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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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The tip-of-the-tongue (TOT) phenomenon is a cognitive experience characterized by a temporary inability to retrieve specific information from memory despite having a strong feeling of knowing the information. Although individuals cannot access the target word or detail, they frequently recall related elements, such as its initial letter, syllable count, or context. This partial retrieval often causes frustration, as one might recognize a familiar face or know that a name starts with a specific...
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In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
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Related Experiment Video

Updated: Nov 6, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Named Entity Aware Transfer Learning for Biomedical Factoid Question Answering.

Keqin Peng, Chuantao Yin, Wenge Rong

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
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    Summary
    This summary is machine-generated.

    This study enhances biomedical question answering by fine-tuning BioBERT with named entities and using BiLSTM for sentence encoding. The combined approach significantly improves question answering performance on benchmark datasets.

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    Area of Science:

    • Biomedical Natural Language Processing
    • Artificial Intelligence in Healthcare

    Background:

    • Biomedical factoid question answering (BQ) is crucial for information retrieval in healthcare.
    • Effective word representation and named entity recognition are key to improving BQ system performance.
    • Pretrained models like BioBERT have shown promise in biomedical natural language processing tasks.

    Purpose of the Study:

    • To enhance biomedical question answering performance by fine-tuning BioBERT using named entity data.
    • To integrate sentence-level information using BiLSTM alongside token-level features.
    • To improve overall system performance through a bagging ensemble method.

    Main Methods:

    • Fine-tuning the BioBERT model with a biomedical named entity dataset.
    • Employing BiLSTM (Bidirectional Long Short-Term Memory) networks to encode question text.
    • Utilizing a bagging ensemble technique to combine question and token level information.

    Main Results:

    • The proposed framework demonstrated superior performance compared to existing baselines.
    • Fine-tuning BioBERT with named entities improved question answering accuracy.
    • The integration of BiLSTM and bagging further boosted the system's effectiveness.

    Conclusions:

    • The developed framework offers a robust approach for biomedical factoid question answering.
    • Transfer learning, combined with advanced deep learning techniques, significantly advances BQ capabilities.
    • This method provides a reliable way to extract answers from biomedical texts.