A* Path-Finding Algorithm to Determine Cell Connections حسب Max Weng (22327159)

"…To address this, the research integrates a modified A* pathfinding algorithm with a U-Net convolutional neural network, a custom statistical binary classification method, and a personalized Min-Max connectivity threshold to automate the detection of astrocyte connectivity.…"

Table1_Development and evaluation of a chronic kidney disease risk prediction model using random forest.xlsx حسب Krish Mendapara (18890758)

Image2_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png حسب Krish Mendapara (18890758)

Image3_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png حسب Krish Mendapara (18890758)

Image1_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png حسب Krish Mendapara (18890758)

Table3_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX حسب Zhen Wang (72451)

"…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …"

Table2_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX حسب Zhen Wang (72451)

"…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …"

Table1_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX حسب Zhen Wang (72451)

"…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …"

Flowchart scheme of the ML-based model. حسب Noshaba Qasmi (20405009)

"…<b>I)</b> Testing data consisting of 20% of the entire dataset. <b>J)</b> Optimization of hyperparameter tuning. <b>K)</b> Algorithm selection from all models. …"

DataSheet1_Comprehensive analysis of immune-related gene signature based on ssGSEA algorithms in the prognosis and immune landscape of hepatocellular carcinoma.ZIP حسب Liangliang Wang (580395)

"…Furthermore, Cox regression analysis revealed 49 DEGs that were associated with survival. Then, 19 DEGs were screened using the LASSO algorithm for IRGs construction and patients were classified into high- and low-risk groups. …"

Schematic representation of semi-automated algorithm for organoids analyzing. حسب Anna Demchenko (18449439)

Unsupervised clustering and marker gene signatures. حسب Josh L. Espinoza (10492448)

An inflammation-associated ferroptosis signature can optimize the diagnosis, prognosis evaluation and immunotherapy options in hepatocellular carcinoma حسب Wanyuan Ruan (13763851)

"…</p> <p>Results: A total of 224 intersecting DEGs identified from different inflammation- and ferroptosis-subtypes were set as IAF-genes. …"

Table1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.xlsx حسب Yu Yin (329063)

"…In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.…"

Image1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.pdf حسب Yu Yin (329063)

"…In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.…"

Image_1_Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm.TIFF حسب Hao Chen (5190)

"…</p>Conclusion<p>This study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.…"

Table_1_Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm.XLSX حسب Hao Chen (5190)

"…</p>Conclusion<p>This study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.…"

Symmetry test result. حسب Jiyong Moon (16819302)

Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) حسب Daniel Pérez Palau (11097348)

"…</p><h2>Model Architecture</h2><p dir="ltr">The model is based on <code>pysentimiento/robertuito-base-uncased</code> with the following modifications:</p><ul><li>A dense classification layer was added over the base model</li><li>Uses input IDs and attention masks as inputs</li><li>Generates a multi-class classification with 5 hate categories</li></ul><h2>Dataset</h2><p dir="ltr"><b>HATEMEDIA Dataset</b>: Custom hate speech dataset with categorization by type:</p><ul><li><b>Labels</b>: 5 hate type categories (0-4)</li><li><b>Preprocessing</b>:</li><li>Null values ​​removed from text and labels</li><li>Reindexing and relabeling (original labels are adjusted by subtracting 1)</li><li>Exclusion of category 2 during training</li><li>Conversion of category 5 to category 2</li></ul><h2>Training Process</h2><h3>Configuration</h3><ul><li><b>Batch size</b>: 128</li><li><b>Epoches</b>: 5</li><li><b>Learning rate</b>: 2e-5 with 10% warmup steps</li><li><b>Early stopping</b> with patience=2</li><li><b>Class weights</b>: Balanced to handle class imbalance</li></ul><h3>Custom Metrics</h3><ul><li>Recall for specific classes (focus on class 2)</li><li>Precision for specific classes (focus on class 3)</li><li>F1-score (weighted)</li><li>AUC-PR</li><li>Recall at precision=0.6 (class 3)</li><li>Precision at recall=0.6 (class 2)</li></ul><h2>Evaluation Metrics</h2><p dir="ltr">The model is evaluated using:</p><ul><li>Macro recall, precision, and F1-score</li><li>One-vs-Rest AUC</li><li>Accuracy</li><li>Per-class metrics</li><li>Confusion matrix</li><li>Full classification report</li></ul><h2>Technical Features</h2><h3>Data Preprocessing</h3><ul><li><b>Tokenization</b>: Maximum length of 128 tokens (truncation and padding)</li><li><b>Encoding of labels</b>: One-hot encoding for multi-class classification</li><li><b>Data split</b>: 80% training, 10% validation, 10% testing</li></ul><h3>Optimization</h3><ul><li><b>Optimizer</b>: Adam with linear warmup scheduling</li><li><b>Loss function</b>: Categorical Crossentropy (from_logits=True)</li><li><b>Imbalance handling</b>: Class weights computed automatically</li></ul><h2>Requirements</h2><p dir="ltr">The following Python packages are required:</p><ul><li>TensorFlow</li><li>Transformers</li><li>scikit-learn</li><li>pandas</li><li>datasets</li><li>matplotlib</li><li>seaborn</li><li>numpy</li></ul><h2>Usage</h2><ol><li><b>Data format</b>:</li></ol><ul><li>CSV file or Pandas DataFrame</li><li>Required column name: <code>text</code> (string type)</li><li>Required column name: Data type label (integer type, 0-4) - optional for evaluation</li></ul><ol><li><b>Text preprocessing</b>:</li></ol><ul><li>Automatic tokenization with a maximum length of 128 tokens</li><li>Long texts will be automatically truncated</li><li>Handling of special characters, URLs, and emojis included</li></ul><ol><li><b>Label encoding</b>:</li></ol><ul><li>The model classifies hate speech into 5 categories (0-4)</li><li><code>0</code>: Political hatred: Expressions directed against individuals or groups based on political orientation.…"

Table_6_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx حسب Ying Zhou (25031)

"…</p>Results<p>A total of 49 PRGs were obtained from public database and 35 of them were significantly differentially expressed genes (DEGs). Cluster analysis was then performed to explore the relationship between DEGs with overall survival. …"