Data Sheet 4_Using AlphaFold-Multimer to study novel protein-protein interactions of predation essential hypothetical proteins in Bdellovibrio.pdf by Ibukun John Abulude (21084308)

“…By applying Markov Cluster Algorithm and AlphaFold-Multimer to analyze the protein network and interaction partners of the AP-PEPs, we predicted high-confidence protein-protein interactions and two structurally similar but unique novel protein complexes formed among proteins of the Bd2209-Bd2212 and Bd2723-Bd2726 operons. …”

Data Sheet 2_Using AlphaFold-Multimer to study novel protein-protein interactions of predation essential hypothetical proteins in Bdellovibrio.pdf by Ibukun John Abulude (21084308)

“…By applying Markov Cluster Algorithm and AlphaFold-Multimer to analyze the protein network and interaction partners of the AP-PEPs, we predicted high-confidence protein-protein interactions and two structurally similar but unique novel protein complexes formed among proteins of the Bd2209-Bd2212 and Bd2723-Bd2726 operons. …”

Data Sheet 3_Using AlphaFold-Multimer to study novel protein-protein interactions of predation essential hypothetical proteins in Bdellovibrio.zip by Ibukun John Abulude (21084308)

“…By applying Markov Cluster Algorithm and AlphaFold-Multimer to analyze the protein network and interaction partners of the AP-PEPs, we predicted high-confidence protein-protein interactions and two structurally similar but unique novel protein complexes formed among proteins of the Bd2209-Bd2212 and Bd2723-Bd2726 operons. …”

SHAP values for chromatin-associated proteins profiled in integrative model from Hughes et al. [21] and Wang et al. [22] (A) Heatmap of absolute mean SHAP values for different algo... by Kashyap Chhatbar (9515350)

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

<b>Protein-Protein interaction (PPI) network of differentially acetylated proteins</b><b> by Aspirin during differentiation of THP-1 cell towards macrophage</b> by Li Xing (21105170)

“…The PPI network was further analyzed to identify densely interconnected functional clusters/modules using topological clustering algorithms. …”

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

Left: Quality profile of LAFM images for 1) periplasmic (top) and 2) cytoplasmic (bottom) SecYEG. by Creighton M. Lisowski (22756444)

SHAP values for chromatin-associated proteins profiled in Hughes et al. [21] and Wang et al. [22] (A) Heatmap of absolute mean SHAP values for different algorithms DeepSHAP, Kernel... by Kashyap Chhatbar (9515350)

Unsupervised Machine Learning for Differential Analysis in Proteomics by Guanyang Xu (22379531)

Subjects:

Computational Pipeline for Accelerating the Design of Glycomimetics by Yao Xiao (227567)

Subjects:

Functional and pathway enrichment analysis of LR-DEGs. (A) Top 10 BP terms. (B) Top 10 CC terms. (C) Top 10 MF terms. (D) Top 10 KEGG terms. P < 0.05 was considered statistically s... by Wannan Gao (22103659)

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

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