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Related Concept Videos

Effects of Chemicals: Overview01:27

Effects of Chemicals: Overview

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Drugs, encompassing various chemical compounds from natural sources, lab synthesis, or genetic engineering, elicit different biological responses in living organisms. Some of these responses are desirable or therapeutic, while others are undesirable. The primary goal of administering a drug is to achieve a therapeutic effect, that is, to address a specific disease or health condition. Any concurrent effects outside of this therapeutic outcome are considered undesirable. These undesirable...
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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Classification of Illness01:17

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Factors Influencing Drug Absorption: Disease States and Pharmacology01:25

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Multiple disease states can significantly influence the oral drug absorption process by affecting blood flow and the functionality of the gastrointestinal (GI) system. Various GI diseases, including conditions that alter GI motility, such as diarrhea, decreased acid secretions (achlorhydria), and infections, have been associated with reduced drug absorption.
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Enhanced Elimination of Poison01:26

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Poison can be effectively removed from the gastrointestinal (GI) tract through various decontamination procedures.
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Toxic Reactions: Overview01:26

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When toxic substances penetrate the human body, they disseminate to various tissues, undergoing metabolic changes. This process yields reactive metabolites that may covalently bind with specific target molecules, resulting in toxicity.
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Related Experiment Video

Updated: Mar 21, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A knowledge-poor approach to chemical-disease relation extraction.

Firoj Alam1, Anna Corazza2, Alberto Lavelli3

  • 1Department of Information Engineering and Computer Science, University of Trento, Italy.

Database : the Journal of Biological Databases and Curation
|May 19, 2016
PubMed
Summary

This study presents a machine learning approach for identifying chemical-disease relations in biomedical literature. The method, requiring minimal domain knowledge, achieved competitive results in a major scientific challenge.

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Biology

Background:

  • Extracting chemical-disease relations from biomedical literature is crucial for understanding drug safety and disease mechanisms.
  • Existing methods often rely heavily on extensive domain-specific knowledge, limiting their portability and scalability.
  • The BioCreative V challenge provided a benchmark for evaluating automated relation extraction systems.

Purpose of the Study:

  • To develop and evaluate a knowledge-poor approach for extracting chemical-disease relations from PubMed abstracts.
  • To design a portable and general-purpose system with limited reliance on domain-specific resources.
  • To improve upon initial performance in the BioCreative V challenge, particularly for chemical-induced disease (CID) relation extraction.

Main Methods:

  • A machine learning-based strategy was employed, integrating a small set of domain-specific resources.
  • Freely available tools were utilized for data preprocessing.
  • The system exclusively used datasets provided by the BioCreative V organizers.

Main Results:

  • The system achieved a ranking of 5th out of 16 participants in Disease Named Entity Recognition and Normalization (DNER).
  • The system ranked 7th out of 18 participants in the Chemical-induced diseases (CID) relation extraction task.
  • Follow-up experiments demonstrated improvements in performance through approach extension and further experimentation.

Conclusions:

  • A knowledge-poor, machine learning approach can effectively extract chemical-disease relations from biomedical text.
  • The developed system is easily portable and requires minimal domain-specific knowledge.
  • The approach shows promise for continued improvement and broader application in biomedical text mining.