Image 2_Intra-tumor heterogeneity-resistant gene signature predicts prognosis and immune infiltration in breast cancer.jpeg by Haixing Shen (22315486)

“…A machine learning framework incorporating ten algorithms was used to construct a prognostic signature.The expression levels and oncogenic function of the prognostic genes were validated through RT-qPCR and in vitro experiments.…”

Daniel Jaschke - Talk "Quantum computing @ INFN" (2024) by Daniel Jaschke (17341783)

“…Quantum red TEA out of the Quantum TEA library specifically addresses handling tensors with different libraries or hardware, where the tensors are the building block of tensor network algorithms. The benchmark problem is a variational search of a ground state in an interacting model. …”

Image 1_Intra-tumor heterogeneity-resistant gene signature predicts prognosis and immune infiltration in breast cancer.jpeg by Haixing Shen (22315486)

“…A machine learning framework incorporating ten algorithms was used to construct a prognostic signature.The expression levels and oncogenic function of the prognostic genes were validated through RT-qPCR and in vitro experiments.…”

Data Sheet 1_The potential of ME1 in guiding immunotherapeutic strategies for ovarian cancer: insights from pan-cancer research.docx by Jiahui Wei (8289561)

“…</p>Methods<p>We analyzed the ME1 expression levels in both normal and tumor tissues across various cancer types. …”

Table 1_The potential of ME1 in guiding immunotherapeutic strategies for ovarian cancer: insights from pan-cancer research.docx by Jiahui Wei (8289561)

“…</p>Methods<p>We analyzed the ME1 expression levels in both normal and tumor tissues across various cancer types. …”

Image 4_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS) was constructed using 114 machine learning algorithm combinations, refined via CoxBoost to select 25 key genes. …”

Table 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS) was constructed using 114 machine learning algorithm combinations, refined via CoxBoost to select 25 key genes. …”

Table 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS) was constructed using 114 machine learning algorithm combinations, refined via CoxBoost to select 25 key genes. …”

Image 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS) was constructed using 114 machine learning algorithm combinations, refined via CoxBoost to select 25 key genes. …”

Image 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS) was constructed using 114 machine learning algorithm combinations, refined via CoxBoost to select 25 key genes. …”

Table 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS) was constructed using 114 machine learning algorithm combinations, refined via CoxBoost to select 25 key genes. …”

Image 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS) was constructed using 114 machine learning algorithm combinations, refined via CoxBoost to select 25 key genes. …”

Data Sheet 1_Impaired and restored CD8+ T cell responses to a sequentially mutated spike epitope in Omicron variants.docx by Eun Joo Chung (9678137)

“…Our findings suggest that LYN may function both as an immune-evasive hotspot and as a cross-reactive epitope, underscoring the importance of continued epitope-level surveillance as future variants emerge.…”

DataSheet1_Identification of novel markers for neuroblastoma immunoclustering using machine learning.zip by Longguo Zhang (20105238)

“…Three machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were used to screen biomarkers and synthesize their function in neuroblastoma.…”

Table 12_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…RT-qPCR confirmed a significant upregulation of SMARCD3 and TCN1 in ARDS samples, aligning with dataset expression analysis results. Both in vitro and in vivo experiments demonstrated that modulation of SMARCD3 and TCN1 (but not RPL14) significantly affected mitochondrial function, oxidative stress, apoptosis, glucose metabolism and inflammatory cytokine expression.…”

Table 9_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…RT-qPCR confirmed a significant upregulation of SMARCD3 and TCN1 in ARDS samples, aligning with dataset expression analysis results. Both in vitro and in vivo experiments demonstrated that modulation of SMARCD3 and TCN1 (but not RPL14) significantly affected mitochondrial function, oxidative stress, apoptosis, glucose metabolism and inflammatory cytokine expression.…”

Table 8_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…RT-qPCR confirmed a significant upregulation of SMARCD3 and TCN1 in ARDS samples, aligning with dataset expression analysis results. Both in vitro and in vivo experiments demonstrated that modulation of SMARCD3 and TCN1 (but not RPL14) significantly affected mitochondrial function, oxidative stress, apoptosis, glucose metabolism and inflammatory cytokine expression.…”

Table 1_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…RT-qPCR confirmed a significant upregulation of SMARCD3 and TCN1 in ARDS samples, aligning with dataset expression analysis results. Both in vitro and in vivo experiments demonstrated that modulation of SMARCD3 and TCN1 (but not RPL14) significantly affected mitochondrial function, oxidative stress, apoptosis, glucose metabolism and inflammatory cytokine expression.…”

Table 2_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…RT-qPCR confirmed a significant upregulation of SMARCD3 and TCN1 in ARDS samples, aligning with dataset expression analysis results. Both in vitro and in vivo experiments demonstrated that modulation of SMARCD3 and TCN1 (but not RPL14) significantly affected mitochondrial function, oxidative stress, apoptosis, glucose metabolism and inflammatory cytokine expression.…”

Table 3_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…RT-qPCR confirmed a significant upregulation of SMARCD3 and TCN1 in ARDS samples, aligning with dataset expression analysis results. Both in vitro and in vivo experiments demonstrated that modulation of SMARCD3 and TCN1 (but not RPL14) significantly affected mitochondrial function, oxidative stress, apoptosis, glucose metabolism and inflammatory cytokine expression.…”