrated that the GSFS scores tend to be correlated with sequence similarity scores and that complexes with high GSFS scores tend to be involved in the same functional catalogue. Compared with the pairwise method and the annotation method, the GSFS shows better discrimination and more accurately reflects the known functional catalogues shared between complexes. Case studies comparing differentially expressed genes of prostate tumour samples from different microarray platforms and identifying coronary heart disease susceptibility pathways revealed that the method could contribute to future studies exploring the molecular basis of complex disorders.

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Additively Manufactured Parts Made of a Polymer Material Used for the Experimental Verification of a Component of a High-Speed Machine with an Optimised Geometry—Preliminary Research

Polymers ◽

10.3390/polym13010137 ◽

2020 ◽

Vol 13 (1) ◽

pp. 137

Author(s):

Artur Andrearczyk ◽

Bartlomiej Konieczny ◽

Jerzy Sokołowski

Keyword(s):

Polymer Material ◽

High Speed ◽

Numerical Models ◽

Flow Analysis ◽

Experimental Tests ◽

Strength Analysis ◽

Compressor Wheel ◽

Operational Characteristics ◽

3D Printed ◽

Novel Method

This paper describes a novel method for the experimental validation of numerically optimised turbomachinery components. In the field of additive manufacturing, numerical models still need to be improved, especially with the experimental data. The paper presents the operational characteristics of a compressor wheel, measured during experimental research. The validation process included conducting a computational flow analysis and experimental tests of two compressor wheels: The aluminium wheel and the 3D printed wheel (made of a polymer material). The chosen manufacturing technology and the results obtained made it possible to determine the speed range in which the operation of the tested machine is stable. In addition, dynamic destructive tests were performed on the polymer disc and their results were compared with the results of the strength analysis. The tests were carried out at high rotational speeds (up to 120,000 rpm). The results of the research described above have proven the utility of this technology in the research and development of high-speed turbomachines operating at speeds up to 90,000 rpm. The research results obtained show that the technology used is suitable for multi-variant optimization of the tested machine part. This work has also contributed to the further development of numerical models.

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Application of Arithmetic Operations Method in Single Pair-Pole Magnetic Encoder

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.128-129.85 ◽

2011 ◽

Vol 128-129 ◽

pp. 85-91

Author(s):

Yi Fan Zeng ◽

Rui Li

Keyword(s):

High Speed ◽

Angle Measurement ◽

Single Pair ◽

Nonlinear Correction ◽

Arithmetic Operations ◽

Rotational Angle ◽

Voltage Signal ◽

Fpga Chip ◽

Novel Method ◽

Hall Sensors

This paper proposes a novel method called arithmetic operations to analyze and process the generated voltage-signal from the single pair-pole magnetic encoder. Dual orthogonal voltage-signals are generated by two vertical hall sensors which are placed in the bottom of a columned magnet. When signals pass A/D converter, the quadrant determination, arithmetic operations and nonlinear correction in FPGA chip are performed before the values of rotational angle are displayed on the LED. This paper also designs and implements the single pair-pole magnetic encoder which has advantages such as high-speed, high-resolution and high-accuracy in the area of angle measurement.

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Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

Journal of Manufacturing and Materials Processing ◽

10.3390/jmmp2040072 ◽

2018 ◽

Vol 2 (4) ◽

pp. 72 ◽

Cited By ~ 5

Author(s):

German Terrazas ◽

Giovanna Martínez-Arellano ◽

Panorios Benardos ◽

Svetan Ratchev

Keyword(s):

Tool Wear ◽

High Speed ◽

Big Data Analytics ◽

Design Development ◽

Online Tool ◽

Overall Evaluation ◽

Intelligent Information ◽

Key Enabling Technologies ◽

Processing Techniques ◽

Tool Wear Classification

The new generation of ICT solutions applied to the monitoring, adaptation, simulation and optimisation of factories are key enabling technologies for a new level of manufacturing capability and adaptability in the context of Industry 4.0. Given the advances in sensor technologies, factories, as well as machine tools can now be sensorised, and the vast amount of data generated can be exploited by intelligent information processing techniques such as machine learning. This paper presents an online tool wear classification system built in terms of a monitoring infrastructure, dedicated to perform dry milling on steel while capturing force signals, and a computing architecture, assembled for the assessment of the flank wear based on deep learning. In particular, this approach demonstrates that a big data analytics method for classification applied to large volumes of continuously-acquired force signals generated at high speed during milling responds sufficiently well when used as an indicator of the different stages of tool wear. This research presents the design, development and deployment of the system components and an overall evaluation that involves machining experiments, data collection, training and validation, which, as a whole, has shown an accuracy of 78 % .

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HomoKinase: A Curated Database of Human Protein Kinases

ISRN Computational Biology ◽

10.1155/2013/417634 ◽

2013 ◽

Vol 2013 ◽

pp. 1-5 ◽

Cited By ~ 11

Author(s):

Suresh Subramani ◽

Saranya Jayapalan ◽

Raja Kalpana ◽

Jeyakumar Natarajan

Keyword(s):

Gene Ontology ◽

Protein Kinases ◽

Kinase Activity ◽

Query Protein ◽

Amino Acid Sequences ◽

Human Protein ◽

Biological Information ◽

Functional Domain ◽

Comprehensive Collection ◽

Search Tool

HomoKinase database is a comprehensive collection of curated human protein kinases and their relevant biological information. The entries in the database are curated by three criteria: HGNC approval, gene ontology-based biological process (protein phosphorylation), and molecular function (ATP binding and kinase activity). For a given query protein kinase name, the database provides its official symbol, full name, other known aliases, amino acid sequences, functional domain, gene ontology, pathways assignments, and drug compounds. In addition, as a search tool, it enables the retrieval of similar protein kinases with specific family, subfamily, group, and domain combinations and tabulates the information. The present version contains 498 curated human protein kinases and links to other popular databases.

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