Artificial Intelligence on High Throughput Data for Biomedical Research

Liang, Stanley

doi:10.2174/138620732407210504084747

Cited by 7 publications

(6 citation statements)

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“…Additionally, the lack of sufficient knowledge regarding the pathogenesis of MS has impeded the progress of treatment options. Through the use of large-scale data, bioinformatics techniques offer a thorough knowledge of numerous illnesses at the molecular level [ 14 , 15 ]. Moreover, it is also particularly important for identifying potential biomarkers for the diagnosis and prognosis of human diseases [ 16 , 17 ].…”

Section: Discussionmentioning

confidence: 99%

Exploration of potential shared gene signatures between periodontitis and multiple sclerosis

Wu,

Cheng,

Zhang

et al. 2024

BMC Oral Health

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Background Although periodontitis has previously been reported to be linked with multiple sclerosis (MS), but the molecular mechanisms and pathological interactions between the two remain unclear. This study aims to explore potential crosstalk genes and pathways between periodontitis and MS. Methods Periodontitis and MS data were obtained from the Gene Expression Omnibus (GEO) database. Shared genes were identified by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Then, enrichment analysis for the shared genes was carried out by multiple methods. The least absolute shrinkage and selection operator (LASSO) regression was used to obtain potential shared diagnostic genes. Furthermore, the expression profile of 28 immune cells in periodontitis and MS was examined using single-sample GSEA (ssGSEA). Finally, real-time quantitative fluorescent PCR (qRT-PCR) and immune histochemical staining were employed to validate Hub gene expressions in periodontitis and MS samples. Results FAM46C, SLC7A7, LY96, CFI, DDIT4L, CD14, C5AR1, and IGJ genes were the shared genes between periodontitis, and MS. GO analysis revealed that the shared genes exhibited the greatest enrichment in response to molecules of bacterial origin. LASSO analysis indicated that CFI, DDIT4L, and FAM46C were the most effective shared diagnostic biomarkers for periodontitis and MS, which were further validated by qPCR and immunohistochemical staining. ssGSEA analysis revealed that T and B cells significantly influence the development of MS and periodontitis. Conclusions FAM46C, SLC7A7, LY96, CFI, DDIT4L, CD14, C5AR1, and IGJ were the most important crosstalk genes between periodontitis, and MS. Further studies found that CFI, DDIT4L, and FAM46C were potential biomarkers in periodontitis and MS.

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Section: Discussionmentioning

confidence: 99%

Exploration of potential shared gene signatures between periodontitis and multiple sclerosis

Wu,

Cheng,

Zhang

et al. 2024

BMC Oral Health

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“…The progress of many important technologies such as solar cells, light‐emitting diodes, batteries, superconductors, and thermoelectrics rely on how fast materials are discovered or developed. Because the best materials are often a blend of multiple components, high‐throughput experimentations (HTEs), [ 1–33 ] both for making and studying mixtures/alloys, have recently gained major attention. [ 2 ] However, state‐of‐art HTEs are unfortunately able to make only a fraction of possible compositions and then employ machine learning algorithms to extrapolate to unmade compositional space.…”

Section: Introductionmentioning

confidence: 99%

High‐Throughput Exploration of Triple‐Cation Perovskites via All‐in‐One Compositionally‐Graded Films

Moradi

Kundu

Awais

et al. 2023

Small

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Many devices heavily rely on combinatorial material optimization. However, new material alloys are classically developed by studying only a fraction of giant chemical space, while many intermediate compositions remain unmade in light of the lack of methods to synthesize gapless material libraries. Here report a high‐throughput all‐in‐one material platform to obtain and study compositionally‐tunable alloys from solution is reported. This strategy is applied to make all CsxMAyFAzPbI3 perovskite alloys (MA and FA stand for methylammonium and formamidinium, respectively), in less than 10 min, on a single film, on which 520 unique alloys are then studied. Through stability mapping of all these alloys in air supersaturated with moisture, a range of targeted perovskites are found, which are then chosen to make efficient and stable solar cells in relaxed fabrication conditions, in ambient air. This all‐in‐one platform provides access to an unprecedented library of compositional space with no unmade alloys, and hence aids in a comprehensive accelerated discovery of efficient energy materials.

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“…Innovations in technology and rapid and large-scale mining of high-throughput data will facilitate the comprehensive and efficient understanding and analysis of the molecular regulatory mechanisms of various diseases [ 15 , 16 ]. Weighted gene coexpression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) algorithms have been applied to various fields of medicine for clinical applications [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

An integrated bioinformatics approach to identify key biomarkers in the tubulointerstitium of patients with focal segmental glomerulosclerosis and construction of mRNA–miRNA-lncRNA/circRNA networks

Zhang,

Bai,

et al. 2023

Renal Failure

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Objective The purpose of this study was to identify potential biomarkers in the tubulointerstitium of focal segmental glomerulosclerosis (FSGS) and comprehensively analyze its mRNA–miRNA-lncRNA/circRNA network. Methods The expression data (GSE108112 and GSE200818) were downloaded from the Gene Expression Omnibus database ( https://www.ncbi.nlm.nih.gov/geo/ ). Identification and enrichment analysis of differentially expressed genes (DEGs) were performed. the PPI networks of the DEGs were constructed and classified using the Cytoscape molecular complex detection (MCODE) plugin. Weighted gene coexpression network analysis (WGCNA) was used to identify critical gene modules. Least absolute shrinkage and selection operator regression analysis were used to screen for key biomarkers of the tubulointerstitium in FSGS, and the receiver operating characteristic curve was used to determine their diagnostic accuracy. The screening results were verified by quantitative real-time-PCR (qRT–PCR) and Western blot. The transcription factors (TFs) affecting the hub genes were identified by Cytoscape iRegulon. The mRNA–miRNA-lncRNA/circRNA network for identifying potential biomarkers was based on the starBase database. Results A total of 535 DEGs were identified. MCODE obtained eight modules. The green module of WGCNA had the greatest association with the tubulointerstitium in FSGS. PPARG coactivator 1 alpha ( PPARGC1A ) was screened as a potential tubulointerstitial biomarker for FSGS and verified by qRT–PCR and Western blot. The TFs FOXO4 and FOXO1 had a regulatory effect on PPARGC1A . The ceRNA network yielded 17 miRNAs, 32 lncRNAs, and 50 circRNAs. Conclusions PPARGC1A may be a potential biomarker in the tubulointerstitium of FSGS. The ceRNA network contributes to the comprehensive elucidation of the mechanisms of tubulointerstitial lesions in FSGS.

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Artificial Intelligence on High Throughput Data for Biomedical Research

Cited by 7 publications

References 0 publications

Exploration of potential shared gene signatures between periodontitis and multiple sclerosis

Exploration of potential shared gene signatures between periodontitis and multiple sclerosis

High‐Throughput Exploration of Triple‐Cation Perovskites via All‐in‐One Compositionally‐Graded Films

An integrated bioinformatics approach to identify key biomarkers in the tubulointerstitium of patients with focal segmental glomerulosclerosis and construction of mRNA–miRNA-lncRNA/circRNA networks

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