Evidence map›Paper›PMID 38043609›Full record

ArticleJournal of advanced research2024

An interpretable artificial intelligence framework for designing synthetic lethality-based anti-cancer combination therapies.

Jing Wang, Yuqi Wen, Yixin Zhang, Zhongming Wang, Yuyang Jiang, Chong Dai, Lianlian Wu, Dongjin Leng, Song He, Xiaochen Bo

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of advanced research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.4field-weighted citation impact, top 15% of its field
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

6 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
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  3. Article
  4. Review
  5. Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025
    Review
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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 at 5 institutions in 3 countries.

Jing WangSchool of Medicine, Tsinghua University, Beijing, 100084, China.
Yuqi WenDepartment of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing, 100850, China.
Yixin ZhangDepartment of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing, 100850, China.
Zhongming WangAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China.
Yuyang JiangAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China.
Chong DaiCollege of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
Lianlian WuAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China.
Dongjin LengDepartment of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing, 100850, China.
Song HeDepartment of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing, 100850, China. Electronic address: hes1224@163.com.
Xiaochen BoDepartment of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing, 100850, China. Electronic address: boxiaoc@163.com.
Tianjin University · CNBeijing University of Chemical Technology · CNBioinformatics Institute · SGInstitute for Transfusion Medicine · USTsinghua University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionSynthetic lethality (SL) provides an opportunity to leverage different genetic interactions when designing synergistic combination therapies. To further explore SL-based combination therapies for cancer treatment, it is important to identify and mechanistically characterize more SL interactions. Artificial intelligence (AI) methods have recently been proposed for SL prediction, but the results of these models are often not interpretable such that deriving the underlying mechanism can be challenging.

objectivesThis study aims to develop an interpretable AI framework for SL prediction and subsequently utilize it to design SL-based synergistic combination therapies.

methodsWe propose a knowledge and data dual-driven AI framework for SL prediction (KDDSL). Specifically, we use gene knowledge related to the SL mechanism to guide the construction of the model and develop a method to identify the most relevant gene knowledge for the predicted results.

resultsExperimental and literature-based validation confirmed a good balance between predictive and interpretable ability when using KDDSL. Moreover, we demonstrated that KDDSL could help to discover promising drug combinations and clarify associated biological processes, such as the combination of MDM2 and CDK9 inhibitors, which exhibited significant anti-cancer effects in vitro and in vivo.

conclusionThese data underscore the potential of KDDSL to guide SL-based combination therapy design. There is a need for biomedicine-focused AI strategies to combine rational biological knowledge with developed models.

Indexed as

Artificial IntelligenceNeoplasmsSynthetic Lethal MutationsAnimalsAntineoplastic AgentsAntineoplastic Combined Chemotherapy ProtocolsCell Line, TumorHumansMiceAntineoplastic AgentsArtificial intelligenceCombination therapyData-drivenKnowledge-drivenSynthetic lethality

Identifiers

PMID38043609
PMCPMC11519055
OpenAlexW4389265672

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.