google/deepvariant
Dec 2024 – Dec 2024
1 Month active
Languages Used
C++Python
Technical Skills
Data AnalysisDeep LearningKerasMachine LearningSoftware EngineeringTensorFlow
tsushi
PROFILE
Tsushi
Tsushi developed Variant Type Aware Metrics and Evaluation for the google/deepvariant repository, enabling granular assessment of SNP and INDEL variant types. Using Python, TensorFlow, and Keras, Tsushi updated data providers to include variant type information and implemented a dedicated metrics module for specialized calculations. The training and tuning pipelines were modified to leverage these new metrics, allowing for detailed model evaluation by variant type. This work addressed the need for more actionable business insights and improved model selection by providing variant-type-specific performance data. The depth of the implementation reflects a strong understanding of data analysis and machine learning workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
234
Activity Months1
Your Network
4166 people
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024Concise monthly summary for 2024-12 focusing on google/deepvariant. Delivered Variant Type Aware Metrics and Evaluation, enabling SNP/INDEL-specific metrics, updating data providers, and introducing a dedicated metrics module. Training and tuning pipelines updated to use these metrics, enabling granular performance evaluation by variant type and actionable business insights. Commit: 2043b96d23ce3e773ddd0b38dfd7df6b0fd89043.
1 Commits • 1 Features
Dec 1, 2024Concise monthly summary for 2024-12 focusing on google/deepvariant. Delivered Variant Type Aware Metrics and Evaluation, enabling SNP/INDEL-specific metrics, updating data providers, and introducing a dedicated metrics module. Training and tuning pipelines updated to use these metrics, enabling granular performance evaluation by variant type and actionable business insights. Commit: 2043b96d23ce3e773ddd0b38dfd7df6b0fd89043.
December 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Data AnalysisDeep LearningKerasMachine LearningSoftware EngineeringTensorFlow
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