Probing Decision Boundaries in Cancer Data Using Noise Injection and Counterfactual Analysis

No abstract is available for this paper.

• Publication year

  2022

• Venue

  Unknown venue

• Publication date

  Unknown publication date

• Fields of study

  Not labeled

• Identifiers

  No identifiers available.

• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Alibi Explain: Algorithms for Explaining Machine Learning Models

  2021cited by this paper
• Deep Learning with Implicit Handling of Tissue-Specific Phenomena Predicts Tumor DNA Accessibility and Immune Activity

  2019cited by this paper
• Combating Label Noise in Deep Learning Using Abstention

  2019cited by this paper
• Interpretable Counterfactual Explanations Guided by Prototypes

  2019cited by this paper
• RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes

  2018cited by this paper
• CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research

  2018cited by this paper
• PLOD2 in cancer research.

  2017cited by this paper
• I and J

  2012cited by this paper
• and as an in

  year unknowncited by this paper

• An Automation Framework for Comparison of Cancer Response Models Across Configurations

  2023cites this paper