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Attribution Theory00:56

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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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In social interactions, individuals frequently seek to understand the motivations and causes behind others' behaviors. This fundamental aspect of social perception, known as attribution, plays a crucial role in shaping interpersonal relationships and guiding future actions. Attribution refers to the cognitive process through which people infer the reasons behind others' behaviors, allowing them to assess character traits, intentions, and situational influences.Attribution Theory and Its...
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Analysis of cancer gene attributes using electrical sensor.

Tanusree Roy1

  • 1University of Engineering and Management, Kolkata 700156, India.

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This study introduces a novel electrical network sensor to differentiate cancer and non-cancer cells using amino acid sequence length and hydrophobicity. The method achieves high accuracy, offering a promising tool for large-scale genomics and cancer gene prediction.

Keywords:
CancerGeneGenomicsModelingNetwork simulationSensor

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Area of Science:

  • Genomics
  • Biophysics
  • Electrical Engineering

Background:

  • Predicting cancer gene attributes from amino acid sequences is crucial for genomics.
  • Current prediction methods face significant challenges in accuracy.
  • Large-scale genomics projects require efficient gene characteristic prediction tools.

Purpose of the Study:

  • To develop a novel electrical network-based sensor for discriminating cancer and non-cancer cells.
  • To utilize amino acid sequence length and hydrophilic/hydrophobic properties for cell discrimination.
  • To assess the efficacy of an electrical model for predicting cancer gene attributes.

Main Methods:

  • An electrical circuit model using resistors, capacitors, and inductors was developed to represent individual amino acids.
  • These models were cascaded to simulate gene systems.
  • Bode and Nyquist analyzers were employed to evaluate electrical responses.

Main Results:

  • The electrical sensor model achieved 89.55% accuracy in discriminating cancer and non-cancer cells.
  • A True Positive rate of 87.06% and a True Negative rate of 95.42% were obtained.
  • The model demonstrated significant promise for predicting cancer gene attributes.

Conclusions:

  • The proposed electrical sensor model is a viable and promising approach for cancer gene attribute prediction.
  • This method offers a new perspective for large-scale genomics studies.
  • The electrical network model effectively utilizes sequence length and hydrophobicity for cell discrimination.