Bridge: A Graph-Based Algorithm to Analyze Dynamic H‑Bond Networks in Membrane Proteins by Malte Siemers (5127779)

“…This long distance communication enables transporters to move protons or other ions and small molecules across the bilayer and receptors to transmit an external signal to the cell. Hydrogen bonds, hydrogen-bond networks, and lipid–protein interactions are essential for the motions and functioning of the membrane protein and, consequently, of outmost interest to structural biology and numerical simulations. …”

Comparison <i>MSE</i>, <i>F</i>(<i>I</i>) and <i>Q</i>(<i>I</i>) evaluation function of segmentation results. by Min Li (12799)

Highly interrelated genes and their biological functions among 21 cancers. by Qunlun Shen (11668968)

DE genes detected by different DEA algorithms. by Jie Xue (417504)

“…Here, we compare 6 performance metrics on both simulated and real scRNA-seq datasets to assess the adaptability of 8 DEA approaches, with a particular emphasis on how well they function under small biological replications. Our findings suggest that DEA algorithms extended from bulk RNA-seq are still competitive under small biological replicate conditions, whereas the newly developed method DEF-scRNA-seq which is based on information entropy offers significant advantages. …”

Dice score for various <i>α</i> values (from the Eq 8) for the tracking of the CST and for the iFOD and PTT algorithms. by Thomas Durantel (20933812)

Subjects: “…Cell Biology…”

Analyses of Immune microenvironment and functions between gene clusters by Jiangli Zhao (2308330)

Functional validation of the metastasis-predictive genes inferred by PLUS from TCGA data. by Junyi Zhou (495644)

Cell surface levels of putative disease-associated ROMK mutants are reduced in HEK293 cells. by Nga H. Nguyen (2075920)

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Search-based testing (Genetic Algorithm) - Chapter 11 of the book "Software Testing Automation" by Saeed Parsa (13893726)

“…</p> <p><br></p> <p>3. Algorithm</p> <p>Below is the main body of the test data generator program:</p> <p>  </p> <p>the main body of a Python program to generate test data for Python functions.…”

The variation process of various loss functions before and after model improvement. by Zhenwei Li (834360)

Differential expression of DSRGs, PPI, and functional enrichment analysis for DSRGs in AMI. by Nan Huang (464390)

Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

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Data_Sheet_1_Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardio... by Nicholas Mattia Marazzi (11936558)

“…<p>Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.…”

Name, biological function and pharmaceutical application of best 10 performing targets with PharmaNet. by Paola Ruiz Puentes (10701646)

(a) ROAL Accuracies and (b) ROAL label requests per episode for the 1^st, 2^nd, 5^th, and 10^th instances of all classes. by Honglan Huang (341951)

Subjects: “…Cell Biology…”

On the improvement of reinforcement active learning with the involvement of cross entropy to address one-shot learning problem by Honglan Huang (341951)

Subjects: “…Cell Biology…”

KEGG analysis bubble plot for other four module. by Zongjin Li (38031)

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Visualization for the module 1. by Zongjin Li (38031)

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Significance levels of gene biomarkers. by Zongjin Li (38031)

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Clustering performance comparison. by Zongjin Li (38031)

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