Molecular Cavity Topological Representation for Pattern Analysis: A NLP Analogy-Based Word2Vec Method.

Dongliang Guo, Qiaoqiao Wang, Meng Liang, Wei Liu, Junlan Nie

Abstract read

In one paragraph

Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

MicroRNA-34a: the bad guy in age-related vascular diseases.Cellular and molecular life sciences : CMLS · 2021
Review

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Dongliang GuoSchool of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.ORCID 0000-0001-9935-7373

Qiaoqiao WangSchool of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.ORCID 0000-0003-0284-5077

Meng LiangSchool of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.

Wei LiuAdvanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney, Ultimo NSW 2007, Australia.

Junlan NieSchool of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.

Funding

the National Science Foundation of China 61802334

6 · The paper itself

Abstract

Cavity analysis in molecular dynamics is important for understanding molecular function. However, analyzing the dynamic pattern of molecular cavities remains a difficult task. In this paper, we propose a novel method to topologically represent molecular cavities by vectorization. First, a characterization of cavities is established through Word2Vec model, based on an analogy between the cavities and natural language processing (NLP) terms. Then, we use some techniques such as dimension reduction and clustering to conduct an exploratory analysis of the vectorized molecular cavity. On a real data set, we demonstrate that our approach is applicable to maintain the topological characteristics of the cavity and can find the change patterns from a large number of cavities.

Indexed as

AlgorithmsCluster AnalysisDatabases, ProteinDrug DiscoveryHumansLigandsMolecular Dynamics SimulationNatural Language ProcessingProtein ConformationProteinsSoftwareLigandsProteinsanalogy-based methodsmolecular cavitytopological representationWord2Vec model

Identifiers

PMID31795343

DOI10.3390/ijms20236019

PMCPMC6928730

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.