Identifying novel genes and chemicals related to nasopharyngeal cancer in a heterogeneous network

Li, Zhandong; An, Lê Văn; Li, Hao; Wang, Shaopeng; Yuan, Fei; Li, Lin

doi:10.1038/srep25515

Cited by 8 publications

(5 citation statements)

References 55 publications

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“…The second challenge is the integration of heterogeneous information 103 . Although deep learning models facilitate the integration of multimodal biological data, it is still difficult to build up a universal deep learning model due to the lack of biological domain knowledge 200 .…”

Section: Discussionmentioning

confidence: 99%

“…The shortest path algorithm has been widely used to determine regulatory paths in cancer networks 103 , 104 and then discover the key targets on the paths 105 . For example, Li et al 106 first identified a set of six genes that can distinguish colorectal tumours from normal adjacent tissues using the maximum relevance minimum redundancy approach 107 .…”

Section: The Principles and Theories For Commonly Used Artificial Int...mentioning

confidence: 99%

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Artificial intelligence in cancer target identification and drug discovery

You

Lai

Pan

et al. 2022

Sig Transduct Target Ther

161

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Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

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Section: Discussionmentioning

confidence: 99%

Section: The Principles and Theories For Commonly Used Artificial Int...mentioning

confidence: 99%

Artificial intelligence in cancer target identification and drug discovery

You

Lai

Pan

et al. 2022

Sig Transduct Target Ther

161

View full text Add to dashboard Cite

show abstract

“…The difficulties are the lack of reliable data for validation, integration of disparate information. and DL models that are difficult to understand [66][67][68]. Although there has been significant improvement in this field, a lot more progress is required.…”

Section: Ai In Drug Target Identificationmentioning

confidence: 99%

Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications

Dhudum,

Ganeshpurkar,

Pawar

2024

DDC

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The drug discovery and development process is very lengthy, highly expensive, and extremely complex in nature. Considering the time and cost constraints associated with conventional drug discovery, new methods must be found to enhance the declining efficiency of traditional approaches. Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Advancements in AI and machine learning (ML) techniques have revolutionized their applications to drug discovery and development. This review illuminates the profound influence of AI on diverse aspects of drug discovery, encompassing drug-target identification, molecular properties, compound analysis, drug development, quality assurance, and drug toxicity assessment. ML algorithms play an important role in testing systems and can predict important aspects such as the pharmacokinetics and toxicity of drug candidates. This review not only strengthens the theoretical foundation and development of this technology, but also explores the myriad challenges and promising prospects of AI in drug discovery and development. The combination of AI and drug discovery offers a promising strategy to overcome the challenges and complexities of the pharmaceutical industry.

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“…Nasopharyngeal carcinoma (also known as NPC) is a rare tumor originating in the nasopharynx and occurring in the head and neck. 1,2 Environment and diet are important to influence factors in the development of NPC. 3,4 In the treatment of NPC, the improvement of intensive radiotherapy has significantly improved the survival rate of patients with NPC.…”

Section: Introductionmentioning

confidence: 99%

Circ_0000285 regulates nasopharyngeal carcinoma progression through miR-1278/FNDC3B axis

Zeng

et al. 2023

Hum Exp Toxicol

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Background Nasopharyngeal carcinoma (NPC) is cancer with high mortality and poor prognosis. Circular RNAs (circRNAs) have been identified in a wide variety of cancers. But the functional mechanism of circ_000285 in NPC remains unclear. Purpose To decipher the biological function and molecular mechanism of circ_000285 in NPC. Methods Quantitative PCR (RT-qPCR) was applied for detecting the expression of circ_0000285, miR-1278, and FNDC3B. Western blot was used to measure the protein levels of Fibronectin type III domain containing 3B (FNDC3B), Bcl2 associated X (Bax), and B cell leukemia/lymphoma 2 (Bcl2). Cell proliferation, migration, and invasion were analyzed by colony formation, 5-ethynyl-2′-deoxyuridine (EdU), and transwell assays. Cell apoptosis was detected by flow cytometry assays. ELISA assay was used to analyze Caspase-3 activity. Bioinformatics was used to predict, and the target relationship between miR-1278 and circ_0000285 or FNDC3B was verified by luciferase reporter assay. Tumor xenograft models were established to examine how circ_0000285 functions during the mediation of NPC tumor growth in vivo. Results Increased circ_0000285 and FNDC3B expressions, and a decreased miR-1278 expression were observed in NPC tissues and cell lines. Knockdown of circ_0000285 inhibited NPC cell proliferation, migration, invasion, and while promoting NPC cell apoptosis in vitro. Circ_0000285 knockdown-mediated anti-tumor effects in NPC cells could be largely reversed by silencing of miR-1278 or overexpression of FNDC3B. Circ_0000285 could up-regulate FNDC3B expression by sponging miR-1278 in NPC cells. Knockdown of circ_0000285 could inhibit tumor growth in vivo. Conclusion Circ_0000285 upregulates FNDC3B expression by adsorbing miR-1278 to promote NPC development.

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Identifying novel genes and chemicals related to nasopharyngeal cancer in a heterogeneous network

Cited by 8 publications

References 55 publications

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence in cancer target identification and drug discovery

Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications

Circ_0000285 regulates nasopharyngeal carcinoma progression through miR-1278/FNDC3B axis

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