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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00126460" target="_blank" >RIV/00216224:14330/22:00126460 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-17849-8_22" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-17849-8_22</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-17849-8_22" target="_blank" >10.1007/978-3-031-17849-8_22</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Similarity Search and Applications, 15th International Conference, SISAP 2022, Bologna, Italy, October 5–7, 2022, Proceedings

ISBN

9783031178481

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

9

Strana od-do

274-282

Název nakladatele

Springer Cham

Místo vydání

Cham

Místo konání akce

Bologna, Italy

Datum konání akce

5. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000874756300022