Image 1_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

“…Next, we comprehensively explored the distinct heterogeneity and characteristics for four CAFs-based BLCA subtypes. Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…”

Table 1_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

“…Next, we comprehensively explored the distinct heterogeneity and characteristics for four CAFs-based BLCA subtypes. Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…”

Image 2_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

“…Next, we comprehensively explored the distinct heterogeneity and characteristics for four CAFs-based BLCA subtypes. Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…”

Data_Sheet_1_Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients.pdf by Negar Darabi (10502579)

“…</p><p>Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting–XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. …”