2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.

Qi-Shi Du, Shu-Qing Wang, Neng-Zhong Xie, Qing-Yan Wang, Ri-Bo Huang, Kuo-Chen Chou

Abstract read

In one paragraph

Article in Oncotarget, 2017. 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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.

iRSpot-Pse6NC: Identifying recombination spots inInternational journal of biological sciences · 2018
Article

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

6 authors.

Qi-Shi DuState Key Laboratory of China for Biomass Energy Enzyme Technology, National Engineering Research Center of China for Non-Food Biorefinery, Guangxi Academy of Sciences, Nanning 530007, China.

Shu-Qing WangSchool of Pharmacy, Tianjin Medical University, Tianjin 300070, China.

Neng-Zhong XieState Key Laboratory of China for Biomass Energy Enzyme Technology, National Engineering Research Center of China for Non-Food Biorefinery, Guangxi Academy of Sciences, Nanning 530007, China.

Qing-Yan WangState Key Laboratory of China for Biomass Energy Enzyme Technology, National Engineering Research Center of China for Non-Food Biorefinery, Guangxi Academy of Sciences, Nanning 530007, China.

Ri-Bo HuangState Key Laboratory of China for Biomass Energy Enzyme Technology, National Engineering Research Center of China for Non-Food Biorefinery, Guangxi Academy of Sciences, Nanning 530007, China.

Kuo-Chen ChouCenter for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.

Indexed as

drug designmolecular fragmentsPCApeptidesphysicochemical properties

Identifiers

PMID29050302

DOI10.18632/oncotarget.19757

PMCPMC5642577

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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.