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10.3390/molecules24020277

A reasonable agreement between the computational and experimental data suggests that PeptoGrid is suitable for discovering functional leads.

PeptoGrid—Rescoring Function for AutoDock Vina to Identify New Bioactive Molecules from Short Peptide Libraries

10.14778/3364324.3364332

Discovering functional dependencies (FDs) usually requires access to all data partitions to find constraints that hold on the whole dataset.

Secure Multi-Party Functional Dependency Discovery

10.17762/IJRITCC.V7I2.5229

route discovering functionality will be incorporated into mobile nodes.

A Novel Methodology for Route Stability Using AODV in MANET

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10.1021/acs.analchem.8b05166

Our techniques and experimental protocols can be potentially used for metabolomic studies of other CSCs and rare types of cells and provide a new approach to discovering functional biomarkers as therapeutic targets.

Metabolomic Studies of Live Single Cancer Stem Cells Using Mass Spectrometry.

10.21203/rs.3.rs-116708/v1

Motivation: Discovering functional modules in protein-protein interaction (PPI) networks by optimization methods remains a longstanding challenge in biology.

Hierarchical hidden community detection for protein complex prediction.

10.1109/ICDE.2019.00149

We address the problem of discovering functional dependencies from distributed big data.

Distributed Discovery of Functional Dependencies

10.1038/s41598-019-42614-y

We confirmed the resistance-causing effects of two pH1N1 changes against oseltamivir in NA activity assays, demonstrating the value of the resource for discovering functionally relevant changes.

Structures and functions linked to genome-wide adaptation of human influenza A viruses

10.1109/TBME.2018.2885812

Significance: Since multichannel recordings have become commonplace in biomedical applications ranging from discovering functional connectivity in the brain to speech data analysis and these recordings are inevitably contaminated by measurement noise, we believe our method has the potential for significant impact.

Estimation of Vector Autoregressive Parameters and Granger Causality From Noisy Multichannel Data

10.1016/j.isci.2019.100754

This research demonstrates the value of multicomponent reactions (MCRs) in interdisciplinary fields by discovering functional polymers for possible practical applications.

High-Throughput Preparation of Antibacterial Polymers from Natural Product Derivatives via the Hantzsch Reaction

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Discovering Functional - A reasonable agreement between the computational and experimental data suggests that PeptoGrid is suitable for discovering functional leads. [1] Discovering functional dependencies (FDs) usually requires access to all data partitions to find constraints that hold on the whole dataset. [2] route discovering functionality will be incorporated into mobile nodes. [3] Our techniques and experimental protocols can be potentially used for metabolomic studies of other CSCs and rare types of cells and provide a new approach to discovering functional biomarkers as therapeutic targets. [4] Motivation: Discovering functional modules in protein-protein interaction (PPI) networks by optimization methods remains a longstanding challenge in biology. [5] We address the problem of discovering functional dependencies from distributed big data. [6] We confirmed the resistance-causing effects of two pH1N1 changes against oseltamivir in NA activity assays, demonstrating the value of the resource for discovering functionally relevant changes. [7] Significance: Since multichannel recordings have become commonplace in biomedical applications ranging from discovering functional connectivity in the brain to speech data analysis and these recordings are inevitably contaminated by measurement noise, we believe our method has the potential for significant impact. [8] This research demonstrates the value of multicomponent reactions (MCRs) in interdisciplinary fields by discovering functional polymers for possible practical applications. [9]

discovering functional dependency

Discovering functional dependencies (FDs) usually requires access to all data partitions to find constraints that hold on the whole dataset. [1] We address the problem of discovering functional dependencies from distributed big data. [2]