Table_5_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

DataSheet_1_Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data.doc by Alexandra Bomane (3840142)

DataSheet1_Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo.docx by Kaixian Yu (2836709)

DataSheet_1_Machine Learning Uses Chemo-Transcriptomic Profiles to Stratify Antimalarial Compounds With Similar Mode of Action.pdf by Ashleigh van Heerden (11041338)

“…Machine learning (ML) algorithms are powerful tools able to deconvolute such complex chemically-induced transcriptional signatures to identify pathways on which a compound act and in this manner provide an indication of the MoA of a compound. …”

Table_1_Genome-wide association studies for a comprehensive understanding of the genetic architecture of culm strength and yield traits in rice.docx by Jyothi Badri (5309546)

“…We made a detailed analysis of various component traits with the aim of deriving optimized parameters for measuring culm strength. Genotyping by sequencing (GBS)-based genome-wide association study (GWAS) was employed among 181 genotypes for dissecting the genetic control of culm strength traits. …”

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…</li><li><b>Linear support vector machine (Linear SVM):</b> This machine finds the optimal hyperplane to separate E. coli strains based on genomic features such as gene presence or sequence variations.…”

Data Sheet 1_Construction of a diagnostic model for temporal lobe epilepsy using interpretable deep learning: disease-associated markers identification.docx by Tianyu Wang (397547)

“…SHAP interpretation identified DEPDC5, STXBP1, GABRG2, SLC2A1, and LGI1 as the most significant TLE-associated genes. The KAN model revealed complex nonlinear relationships between these genes and TLE status, providing mathematical expressions that capture their contributions. …”

Supplementary Material 9 by Nishitha R Kumar (19750617)

“…CD-HIT can group similar sequences or genomic features when applied to machine learning results in Escherichia coli genomic analysis, improving model efficiency and reducing computational complexity.</p><h4><b>CD-HIT in machine learning-based </b><b><i>E. coli</i></b><b> genomic analysis:</b></h4><ol><li><b>Feature reduction:</b> In supervised machine learning, CD-HIT can cluster similar sequences from genomic data, eliminating redundant information and improving feature selection for classifiers like Random Forest, XGBoost, and SVM.…”

Table_1_Landscape of Immune Microenvironment Under Immune Cell Infiltration Pattern in Breast Cancer.xlsx by Qianhui Xu (10517717)

“…</p>Conclusion<p>This work comprehensively elucidated that the ICI patterns served as an indispensable player in complexity and diversity of TIME. Quantitative identification of the ICI patterns in individual tumor will contribute into mapping the landscape of TIME further optimizing precision immunotherapy.…”

DataSheet_1_Landscape of Immune Microenvironment Under Immune Cell Infiltration Pattern in Breast Cancer.docx by Qianhui Xu (10517717)

“…</p>Conclusion<p>This work comprehensively elucidated that the ICI patterns served as an indispensable player in complexity and diversity of TIME. Quantitative identification of the ICI patterns in individual tumor will contribute into mapping the landscape of TIME further optimizing precision immunotherapy.…”

Data Sheet 1_Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods.docx by Ankita Lawarde (16544943)

“…Introduction<p>MicroRNAs (miRNAs) regulate gene expression and play an important role in carcinogenesis through complex interactions with messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs). …”