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. …”

Pseudocode of the RPath algorithm. by Daniel Domingo-Fernández (5432273)

“…The function then prioritizes the drug if at least one path is concordant with the observed drug-perturbed transcriptomic signatures (evaluated via <b>Function 1</b>, <i>is_concordant</i>) and the same path is anti-correlated with the observed disease-specific transcriptomic signatures (evaluated via <b>Function 2</b>, <i>is_anti_correlated</i>). …”

Algorithm membership function. by Mohamed Raef Smaoui (9865830)

“…<p>(Top) Input Membership Function. The algorithm classifies glucose input into 4 sets: low, medium, high, and ex_high. …”

Comparison of BLAST, FFPRED, CSSAG, STDNN, MTDNN and PFP-WGAN on dataset 2. by Seyyede Fatemeh Seyyedsalehi (10196767)

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Constructing the dynamical law. by Robert Marsland (8616483)

ROC Plots with 99% confidence interval (shaped region) for all-functional parameters of KCNQ1 channel considered in this study. by Saksham Phul (12437059)

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List of Up and Down regulated genes in <i>Dyrk1a</i>^<i>C/C</i> hippocampi compared with controls (Deseq algorithm, P<0.025). by Véronique Brault (160595)

Select disease-linked mutations in the gene encoding ROMK destabilize the protein. by Nga H. Nguyen (2075920)

Subjects: “…verified computational algorithm…”

S1 File - by Yuh-Chin T. Huang (17867207)

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S1 Dataset - by Yuh-Chin T. Huang (17867207)

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Table_1_A Comparative Study of Cluster Detection Algorithms in Protein–Protein Interaction for Drug Target Discovery and Drug Repurposing.DOCX by Jun Ma (9393)

“…The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein–protein interaction (PPI) network, could differ depending on different cluster detection algorithms. …”

Partner identification for proteins where the predicted and experimental interfaces do not match (F1-score < 0.2). by Chloé Dequeker (4966165)

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Alignment of amino-acids sequence of nucleotide-binding domains of Ssa1 with DnaK from <i>E</i>. <i>coli</i> using the Needleman-Wunsch algorithm for global alignment. by Deepika Gaur (814560)

Force Field Optimization Guided by Small Molecule Crystal Lattice Data Enables Consistent Sub-Angstrom Protein–Ligand Docking by Hahnbeom Park (665004)

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Comparing runtimes and the number of minimized conformations between fries/<i>EWAK</i>* and <i>BBK</i>* for a variety of designs. by Anna U. Lowegard (8051597)

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BLI titration experiments to calculate <i>K</i>_<i>d</i> values for select c-Raf-RBD variants. BLI titration experiments to calculate <i>K</i>_<i>d</i> values f... by Anna U. Lowegard (8051597)

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Identification of hub genes from the PPI network by bottleneck algorithm using the cytoHubba Cytoscape plugin. by Cihat Erdogan (12529243)

Best sampling for each range of selected points (<i>n</i>_<i>S</i>). by Greta Grassmann (12418506)

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A framework for improving localisation prediction algorithms. by Sven B. Gould (12237287)

“…One can expect that the combination of multi-dimensional parameters from evolutionary biology, cell biology and molecular biology on evolutionary diverse species will significantly improve the next generation of machine leaning algorithms that serve localisation (and function) predictions.…”

Top 15 hub genes in five algorithms. by Tianqi Liu (1625776)