Evidence map›Paper›PMID 39338392›Full record

ArticlePharmaceuticals (Basel, Switzerland)2024

Syn-COM: A Multi-Level Predictive Synergy Framework for Innovative Drug Combinations.

Yinli Shi, Jun Liu, Shuang Guan, Sicun Wang, Chengcheng Yu, Yanan Yu, Bing Li, Yingying Zhang, Weibin Yang, Zhong Wang

Abstract read
In one paragraph

Article in Pharmaceuticals (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. 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

10 authors.

Yinli ShiInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.ORCID 0000-0001-7186-2503
Jun LiuInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
Shuang GuanInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
Sicun WangInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
Chengcheng YuInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
Yanan YuInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
Bing LiInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China.
Yingying ZhangDongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China.
Weibin YangGraduate School of China Academy of Chinese Medical Sciences, Beijing 100027, China.
Zhong WangInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.

Funding

Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences CI2023C063YLLthe National Major Scientific and Technological Special Project for Significant New Drugs De-velopment 2017ZX09301059the the Open Research Fund of Chengdu University of Traditional Chinese Medicine Key Labor-atory of Systematic Research of Distinctive Chinese Medicine Resources in Southwest China
6 · The paper itself

Abstract

Drug prediction and treatment using bioinformatics and large-scale modeling have emerged as pivotal research areas. This study proposes a novel multi-level collaboration framework named Syn-COM for feature extraction and data integration of diseases and drugs. The framework aims to explore optimal drug combinations and interactions by integrating molecular virtuality, similarity clustering, overlap area, and network distance. It uniquely combines the characteristics of Chinese herbal medicine with clinical experience and innovatively assesses drug interaction and correlation through a synergy matrix. Gouty arthritis (GA) was used as a case study to validate the framework's reliability, leading to the identification of an effective drug combination for GA treatment, comprising

Indexed as

artificial intelligencedrug combinationformula designgouty arthritissimilarity clusteringsynergy index

Identifiers

PMID39338392
PMCPMC11434649

What OpenQuestion holds

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Registered trials

None linked

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.