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.

Základní údaje

Originální název

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

Autoři

OĽHA, Jaroslav (703 Slovensko, domácí), Terézia SLANINÁKOVÁ (703 Slovensko, domácí), Martin GENDIAR (703 Slovensko, domácí), Matej ANTOL (703 Slovensko, domácí) a Vlastislav DOHNAL (203 Česká republika, garant, domácí)

Vydání

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

Nakladatel

Springer Cham

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

URL

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/22:00126460

Organizační jednotka

Fakulta informatiky

ISBN

978-3-031-17848-1

ISSN

DOI

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

UT WoS

000874756300022

Klíčová slova anglicky

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

Štítky

core_B, DISA, firank_B, LMI

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 16. 8. 2023 13:25, RNDr. Pavel Šmerk, Ph.D.

Anotace

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.

Návaznosti

EF16_019/0000822, projekt VaV

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

LM2018131, projekt VaV

Název: Česká národní infrastruktura pro biologická data (Akronym: ELIXIR-CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Česká národní infrastruktura pro biologická data

LM2018140, projekt VaV

Název: e-Infrastruktura CZ (Akronym: e-INFRA CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, e-Infrastruktura CZ

MUNI/A/1195/2021, interní kód MU

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

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

Citovat

OĽHA, Jaroslav, Terézia SLANINÁKOVÁ, Martin GENDIAR, Matej ANTOL a 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. 1. vyd. Cham: Springer Cham, 2022, s. 274-282. ISBN 978-3-031-17848-1. Dostupné z: 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 a 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}. 1. vyd. Cham: Springer Cham, 2022, s.~274-282. ISBN~978-3-031-17848-1. Dostupné z: https://dx.doi.org/10.1007/978-3-031-17849-8\_{}22.

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