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Related Concept Videos

EDTA: Auxiliary Complexing Reagents01:26

EDTA: Auxiliary Complexing Reagents

EDTA titrations are usually carried out in highly basic conditions, where the fully deprotonated form of EDTA, Y4−, actively complexes with the free metal ions in the solution. Several metal ions precipitate as hydrous oxide (hydroxides, oxides, or oxyhydroxides) under these conditions, lowering the concentration of free metal ions in the solution. For this reason, auxiliary complexing agents or ligands such as ammonia, tartrate, citrate, or triethanolamine are used in EDTA titrations to...
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
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...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Archival Research01:40

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
Atomic Orbitals02:44

Atomic Orbitals

An atomic orbital represents the three-dimensional regions in an atom where an electron has the highest probability to reside. The radial distribution function indicates the total probability of finding an electron within the thin shell at a distance r from the nucleus. The atomic orbitals have distinct shapes which are determined by l, the angular momentum quantum number. The orbitals are often drawn with a boundary surface, enclosing densest regions of the cloud.

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Related Experiment Video

Updated: May 23, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Argo: an integrative, interactive, text mining-based workbench supporting curation.

Rafal Rak1, Andrew Rowley, William Black

  • 1National Centre for Text Mining and School of Computer Science, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. rafal.rak@manchester.ac.uk

Database : the Journal of Biological Databases and Curation
|March 22, 2012
PubMed
Summary
This summary is machine-generated.

Argo is a new text analysis system that integrates text mining tools for biocuration. It offers a user-friendly interface to streamline workflows and improve productivity in manual curation tasks.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

  • Biomedical Informatics
  • Computational Linguistics
  • Text Mining

Background:

  • Biomedical literature curation relies on automated text analysis, often using disparate processing components.
  • Existing text mining tools lack interoperability and are not user-friendly for curators.
  • The Unstructured Information Management Architecture framework addresses interoperability but is developer-focused.

Purpose of the Study:

  • To introduce Argo, an interoperable, integrative, interactive, and collaborative system for text analysis.
  • To provide a convenient graphical user interface for developing text analysis workflows.
  • To enhance productivity in labor-intensive manual biocuration.

Main Methods:

  • Argo employs a modular approach with scalable text analytics components.
  • A web-based interface eliminates complex installation procedures.
  • The system supports user-defined components and accommodates various expertise levels.

Main Results:

  • Argo offers a user-friendly graphical interface for text analysis workflow development.
  • It integrates user-interactive components, like manual annotation editors, into automatic pipelines.
  • A use case demonstrates an in-built manual annotation editor for corpus annotation.

Conclusions:

  • Argo enhances biocuration by providing an interoperable and user-friendly text analysis environment.
  • The system boosts productivity through streamlined workflow development and integration of interactive components.
  • Argo supports diverse user expertise, from text mining to ontology-based curation.