Learned Indexing in Proteins: Substituting Complex Distance
Calculations with Embedding and Clustering Techniques

D 2022

Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques

OĽHA, Jaroslav, Terézia SLANINÁKOVÁ, Martin GENDIAR, Matej ANTOL, Vlastislav DOHNAL et. al.

Basic information

Original name

Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques

Authors

OĽHA, Jaroslav (703 Slovakia, belonging to the institution), Terézia SLANINÁKOVÁ (703 Slovakia, belonging to the institution), Martin GENDIAR (703 Slovakia, belonging to the institution), Matej ANTOL (703 Slovakia, belonging to the institution) and Vlastislav DOHNAL (203 Czech Republic, guarantor, belonging to the institution)

Edition

1. vyd. Cham, Similarity Search and Applications, 15th International Conference, SISAP 2022, Bologna, Italy, October 5–7, 2022, Proceedings, p. 274-282, 9 pp. 2022

Publisher

Springer Cham

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

URL

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/22:00126460

Organization unit

Faculty of Informatics

ISBN

978-3-031-17848-1

ISSN

DOI

http://dx.doi.org/10.1007/978-3-031-17849-8_22

UT WoS

000874756300022

Keywords in English

Protein database;Embedding non-vector data;Learned metric index;Similarity search;Machine learning for indexing

Abstract

V originále

Despite the constant evolution of similarity searching research, it continues to face challenges stemming from the complexity of the data, such as the curse of dimensionality and computationally expensive distance functions. Various machine learning techniques have proven capable of replacing elaborate mathematical models with simple linear functions, often gaining speed and simplicity at the cost of formal guarantees of accuracy and correctness of querying. The authors explore the potential of this research trend by presenting a lightweight solution for the complex problem of 3D protein structure search. The solution consists of three steps – (i) transformation of 3D protein structural information into very compact vectors, (ii) use of a probabilistic model to group these vectors and respond to queries by returning a given number of similar objects, and (iii) a final filtering step which applies basic vector distance functions to refine the result.

Links

EF16_019/0000822, research and development project

Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur

LM2018131, research and development project

Name: Česká národní infrastruktura pro biologická data (Acronym: ELIXIR-CZ)

Investor: Ministry of Education, Youth and Sports of the CR, Czech National Infrastructure for Biological Data

LM2018140, research and development project

Name: e-Infrastruktura CZ (Acronym: e-INFRA CZ)

Investor: Ministry of Education, Youth and Sports of the CR

MUNI/A/1195/2021, interní kód MU

Name: Aplikovaný výzkum v oblastech vyhledávání, analýz a vizualizací rozsáhlých dat, zpracování přirozeného jazyka a aplikované umělé inteligence

Investor: Masaryk University

Citovat

OĽHA, Jaroslav, Terézia SLANINÁKOVÁ, Martin GENDIAR, Matej ANTOL and Vlastislav DOHNAL. Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques. In Tomáš Skopal, Fabrizio Falchi, Jakub Lokoč, Maria Luisa Sapino, Ilaria Bartolini, Marco Patella. Similarity Search and Applications, 15th International Conference, SISAP 2022, Bologna, Italy, October 5–7, 2022, Proceedings. 1st ed. Cham: Springer Cham, 2022, p. 274-282. ISBN 978-3-031-17848-1. Available from: https://dx.doi.org/10.1007/978-3-031-17849-8_22.

@inproceedings{2212037,
   author = {Oľha, Jaroslav and Slanináková, Terézia and Gendiar, Martin and Antol, Matej and Dohnal, Vlastislav},
   address = {Cham},
   booktitle = {Similarity Search and Applications, 15th International Conference, SISAP 2022, Bologna, Italy, October 5–7, 2022, Proceedings},
   doi = {http://dx.doi.org/10.1007/978-3-031-17849-8_22},
   edition = {1},
   editor = {Tomáš Skopal, Fabrizio Falchi, Jakub Lokoč, Maria Luisa Sapino, Ilaria Bartolini, Marco Patella},
   keywords = {Protein database;Embedding non-vector data;Learned metric index;Similarity search;Machine learning for indexing},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-031-17848-1},
   pages = {274-282},
   publisher = {Springer Cham},
   title = {Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques},
   url = {https://link.springer.com/chapter/10.1007/978-3-031-17849-8_22},
   year = {2022}
}

TY  - JOUR
ID  - 2212037
AU  - Oľha, Jaroslav - Slanináková, Terézia - Gendiar, Martin - Antol, Matej - Dohnal, Vlastislav
PY  - 2022
TI  - Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques
PB  - Springer Cham
CY  - Cham
SN  - 9783031178481
KW  - Protein database;Embedding non-vector data;Learned metric index;Similarity search;Machine learning for indexing
UR  - https://link.springer.com/chapter/10.1007/978-3-031-17849-8_22
N2  - Despite the constant evolution of similarity searching research, it continues to face challenges stemming from the complexity of the data, such as the curse of dimensionality and computationally expensive distance functions. Various machine learning techniques have proven capable of replacing elaborate mathematical models with simple linear functions, often gaining speed and simplicity at the cost of formal guarantees of accuracy and correctness of querying. The authors explore the potential of this research trend by presenting a lightweight solution for the complex problem of 3D protein structure search. The solution consists of three steps – (i) transformation of 3D protein structural information into very compact vectors, (ii) use of a probabilistic model to group these vectors and respond to queries by returning a given number of similar objects, and (iii) a final filtering step which applies basic vector distance functions to refine the result.
ER  -

OĽHA, Jaroslav, Terézia SLANINÁKOVÁ, Martin GENDIAR, Matej ANTOL and Vlastislav DOHNAL. Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques. In Tomáš Skopal, Fabrizio Falchi, Jakub Lokoč, Maria Luisa Sapino, Ilaria Bartolini, Marco Patella. \textit{Similarity Search and Applications, 15th International Conference, SISAP 2022, Bologna, Italy, October 5–7, 2022, Proceedings}. 1st ed. Cham: Springer Cham, 2022, p.~274-282. ISBN~978-3-031-17848-1. Available from: https://dx.doi.org/10.1007/978-3-031-17849-8\_{}22.

Detailed Information on Publication Record