Optimal image settings for object detection of Iba1 immunolabelled cells in DAB. by Danish M. Anwer (15383441)

Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

Most informative genes for predicting cell-line responses for Gefitinib. by Sofia P. Miranda (11418930)

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

Optimal Drug-Target Docking Visualization. by Qijun Wo (12992781)

Algorithm 2: Maintain inhibitory densities coherence. by Dimitri Rodarie (10148838)

IFS curves of two classification algorithms based on feature list yielded by MCFS. by Zhan Dong Li (12481390)

Algorithm 1: Cap inhibitory densities to number of neurons. by Dimitri Rodarie (10148838)

Table1_Identification of biomarkers for hepatocellular carcinoma based on single cell sequencing and machine learning algorithms.DOCX by Weimin Li (131040)

“…In this study, based on the scRNA-seq results of primary neoplastic cells and paired normal liver cells from eight HCC patients, a new strategy of machine learning algorithms was applied to screen core biomarkers that distinguished HCC tumor tissues from the adjacent normal liver. …”

Triplet Matching for Estimating Causal Effects With Three Treatment Arms: A Comparative Study of Mortality by Trauma Center Level by Giovanni Nattino (561797)

“…We fill the gap by developing an iterative matching algorithm for the three-group setting. Our algorithm outperforms the nearest neighbor algorithm and is shown to produce matched samples with total distance no larger than twice the optimal distance. …”

Screening MMRG gene variables associated with UC based on the optimal machine learning model. by Li Li (14993)

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.…”

Table2_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

Table1_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

Table3_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

Table4_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

Image_1_Identification of a Novel Prognostic Signature for Gastric Cancer Based on Multiple Level Integration and Global Network Optimization.TIF by Lin Cui (163750)

UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms. by Fatemeh Keikha (20917848)

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

IFS performance with different classification algorithms and number of top features in the list yielded by MCFS. by Zhan Dong Li (12481390)