LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery by M RN (9866504)

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.…”

Who has ears, listen: Citizen Listening Program for disease prevention. by Ramiro García Pereira (2836310)

Example of a typical workflow to construct a network using NeKo. by Marco Ruscone (11747264)

DataSheet_1_CL-PMI: A Precursor MicroRNA Identification Method Based on Convolutional and Long Short-Term Memory Networks.pdf by Huiqing Wang (1370457)

“…In order to overcome the limitations of existing methods, we propose a pre-miRNA identification algorithm based on a cascaded CNN-LSTM framework, called CL-PMI. …”

Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures. by Kevin Sawade (16726527)

“…However, our algorithm was 1000 times faster than pyROSETTA (both algorithms have been parallelized on a per-structure basis using the Python package joblib [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010531#pcbi.1010531.ref069" target="_blank">69</a>]).…”

Image_1_KairoSight: Open-Source Software for the Analysis of Cardiac Optical Data Collected From Multiple Species.TIF by Blake L. Cooper (11622613)

“…Despite the refinement of software tools and algorithms, significant programming expertise is often required to analyze large optical data sets, and data analysis can be laborious and time-consuming. …”

Cumulative density function (CDF) of prediction error (PE, achieved spherical equivalent–formula predicted spherical equivalent) for the SRKT, Hoffer Q, Holladay1, Haigis (with opt... by Achim Langenbucher (3156741)

Pyrho Validation - Check re-gridded periodic data by Jimmy Shen (12664675)

Schematic example of multiscale modelling implementation. by Robyn M. Stuart (3754000)

Table 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.xlsx by Peng Liu (120506)

“…We identified five key anoikis-DEGs (PDK4, CEACAM6, CFB, CX3CL1, and HLA-DMA) and demonstrated their high diagnostic accuracy for UC. …”

Image 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…We identified five key anoikis-DEGs (PDK4, CEACAM6, CFB, CX3CL1, and HLA-DMA) and demonstrated their high diagnostic accuracy for UC. …”

Image 4_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…We identified five key anoikis-DEGs (PDK4, CEACAM6, CFB, CX3CL1, and HLA-DMA) and demonstrated their high diagnostic accuracy for UC. …”

Image 5_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…We identified five key anoikis-DEGs (PDK4, CEACAM6, CFB, CX3CL1, and HLA-DMA) and demonstrated their high diagnostic accuracy for UC. …”

Image 3_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…We identified five key anoikis-DEGs (PDK4, CEACAM6, CFB, CX3CL1, and HLA-DMA) and demonstrated their high diagnostic accuracy for UC. …”

Image 2_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…We identified five key anoikis-DEGs (PDK4, CEACAM6, CFB, CX3CL1, and HLA-DMA) and demonstrated their high diagnostic accuracy for UC. …”

PyPEFAn Integrated Framework for Data-Driven Protein Engineering by Niklas E. Siedhoff (11133851)

“…Data-driven strategies are gaining increased attention in protein engineering due to recent advances in access to large experimental databanks of proteins, next-generation sequencing (NGS), high-throughput screening (HTS) methods, and the development of artificial intelligence algorithms. However, the reliable prediction of beneficial amino acid substitutions, their combination, and the effect on functional properties remain the most significant challenges in protein engineering, which is applied to develop proteins and enzymes for biocatalysis, biomedicine, and life sciences. …”

The T300R mutation in ROMK abolishes channel currents. by Nga H. Nguyen (2075920)

CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages by Vicente Martí-Centelles (1422415)

“…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”