Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

Subjects:

Relative performance of classification algorithms using gene-expression and clinical predictors and performing feature selection. by Stephen R. Piccolo (8367780)

“…Next, we sorted the algorithms based on the average rank across all dataset/class combinations. …”

DataSheet1_Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information.DOCX by Jingyi Wang (550305)

“…<p>Purpose: To assess the significance of mutation mutual exclusion information in the optimization of radiomics algorithms for predicting gene mutation.…”

QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm by Z.Y. Algamal (5547620)

“…The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. …”

Comparison of ranks for classification algorithms across performance metrics. by Stephen R. Piccolo (8367780)

DataSheet_1_Comparative analysis of assembly algorithms to optimize biosynthetic gene cluster identification in novel marine actinomycete genomes.docx by Daniela Tizabi (10297654)

DataSheet_2_Comparative analysis of assembly algorithms to optimize biosynthetic gene cluster identification in novel marine actinomycete genomes.pdf by Daniela Tizabi (10297654)

Illustration of the BRIDES algorithm and the selection process. by Cindy Bouchard (6844979)

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

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

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

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

Cauchy mutation. by Dmitrii Smirnov (8822324)

Random mutation direction in multi-dimensional parameter space. by Dmitrii Smirnov (8822324)

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

“…</p><p>Methods: Transcriptomic data of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We assessed the immune cell infiltration in each HCC specimen using single sample gene set enrichment analysis (ssGSEA) and classified all patients with HCC into high- and low-immune clusters using a hierarchical clustering algorithm. …”

Network propagation identifies ageing-associated genes as well as genes distinguishing prostate tumors of different grades. by Konstantina Charmpi (9901053)

Table1_Identification of a ferroptosis-related gene signature predicting recurrence in stage II/III colorectal cancer based on machine learning algorithms.XLSX by Ze Wang (132986)

“…We developed a machine learning framework with 83 combinations of 10 algorithms based on 10-fold cross-validation (CV) or bootstrap resampling algorithm to identify the most robust and stable model. …”

Optimal use of statistical methods to validate reference gene stability in longitudinal studies by Venkat Krishnan Sundaram (7026989)

DataSheet1_Identification of a ferroptosis-related gene signature predicting recurrence in stage II/III colorectal cancer based on machine learning algorithms.DOCX by Ze Wang (132986)

“…We developed a machine learning framework with 83 combinations of 10 algorithms based on 10-fold cross-validation (CV) or bootstrap resampling algorithm to identify the most robust and stable model. …”