Image 11_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherap... by Weifeng Wang (83822)

“…</p>Methods<p>Transcriptome sequencing and single-cell transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. …”

Image 4_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Methods<p>Transcriptome sequencing and single-cell transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. …”

Image 7_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Methods<p>Transcriptome sequencing and single-cell transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. …”

Table 9_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Methods<p>Transcriptome sequencing and single-cell transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. …”

Table 5_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Methods<p>Transcriptome sequencing and single-cell transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. …”

Table 8_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Methods<p>Transcriptome sequencing and single-cell transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. …”

Table 7_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Methods<p>Transcriptome sequencing and single-cell transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. …”

Image 1_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Methods<p>Transcriptome sequencing and single-cell transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. …”

Image 13_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherap... by Weifeng Wang (83822)

“…</p>Methods<p>Transcriptome sequencing and single-cell transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. …”

Table 11_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherap... by Weifeng Wang (83822)

“…</p>Methods<p>Transcriptome sequencing and single-cell transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. …”

Image 12_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherap... by Weifeng Wang (83822)

“…</p>Methods<p>Transcriptome sequencing and single-cell transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. …”

Catalogue of compact radio sources in Messier-82 from e-MERLIN observations by Sibongumusa Shungube (21197363)

“…</p><p dir="ltr">The data were processed using the e-MERLIN CASA pipeline for initial calibration. …”

Flow duration. by Himanshi Babbar (20448110)

“…The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. …”

RF model. by Himanshi Babbar (20448110)

“…The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. …”

Table 1_Older people and stroke: a machine learning approach to personalize the rehabilitation of gait.docx by Elvira Maranesi (8839496)

“…This underscores the basal ganglia’s role in motor control, sensory processing, and postural control, highlighting the importance of sensory input in post-stroke rehabilitation.…”

Software defined vehicular networks. by Himanshi Babbar (20448110)

“…The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. …”

Flow length. by Himanshi Babbar (20448110)

“…The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. …”

LR_Reconnaissance attack. by Himanshi Babbar (20448110)

“…The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. …”

LR model. by Himanshi Babbar (20448110)

“…The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. …”

Traffic categorization. by Himanshi Babbar (20448110)

“…The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. …”