Gene utility recapitulates chromosomal aberrancies in advanced stage neuroblastoma

Ung, Choong Yong; Levee, Taylor M.; Zhang, Cheng; Correia, Cristina; Yeo, Kok Siong; Li, Hu; Zhu, Shizhen

doi:10.1016/j.csbj.2022.06.024

Cited by 2 publications

(3 citation statements)

References 67 publications

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“…We recently proposed the Gene Utility Model (GUM) to identify key disease-relevant genes based on the utilization, function, and interactions of genes in PPI networks. 62 Our computational modeling employs information flow to determine how genes are utilized in physiologic and pathologic states. A recent study using this approach showed that the chromosomal distribution of frequently and infrequently utilized genes can recapitulate chromosomal aberrancies in advanced stages of neuroblastoma, a devastating childhood cancer with few mutations.…”

Section: Systems Biology and Ai Platforms To Decipher Multiorgan Lock...mentioning

confidence: 99%

“…A recent study using this approach showed that the chromosomal distribution of frequently and infrequently utilized genes can recapitulate chromosomal aberrancies in advanced stages of neuroblastoma, a devastating childhood cancer with few mutations. 62 We reason that the GUM can also be applied to identify highly utilized genes involved in molecular networks that are responsible for the coordination of pathways induced by locking factors in highly engaged communicating organs.…”

Section: Systems Biology and Ai Platforms To Decipher Multiorgan Lock...mentioning

confidence: 99%

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Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities

Ung,

Correia,

et al. 2024

Drug Discovery Today

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Section: Systems Biology and Ai Platforms To Decipher Multiorgan Lock...mentioning

confidence: 99%

Section: Systems Biology and Ai Platforms To Decipher Multiorgan Lock...mentioning

confidence: 99%

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities

Ung,

Correia,

et al. 2024

Drug Discovery Today

Self Cite

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“…Another biological concept we proposed recently is the Gene Utility Model (GUM), which states that the significance of a gene in disease etiology hinges on its utility within a protein-protein interaction (PPI) network specific to a certain disease context [42]. Here, we simulated gene utilities using a process-guided algorithm [43] and showed that gene utility profiles capture the patterns of chromosomal aberrancies in advanced-stage neuroblastoma, a childhood cancer with few somatic mutations but that shows enigmatic conserved chromosomal abnormalities [44].…”

Section: Food For Thoughtmentioning

confidence: 99%

The Rise of Hypothesis-Driven Artificial Intelligence in Oncology

Xianyu,

Correia,

Ung

et al. 2024

Cancers

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Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.

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Gene utility recapitulates chromosomal aberrancies in advanced stage neuroblastoma

Cited by 2 publications

References 67 publications

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities

The Rise of Hypothesis-Driven Artificial Intelligence in Oncology

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