Flowchart of step() function. by Raed Alharthi (18340157)

High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

Subjects:

High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

Subjects:

High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

Subjects:

High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

Subjects:

High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

Subjects:

High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

Subjects:

Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

BOFdat Step 3: Identifying species-specific metabolic end goals. by Jean-Christophe Lachance (6619307)

“…<p>(A) After Step 3, the entire weight of the cell is accounted for by BOFdat. …”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

Subjects: “…probabilistic scoring function…”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

Subjects: “…probabilistic scoring function…”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

Subjects: “…probabilistic scoring function…”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

Subjects: “…probabilistic scoring function…”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

Subjects: “…probabilistic scoring function…”

User options and main steps of the generative algorithm. by Diana C. de Oliveira (9220729)

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. …”

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

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”

A CRISPR engineering workflow creates a library of fluorescently tagged protein-coding genes. by Diane M. Wiener (1956583)

Subjects:

Curve of step response signal of 6 algorithms. by Guangwei Liu (181992)