Table 1_Trajectories of health conditions predict cardiovascular disease risk among middle-aged and older adults: a national cohort study.docx by Wenlong Li (571749)

“…Trajectories of multimorbidity status, activities of daily living (ADLs) limitations, body roundness index (BRI), pain, sleep duration, depressive symptoms, and cognitive function were identified using latent class growth models (LCGMs). …”

Analysis of HK2-<i>SOX10</i> mRNA interaction. by Ana Luisa Dian (22258504)

Data Sheet 1_Multi-omics analysis and metastasis risk factor prediction in N1b stage PTMC: insights into immune infiltration and therapeutic implications.docx by Hao Dai (4277254)

Subgraph of the STRING reference, physical network showing the three InfoMap-based communities with highest average ENQUIRE-informed Q-scores. by Luca Musella (19986312)

Table 2_Uncovering differential gene expression between mtRNA-positive and -negative osteosarcoma cells: implications beyond mitochondrial function.xlsx by Xiaoyuan Meng (22261234)

“…Identifying novel molecular targets to inhibit osteosarcoma cell growth remains an urgent challenge.</p>Methods<p>Explore the function of mtRNA in the occurrence and development of osteosarcoma utilizing bioinformatics analysis of the Gene Expression Omnibus (GEO) microarray dataset. …”

Table 1_Uncovering differential gene expression between mtRNA-positive and -negative osteosarcoma cells: implications beyond mitochondrial function.xlsx by Xiaoyuan Meng (22261234)

“…Identifying novel molecular targets to inhibit osteosarcoma cell growth remains an urgent challenge.</p>Methods<p>Explore the function of mtRNA in the occurrence and development of osteosarcoma utilizing bioinformatics analysis of the Gene Expression Omnibus (GEO) microarray dataset. …”

Table 3_Uncovering differential gene expression between mtRNA-positive and -negative osteosarcoma cells: implications beyond mitochondrial function.xlsx by Xiaoyuan Meng (22261234)

“…Identifying novel molecular targets to inhibit osteosarcoma cell growth remains an urgent challenge.</p>Methods<p>Explore the function of mtRNA in the occurrence and development of osteosarcoma utilizing bioinformatics analysis of the Gene Expression Omnibus (GEO) microarray dataset. …”

ENQUIRE’s flowchart. by Luca Musella (19986312)

Data Sheet 1_Systematic pan-cancer analysis identifies PKNOX1 as a potential prognostic and immunological biomarker and its functional validation.docx by Kan Liu (250462)

“…The correlations between PKNOX1 expression and MDSC immune infiltration and immune cells were analyzed using the TIDE algorithm and the ESTIMATE algorithm. PKNOX1 -interacting proteins and expression-related genes were analysed via the STRING and TIMER 2.0 platforms, and the functions of PKNOX1 in tumors and the cell pathways involved were predicted via KEGG enrichment analysis. …”

GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

“…Using occurrence data and environmental variable, we employ the Maximum Entropy (MaxEnt) algorithm within the species distribution modeling (SDM) framework to estimate occurrence probability at a spatial resolution of 1/12° (~10 km). …”

Prediction of TFs for key anoikis-related genes. by Yufeng He (5673119)

“…Through Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) machine learning algorithms, six key hub genes were identified: hepatocyte growth factor (<i>HGF</i>), matrix metalloproteinase 13 (<i>MMP13</i>), c-abl oncogene 1, non-receptor tyrosine kinase (<i>ABL1</i>), elastase neutrophil expressed (<i>ELANE</i>), fatty acid synthase (<i>FASN</i>), and long non-coding RNA (<i>Linc00324</i>). …”

Figs 1–11. by Yufeng He (5673119)

“…Through Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) machine learning algorithms, six key hub genes were identified: hepatocyte growth factor (<i>HGF</i>), matrix metalloproteinase 13 (<i>MMP13</i>), c-abl oncogene 1, non-receptor tyrosine kinase (<i>ABL1</i>), elastase neutrophil expressed (<i>ELANE</i>), fatty acid synthase (<i>FASN</i>), and long non-coding RNA (<i>Linc00324</i>). …”

Flow chart of the experimental procedure. by Yufeng He (5673119)

“…Through Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) machine learning algorithms, six key hub genes were identified: hepatocyte growth factor (<i>HGF</i>), matrix metalloproteinase 13 (<i>MMP13</i>), c-abl oncogene 1, non-receptor tyrosine kinase (<i>ABL1</i>), elastase neutrophil expressed (<i>ELANE</i>), fatty acid synthase (<i>FASN</i>), and long non-coding RNA (<i>Linc00324</i>). …”

PCR primers. by Yufeng He (5673119)

“…Through Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) machine learning algorithms, six key hub genes were identified: hepatocyte growth factor (<i>HGF</i>), matrix metalloproteinase 13 (<i>MMP13</i>), c-abl oncogene 1, non-receptor tyrosine kinase (<i>ABL1</i>), elastase neutrophil expressed (<i>ELANE</i>), fatty acid synthase (<i>FASN</i>), and long non-coding RNA (<i>Linc00324</i>). …”

H&E staining results of rats in each group. by Yufeng He (5673119)

“…Through Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) machine learning algorithms, six key hub genes were identified: hepatocyte growth factor (<i>HGF</i>), matrix metalloproteinase 13 (<i>MMP13</i>), c-abl oncogene 1, non-receptor tyrosine kinase (<i>ABL1</i>), elastase neutrophil expressed (<i>ELANE</i>), fatty acid synthase (<i>FASN</i>), and long non-coding RNA (<i>Linc00324</i>). …”

Supporting data for Histone crotonylation is a novel epigenetic regulation and a therapeutic vulnerability for liver cancer treatment by Qidong Li (9069932)

“…Using the ChromHMM machine learning algorithm, we annotated chromatin states based on distinct combinations of these markers. …”

SpatialKNifeY analysis landscape. by Shunsuke A. Sakai (13789939)

LDD microtubule-based nucleation naturally results in a local microtubule density-dependent fraction of microtubule-based nucleation. by Marco Saltini (22209094)

Data for "A multi-objective platform for autonomous property targeting and optimization of colloidal lead halide perovskite quantum dots" by Neal Munyebvu (19690954)

Hippocampal and cortical activity reflect early hyperexcitability in an Alzheimer's mouse model by Marina Diachenko (19739092)

“…</p><p dir="ltr">All data are available upon request. The standalone Python implementation of the fE/I algorithm is available under a CC-BY-NC-SA license at <a href="https://github.com/arthur-ervin/crosci" target="_blank">https://github.com/arthur-ervin/crosci</a>. …”