Decoding results. حسب Sam Hall-McMaster (10343795)

Decoding evidence for feature triplets on test tasks. حسب Sam Hall-McMaster (10343795)

"…<p>Another neural hypothesis formulated based on the SF&GPI algorithm is that feature triplets associated with the optimal training policies should be reactivated on test trials. …"

Codes for high-order analytical continuation algorithms based on direct derivation and recursion methods حسب Jian Ma (19747060)

"…The optimal order of the analytical continuation algorithm is contingent upon the noise level of gravity data. …"

Performance comparison of four optimization methods. حسب Xuexing Du (20624042)

"…(B) Relative error convergence of four optimization methods, plotted as a function of the logarithm of iterations. …"

SBH17: Benchmark Database of Barrier Heights for Dissociative Chemisorption on Transition Metal Surfaces حسب T. Tchakoua (7202804)

"…The medium algorithm does require metal lattice constants and interlayer distances that are optimized separately for each functional. …"

Supporting data for "Two Essays on Consumers' Relative Preference for Algorithm-based Versus Human Recommendations" حسب Xunchang Fang (9070241)

"…<p>Recommendation systems, the information filtering tools that provide options of products or services to customers, are powered by programmed algorithms or human experts. Both algorithm-based and human recommendation systems are widely adopted by companies to help consumers make optimal purchasing decisions amid their access to product information. …"

Closed search (±5ppm) comparison against Crux and MSFragger using real-world data from PRIDE repository PXD000612 (top), PXD009861 (middle), and PXD001468 (bottom). حسب Muhammad Usman Tariq (11022141)

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Training parameters for SpeCollate. حسب Muhammad Usman Tariq (11022141)

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Peptides. حسب Muhammad Usman Tariq (11022141)

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Fig 9 - حسب Muhammad Usman Tariq (11022141)

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UMAP. حسب Muhammad Usman Tariq (11022141)

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Process of generating MS/MS spectra from a protein mixture using mass-spectrometry analysis. حسب Muhammad Usman Tariq (11022141)

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Modifications and the character values used in the training data. حسب Muhammad Usman Tariq (11022141)

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2D UMAP visualization of embedded spectra and peptide vectors generated by SpeCollate. حسب Muhammad Usman Tariq (11022141)

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Fig 6 - حسب Muhammad Usman Tariq (11022141)

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Online sextuplet mining for SNAP-loss. حسب Muhammad Usman Tariq (11022141)

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SpeCollate: Deep Similarity Network for proteomics. حسب Muhammad Usman Tariq (11022141)

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A generic proteomics flow. حسب Muhammad Usman Tariq (11022141)

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Space transition methods. حسب Muhammad Usman Tariq (11022141)

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Characteristics of training dataset used. حسب Muhammad Usman Tariq (11022141)

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