Data_Sheet_1_A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis.PDF by Seema Khadirnaikar (9227318)

“…Further, feature selection using greedy algorithm identified 17-hESC-lncRNAs signature, which showed significant consistency with 198 hESC-lncRNAs–based classification, and identified a group of patients with high stem cell–like characteristic in the 10 most common cancer types and CCLE cell lines. …”

Data_Sheet_2_A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis.xlsx by Seema Khadirnaikar (9227318)

“…Further, feature selection using greedy algorithm identified 17-hESC-lncRNAs signature, which showed significant consistency with 198 hESC-lncRNAs–based classification, and identified a group of patients with high stem cell–like characteristic in the 10 most common cancer types and CCLE cell lines. …”

Model convergence to steady state. by Dmitrii Smirnov (8822324)

Tissue effects. by Dmitrii Smirnov (8822324)

Indels characterization algorithm development and benchmark with simulated data and real data. by Marta Sanvicente-García (12306659)

Table 1_Leveraging a disulfidptosis-based signature to characterize heterogeneity and optimize treatment in multiple myeloma.docx by Bingxin Zhang (13011397)

Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)

Sensitivity and Specificity Analysis. by Afnan M. Alhassan (18349378)

CSI, Balanced accuracy, and FMI Analysis. by Afnan M. Alhassan (18349378)

Architecture of CDL Network. by Afnan M. Alhassan (18349378)

Hyper-parameter tuning of the proposed model. by Afnan M. Alhassan (18349378)

Accuracy and F-Measure Analysis. by Afnan M. Alhassan (18349378)

FPR and FNR analysis. by Afnan M. Alhassan (18349378)

Comparative Analysis of Mitosis Detection Method. by Afnan M. Alhassan (18349378)

Markedness and NLR Analysis. by Afnan M. Alhassan (18349378)

Accuracy vs loss for varying epochs. by Afnan M. Alhassan (18349378)

Growth rate comparison between models and experimental results in mono-culture and co-culture. by Elias Vera-Siguenza (16993830)

Error function description and test optimization results. by Timothy Rumbell (7419269)

Comprehensive Detection of Single Amino Acid Variants and Evaluation of Their Deleterious Potential in a PANC‑1 Cell Line by Zhijing Tan (1314216)

“…Identifying single amino acid variants (SAAVs) in cancer is critical for precision oncology. Several advanced algorithms are now available to identify SAAVs, but attempts to combine different algorithms and optimize them on large data sets to achieve a more comprehensive coverage of SAAVs have not been implemented. …”

Table_6_Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules.XLSX by Shiqi Zhang (709298)