Data_Sheet_7_Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease.xlsx by Yi-Jie He (14162616)

“…In the second part: we elucidate the biological functions involved in the Hub genes and their role in the immune microenvironment by integrating algorithms (GSEA, GSVA and CIBERSORT). …”

Image_3_EDEM2 is a diagnostic and prognostic biomarker and associated with immune infiltration in glioma: A comprehensive analysis.tiff by Yuxi Wu (9183168)

“…<p>Glioma is a highly common pathological brain tumor. Misfolded protein response, which is strongly associated with the growth of cancerous tumors, is mediated by the gene, endoplasmic reticulum degradation-enhancing alpha-mannosidase-like protein 2. …”

Data_Sheet_1_Peripheral Nerve Activation Evokes Machine-Learnable Signals in the Dorsal Column Nuclei.pdf by Alastair J. Loutit (6489977)

“…<p>The brainstem dorsal column nuclei (DCN) are essential to inform the brain of tactile and proprioceptive events experienced by the body. …”

Data_Sheet_1_Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease.xlsx by Yi-Jie He (14162616)

“…In the second part: we elucidate the biological functions involved in the Hub genes and their role in the immune microenvironment by integrating algorithms (GSEA, GSVA and CIBERSORT). …”

Data_Sheet_4_Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease.xlsx by Yi-Jie He (14162616)

“…In the second part: we elucidate the biological functions involved in the Hub genes and their role in the immune microenvironment by integrating algorithms (GSEA, GSVA and CIBERSORT). …”

Data_Sheet_1_Adaptive Tuning Curve Widths Improve Sample Efficient Learning.PDF by Florian Meier (591296)

“…<p>Natural brains perform miraculously well in learning new tasks from a small number of samples, whereas sample efficient learning is still a major open problem in the field of machine learning. …”

Table_3_A Prognostic Signature Consisting of Pyroptosis-Related Genes and SCAF11 for Predicting Immune Response in Breast Cancer.XLSX by Ling Chu (434707)

“…Knocking down SCAF11 possessed an anti-cancer effect in terms of inhibiting cell viability and suppressing colony-formation in in-vitro functional assays. Meanwhile, the biological functions of SCAF11 in BRCA were further validated with several algorithms, such as Xiantao tool, LinkedOmics, GEPIA2, and TISIDB. …”

Table_2_A Prognostic Signature Consisting of Pyroptosis-Related Genes and SCAF11 for Predicting Immune Response in Breast Cancer.XLSX by Ling Chu (434707)

“…Knocking down SCAF11 possessed an anti-cancer effect in terms of inhibiting cell viability and suppressing colony-formation in in-vitro functional assays. Meanwhile, the biological functions of SCAF11 in BRCA were further validated with several algorithms, such as Xiantao tool, LinkedOmics, GEPIA2, and TISIDB. …”

Image_1_A Prognostic Signature Consisting of Pyroptosis-Related Genes and SCAF11 for Predicting Immune Response in Breast Cancer.TIF by Ling Chu (434707)

“…Knocking down SCAF11 possessed an anti-cancer effect in terms of inhibiting cell viability and suppressing colony-formation in in-vitro functional assays. Meanwhile, the biological functions of SCAF11 in BRCA were further validated with several algorithms, such as Xiantao tool, LinkedOmics, GEPIA2, and TISIDB. …”

Table_1_A Prognostic Signature Consisting of Pyroptosis-Related Genes and SCAF11 for Predicting Immune Response in Breast Cancer.XLSX by Ling Chu (434707)

“…Knocking down SCAF11 possessed an anti-cancer effect in terms of inhibiting cell viability and suppressing colony-formation in in-vitro functional assays. Meanwhile, the biological functions of SCAF11 in BRCA were further validated with several algorithms, such as Xiantao tool, LinkedOmics, GEPIA2, and TISIDB. …”

Code by 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 by 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). …”

Table_1_Construction and Validation of an Immune-Related Risk Score Model for Survival Prediction in Glioblastoma.DOCX by Wei Ren (267391)

“…Background<p>As one of the most important brain tumors, glioblastoma (GBM) has a poor prognosis, especially in adults. …”

DataSheet1_Identification of a cellular senescence-related-lncRNA (SRlncRNA) signature to predict the overall survival of glioma patients and the tumor immune microenvironment.docx by Qing Liu (20889)

“…Gene set enrichment analysis was used to visualize functional enrichment (GSEA). The CIBERSORT algorithm, ESTIMATE and TIMER databases were utilized to evaluate the differences in the infiltration of 22 types of immune cells and their association with the signature. …”

MCCN Case Study 2 - Spatial projection via modelled data by 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 by Guangzong Li (16696443)

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

VinaLigGen: a method to generate LigPlots and retrieval of hydrogen and hydrophobic interactions from protein-ligand complexes by Raghvendra Agrawal (17135479)

“…This paper describes an implementation of an automation technique on the executable programs like ligplot.exe, hbplus.exe and hbadd.exe to obtain the 2D interaction map (LigPlots) of the protein and ligand complex (*.ps) and hydrogen bonds and hydrophobic interactions in *.csv format for molecules to be considered for virtual screening by using some sorting & searching algorithms and python’s file handling functions, and it also mentions the program’s limitations and availability of the program. …”

Data_Sheet_1_MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence.docx by Mehdi Foroozandeh Shahraki (9555317)

“…MCIC is freely available as a python package and standalone toolkit for Windows and Linux-based operating systems with several functions to facilitate the screening and thermal and pH dependence prediction of cellulases.…”

Table_1_MiR34a Regulates Neuronal MHC Class I Molecules and Promotes Primary Hippocampal Neuron Dendritic Growth and Branching.DOCX by Yue Hu (201714)

“…Therefore, clarifying the regulation of MHC-I expression is necessary to develop an accurate understanding of its function in the CNS. Here, we show that microRNA 34a (miR34a), a brain enriched noncoding RNA, is temporally expressed in developing hippocampal neurons, and its expression is significantly increased after MHC-I protein abundance is decreased in the hippocampus. …”

Image_1_MiR34a Regulates Neuronal MHC Class I Molecules and Promotes Primary Hippocampal Neuron Dendritic Growth and Branching.TIF by Yue Hu (201714)

“…Therefore, clarifying the regulation of MHC-I expression is necessary to develop an accurate understanding of its function in the CNS. Here, we show that microRNA 34a (miR34a), a brain enriched noncoding RNA, is temporally expressed in developing hippocampal neurons, and its expression is significantly increased after MHC-I protein abundance is decreased in the hippocampus. …”