Image 3_Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model.jpeg by Ting Chen (15205)

“…</p>Results<p>The improved algorithm significantly outperformed other algorithms on 23 standard test functions. …”

Data Sheet 2_Using AlphaFold-Multimer to study novel protein-protein interactions of predation essential hypothetical proteins in Bdellovibrio.pdf by Ibukun John Abulude (21084308)

“…Hypothetical proteins with unestablished functions have been implicated in B. bacteriovorus predation by many studies. …”

Data Sheet 3_Using AlphaFold-Multimer to study novel protein-protein interactions of predation essential hypothetical proteins in Bdellovibrio.zip by Ibukun John Abulude (21084308)

“…Hypothetical proteins with unestablished functions have been implicated in B. bacteriovorus predation by many studies. …”

Image 1_Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model.jpeg by Ting Chen (15205)

“…</p>Results<p>The improved algorithm significantly outperformed other algorithms on 23 standard test functions. …”

Data Sheet 2_Machine learning-driven discovery of NETs-associated diagnostic biomarkers and molecular subtypes in tuberculosis.pdf by Shoupeng Ding (21555394)

“…Among the ensemble of 113 machine learning methods, the “StepgIm[both]+RF” algorithm demonstrated superior performance, ultimately identifying six core NETs genes. …”

Data Sheet 1_Machine learning-driven discovery of NETs-associated diagnostic biomarkers and molecular subtypes in tuberculosis.pdf by Shoupeng Ding (21555394)

“…Among the ensemble of 113 machine learning methods, the “StepgIm[both]+RF” algorithm demonstrated superior performance, ultimately identifying six core NETs genes. …”

Table 1_Using AlphaFold-Multimer to study novel protein-protein interactions of predation essential hypothetical proteins in Bdellovibrio.xlsx by Ibukun John Abulude (21084308)

“…Hypothetical proteins with unestablished functions have been implicated in B. bacteriovorus predation by many studies. …”

DataSheet1_Deletion of Mgat2 in spermatogonia blocks spermatogenesis.pdf by Mohd Shamoon Asmat (19700998)

“…In addition, RNA-seq analysis at 15 days after birth revealed a unique transcriptomic landscape in Mgat2[−/−] germ cells with genes required for sperm formation and functions being most downregulated. Bioinformatic analyses using the ingenuity pathway analysis (IPA) algorithm identified ERK and AKT as central activities. …”

Data Sheet 1_Epigenetic modifications in developmental coordination disorder: association between DNA methylation and motor performance.docx by Fangfang Huang (821089)

“…Among the key DMPs, methylation levels at cg18187326 (FAM45A) and cg11968956 (FAM184A) were significantly associated with both total motor and gross motor scores. In addition, cg03597174 (SEZ6) was negatively associated, while cg05986449 (GPD2) was positively associated with gross motor function.…”

Data Sheet 2_Epigenetic modifications in developmental coordination disorder: association between DNA methylation and motor performance.xlsx by Fangfang Huang (821089)

“…Among the key DMPs, methylation levels at cg18187326 (FAM45A) and cg11968956 (FAM184A) were significantly associated with both total motor and gross motor scores. In addition, cg03597174 (SEZ6) was negatively associated, while cg05986449 (GPD2) was positively associated with gross motor function.…”

Table 1_Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning.docx by Tao Zhou (117050)

“…Hub genes in IC/BPS patients were identified through the application of three distinct machine-learning algorithms. Additionally, the inflammatory status and immune landscape of IC/BPS patients were evaluated using the ssGSEA algorithm. …”

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. …”