2025-01-14 - PAG Talk - Kmerseek - Olga Botvinnik - Seanome by Olga Botvinnik (20571431)

Subjects:

Core genes were selected through PPI analysis based on three algorithms. by Dong-Hee Han (140305)

“…<b>(B)</b> The priority of the top 10 genes was evaluated through MNC, which identifies clusters of protein nodes that are more functionally connected to each other and selects the central proteins within the cluster. …”

Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

“…Firstly, we have investigated how the level of accuracy in the estimation of structural variability between a target protein and its templates in the form of <i>σ</i> values profoundly influences 3D modeling. …”

GA[ESM,pMPNN,AF2Rank] algorithm flowchart. by Lu Hong (399882)

Subjects:

Depiction of the theory, can sequence infer function? by Ashley I. Heinson (8124374)

Example of the CNF function for off-axis DHM holograms recorded in a non-telecentric configuration. by Raul Castañeda (13932971)

Functional enrichment analysis of DEGs. by Huiling Deng (4427161)

Fly-QMA: Automated analysis of mosaic imaginal discs in <i>Drosophila</i> by Sebastian M. Bernasek (8525829)

Subjects:

Functional prediction of SOD1 protein variants. by Gabriel Rodrigues Coutinho Pereira (6612542)

“…<p>The SOD1 variants compiled from the literature and databases (233) were analyzed using ten different functional prediction algorithms. …”

Sensitivity of the PFP-WGAN on parameter λ₁. by Seyyede Fatemeh Seyyedsalehi (10196767)

Subjects:

Comparison of BLAST, DeepGO-Seq and PFP-WGAN on dataset 1. by Seyyede Fatemeh Seyyedsalehi (10196767)

Subjects:

(a-b) The LASSO algorithm was used for hyperparameter tuning and identification of the important predictive features. by Devi Soorya Narayana Sasikumar (22331743)

Subjects:

Functions of most frequently mutated genes. by Anda Stan (18056225)

Subjects:

Mutations at functionally important interfaces. by Myo T. Naung (10873278)

Subjects:

Table2_Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features.XLSX by Lei Chen (54296)

“…Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.…”

Table3_Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features.XLSX by Lei Chen (54296)

“…Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.…”

Table4_Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features.XLSX by Lei Chen (54296)

“…Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.…”

Supplementary Files 1-11 from Protein Signatures of NK Cell–Mediated Melanoma Killing Predict Response to Immunotherapies by Sabrina Cappello (14950324)

Dynamical comparison between Drosha and Dicer reveals functional motion similarities and dissimilarities by Rotem Aharoni (8108177)

“…Since dynamics is essential for protein function, a comparison at their dynamics level is fundamental for a complete understanding of the overall relations between these proteins. …”

Functional and stability predictions of SOD1 protein variants. by Gabriel Rodrigues Coutinho Pereira (6612542)

“…<p>The SOD1 variants compiled from the literature and databases (233) were analyzed using functional and stability prediction algorithms. …”