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

"…Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). 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. …"

University of Arizona authors' scholarly works published and cited works year 2022 from OpenAlex حسب Yan Han (11106597)

"…</li><li><b>Data Retrieval:</b> The process involves using the oa_fetch function from the openalexR package with the entity="works" parameter and specifying the institutions.ror.…"

University of Arizona authors' scholarly works published and cited works year 2023 from OpenAlex حسب Yan Han (11106597)

"…Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i></p><p><br></p>…"

University of Arizona authors' scholarly works published and cited works year 2021 from OpenAlex حسب Yan Han (11106597)

"…</li><li><b>Data Retrieval:</b> The process involves using the oa_fetch function from the openalexR package with the entity="works" parameter and specifying the institutions.ror.…"

University of Arizona authors' scholarly works published and cited works year 2024 from OpenAlex حسب Yan Han (11106597)

"…</li><li><b>Data Retrieval:</b> The process involves using the oa_fetch function from the openalexR package with the entity="works" parameter and specifying the institutions.ror.…"

University of Arizona authors' scholarly works published and cited works year 2020 from OpenAlex حسب Yan Han (11106597)

"…</li><li><b>Data Retrieval:</b> The process involves using the oa_fetch function from the openalexR package with the entity="works" parameter and specifying the institutions.ror.…"

Code حسب Baoqiang Chen (21099509)

"…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"

Core data حسب Baoqiang Chen (21099509)

"…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"

Landscape17 حسب Vlad Carare (22092515)

"…</p><p dir="ltr">We utilized TopSearch, an open-source Python package, to perform landscape exploration, at an estimated cost of 10⁵CPUh. …"

Table 1_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.xlsx حسب Liu Haoming (22524473)

"…</p>Methods<p>We analyzed TCGA-LUAD/LUSC miRNA-seq data to identify mtRNAs via mitochondrial genome alignment. Machine learning algorithms (SVM, Random Forest, Logistic Regression) classified samples using differentially expressed mtRNAs (P < 0.01, |log2FC| > 1). …"

Data Sheet 2_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.csv حسب Liu Haoming (22524473)

"…</p>Methods<p>We analyzed TCGA-LUAD/LUSC miRNA-seq data to identify mtRNAs via mitochondrial genome alignment. Machine learning algorithms (SVM, Random Forest, Logistic Regression) classified samples using differentially expressed mtRNAs (P < 0.01, |log2FC| > 1). …"

Data Sheet 1_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.csv حسب Liu Haoming (22524473)

"…</p>Methods<p>We analyzed TCGA-LUAD/LUSC miRNA-seq data to identify mtRNAs via mitochondrial genome alignment. Machine learning algorithms (SVM, Random Forest, Logistic Regression) classified samples using differentially expressed mtRNAs (P < 0.01, |log2FC| > 1). …"

MCCN Case Study 2 - Spatial projection via modelled data حسب Donald Hobern (21435904)

"…This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the <a href="https://pypi.org/project/PyKrige/" rel="nofollow" target="_blank">pykrige</a> and <a href="https://pypi.org/project/rioxarray/" rel="nofollow" target="_blank">rioxarray</a> packages.…"

Data Sheet 1_Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy.pdf حسب Guangzong Li (16696443)

"…Eleven ML algorithms were trained and validated using Python, and external validation and performance evaluations were conducted. …"

Pressure control techniques in freeze-drying حسب Geoff Smith (6064268)

"…The most common Pressure control techniques would be listed as follows:</p><ul><li>PID method</li><li>Fuzzy logic</li><li>Max pressure algorithms</li><li>Reinforcement learning</li><li>Adaptive control</li><li>Setpoint profile tracking (Bang-bang control)</li></ul><p dir="ltr">Pressure control systems have to perform a particular task in the target process considering some key functionalities like: system dynamism, control performance, stability, adaptability, accuracy, etc. …"

Bioinformatics-based screening and experimental validation of biomarkers for the treatment of connective tissue-associated interstitial lung disease with liquorice and dried ginger... حسب Hui Yuan (402180)

"…</p> <p>Public datasets of Peripheral blood mononuclear cells (PBMCs) from CTD-ILD (n = 4) and connective tissue disease-associated non-Inflammatory lung disease (CTD-NILD) (n = 3) patients were analyzed using differential expression (p.adj < 0.05 & |log2 Fold Change (FC)| > 0.5), protein-protein interaction networks, and cytohubba algorithms (Top5 genes from six algorithms). …"

a. How various statistical models account for modulation classification performance across the entire dataset. حسب Chris Scholes (3309477)

"…Parameters are <i>Type:</i> neuron classification (primary-like, sustained chopper, etc.); <i>CV:</i> Coefficient of variation of the interspike intervals in response to a pure tone at CF. …"

<b>Drug Release Nanoparticle Systems Design:</b><b>Dataset Compilation and Machine Learning Modeling</b> حسب Shan He (14524901)

"…Herein 11 different AI/ML algorithms were used to develop the predictive AI/ML models. …"

Table 5_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.xlsx حسب Yubo Wang (556762)

"…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …"

Data Sheet 1_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.docx حسب Yubo Wang (556762)

"…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …"