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

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

“…Expression profiles of HCC cells and normal liver cells were first analyzed by maximum relevance minimum redundancy (mRMR) to get a top 50 signature gene feature. For further analysis, the incremental feature selection (IFS) method and leave-one-out cross validation (LOOCV) were conducted to build an optimal classification model and to extract 21 potentially essential biomarkers for HCC cells. …”

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

DataSheet_1_Trans-population graph-based coverage optimization of allogeneic cellular therapy.xlsx by Sapir Israeli (15415241)

DataSheet_1_Trans-population graph-based coverage optimization of allogeneic cellular therapy.xlsx by Sapir Israeli (15415241)

DataSheet_1_Trans-population graph-based coverage optimization of allogeneic cellular therapy.xlsx by Sapir Israeli (15415241)

Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures by Dibyajyoti Das (14845321)

Subjects:

Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures by Dibyajyoti Das (14845321)

Subjects:

Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures by Dibyajyoti Das (14845321)

Subjects:

WGCNA analysis and construction of a prognostic risk score based on CXCL9 + macrophage-related genes. by Bin Liang (228729)

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

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

<i>In silico</i> prediction of blood cholesterol levels from genotype data by Francesco Reggiani (5727733)

“…<div><p>In this work we present a framework for blood cholesterol levels prediction from genotype data. The predictor is based on an algorithm for cholesterol metabolism simulation available in literature, implemented and optimized by our group in the R language. …”

Results of the BRIDES node selection procedure for two scenarios and three weighted models. by Cindy Bouchard (6844979)

Data Sheet 1_Clinical potential and experimental validation of prognostic genes in hepatocellular carcinoma revealed by risk modeling utilizing single cell and transcriptome constr... by Hang Deng (1664668)

“…</p>Methods<p>The HCC datasets were obtained from public databases and then differential expression analysis were used to obtain significant gene expression profiles. Subsequently, univariate Cox regression analysis and PH assumption test were performed, and a risk model was developed using an optimal algorithm from 101 combinations on the TCGA-LIHC dataset to pinpoint prognostic genes. …”

MOA classification performance and model benchmarking. by Josh L. Espinoza (10492448)

“…B) The influence of the <i>Clairvoyance</i> optimization algorithm for feature selection on model performance at each of the 5 sub-model decision points. …”

The penetrance tables for the 8 DNME models. by Liyan Sun (760586)

“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”

The penetrance tables for the 8 DME models. by Liyan Sun (760586)

“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”

DE-DSRGs were identified as diagnostic genes for AMI. by Nan Huang (464390)

Image_2_A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.TIF by Qingqing Li (1505614)

“…First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. …”