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Interpretation support software(erp) - List of Manufacturers, Suppliers, Companies and Products

Interpretation support software Product List

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QCI Interpret Achieving Transparency and Acceleration in Human Cancer Mutation Evaluation

A highly transparent human cancer mutation interpretation support tool - achieving simplification, time reduction, and cost reduction in human mutation evaluation - responding to a rapid increase in analysis volume at low cost.

QIAGEN Clinical Insight (QCI) Interpret supports the acceleration and scaling of human somatic mutation evaluation for clinical NGS testing institutions. QCI Interpret leverages the QIAGEN Knowledge Base (QKB), which has been continuously updated through manual curation for approximately 25 years, providing evidence to support decision-making. The vast knowledge and information included in the QKB enable the interpretation and reporting of mutations in accordance with international guidelines (such as AMP/ASCO/CAP), the presentation of supporting references, and the reduction of VUS (variants of uncertain significance). QCI Interpret simplifies, shortens, and reduces the cost of the process for testing institutions to derive clinical significance from mutation information. **Features** - High transparency in mutation evaluation with evidence - Evaluation in accordance with guidelines - Reduction of the number of variants of uncertain significance (VUS) - Utilization of a curation knowledge base by both human expertise and AI - Support for decision-making by oncologists and molecular pathologists - Proven interpretation of over 4 million patient-derived mutations

  • Bioinformatics

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[Example] Support for interpreting machine learning models using XAI.

Implemented PFI, PD, ICE, and SHAP! Output feature importance and prediction reasons, and interpreted them!

In recent years, while highly accurate machine learning models have been developed, there is a problem of increasing black-box nature of these models. Since it is difficult to use them for decision-making in their black-box state, we aim to demonstrate the rationale behind predictions by using XAI. Our company has implemented model construction and XAI (PFI, PD, ICE, SHAP). We compiled the analysis results and the interpretations provided by XAI into a report and submitted it. 【Case Overview】 ■ Issues - In recent years, while highly accurate machine learning models have been developed, there is a problem of increasing black-box nature of these models. - It is difficult to use them for decision-making in their black-box state, so we want to demonstrate the rationale behind predictions by using XAI. ■ Work Content - Model construction and implementation of XAI (PFI, PD, ICE, SHAP) - Interpretation of model prediction results using XAI *For more details, please refer to the PDF document or feel free to contact us.

  • Embedded system design service
  • Other contract services

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