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Related Concept Videos

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
P-value01:10

P-value

P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more unlikely...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...

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Related Experiment Videos

Dynamic programming re-ranking for PPI interactor and pair extraction in full-text articles.

Richard Tzong-Han Tsai1, Po-Ting Lai

  • 1Department of Computer Science & Engineering, Yuan Ze University, Chung-Li, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw

BMC Bioinformatics
|February 24, 2011
PubMed
Summary
This summary is machine-generated.

A new re-ranking algorithm improves protein-protein interaction (PPI) database curation by enhancing gene identifier normalization (INT) and interaction pair identification (IPT). This method boosts performance significantly, aiding researchers in accessing vital biological data.

Related Experiment Videos

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Text Mining

Background:

  • Protein-protein interactions (PPIs) are crucial but difficult to retrieve without specialized databases.
  • Text-mining systems can automate the identification and normalization of interacting genes (INT) and interaction pairs (IPT) from scientific literature.

Purpose of the Study:

  • To develop and evaluate a novel re-ranking algorithm to improve the accuracy of gene interactor normalization (INT) and interaction pair identification (IPT).
  • To enhance the efficiency and effectiveness of curating protein-protein interaction databases.

Main Methods:

  • A support vector machine (SVM)-based ranking procedure was employed for the interactor normalization task (INT).
  • A new re-ranking algorithm considering co-occurrence among identifiers was developed and applied.
  • An unsupervised approach was used to find associations among interactors for the interaction pair task (IPT).
  • Dynamic programming was implemented for efficient score computation.

Main Results:

  • The re-ranking algorithm improved the INT task performance (AUC iP/R) by 1.84%.
  • The enhanced INT results boosted the unsupervised IPT system performance to an AUC iP/R of 23.86%, outperforming the best previous system.
  • Re-ranked INT results led to a significant 7.84% improvement in AUC iP/R for the INT/IPT system compared to SVM-only ranking.

Conclusions:

  • The proposed re-ranking algorithm effectively improves both INT and IPT tasks by leveraging identifier co-occurrence.
  • Combining re-ranked INT results with an unsupervised approach enhances IPT performance.
  • The use of dynamic programming for score computation offers a faster and more efficient solution.