Use Cases
Drill down to see what use cases Purgo AI's Agent can handle.
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Accelerate Drug Discovery
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Genetic Target Identification
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QSAR Modeling (Quantitative Structure Activity Relationship)
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Geneformer Modeling (Gene Expressions & Network Biology)
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Image Classification (eg. Digital Path.)
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Chromatography Insights
Streamline Clinical Development
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Clinical Trial Protocol Design
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Clinical Trial Site Selection
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Drug Repurposing
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Modernize Clinical Data Repository
Build a FAIR Data Platform
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Knowledge Graphs for R&D
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Medical Image Processing & Management (Pixels for DICOM)
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Omics Data Management
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Research Assistant
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BioMedical Information Retrieval
View by Business Solution
Automate QA of Clinical Data
DQ Validation on Queries in C360 Config Table
Requirement:
Prepare a PySpark code to perform DQ comparison between sql queries in c360\config table and the target table as per the attached excel sheet. Display the DQ validation result which should have metric\name,validation, result (PASS/FAIL). Ignore DQ validation for the sql queries whose target table is “NA“ in c360 config table.
DQ Validation on Queries in C360 Config Table
Requirement:
Prepare a PySpark code to perform DQ comparison between sql queries in c360\config table and the target table as per the attached excel sheet. Display the DQ validation result which should have metric\name,validation, result (PASS/FAIL). Ignore DQ validation for the sql queries whose target table is “NA“ in c360 config table.
Automate Reporting (e.g., OTIF)
AD-344: Aggregate REP_SHARE_OF_VOICE Metrics by Reporting Date for Repatha
*Requirements*: Develop SQL Logic, Read the table 'stitched_pmr_consolidation_data' and extracts Share of Voice (SoV) metrics specifically for the product *Repatha*. The logic must filter the dataset to include only records where *metric = 'REP_SHARE_OF_VOICE'*, *frequency = 'QUARTERLY'*, *speciality = 'ALL'*, and *product_name = 'REPATHA'*. Additionally, the results must be restricted to reporting dates on or after *September 1, 2023*. After applying these filters, the SQL logic should group the dataset by *product_name* and *time_stamp*, and compute the *SUM(value)* for each group to derive the total Share of Voice for Repatha per reporting period. The final transformed output should expose three fields — *product_name*, *time_stamp*, and *total_share_of_voice* — and must be published through a finalized view named *stitched_pmr_consolidation_data_vw*.
*Final Output*: Show the results.
Unity Catalog: stitched_pmr_consolidation_data_vw
AD-342: Customer_360 - Fix CPD Value Inflation Issue Across Brands for BLUE BALL Sales Team
*Introduction*: Users observed that *CPD (Calls Per Day)* values are appearing doubled or inflated across multiple brands and quarters in the dashboards. When the backend table s_field_reporting_activity_sales_team_brand_interactions_intmd_union_all was checked, the CPD values were already inflated at the source. It is focuses on validating the source calculations and identifying the correct CPD computation using the formula CPD = rep_calls_total_calls / rep_calls_total_days_in_territory. The team needs a query to reproduce the issue and verify if the inflated values match the dashboard output and source table.
*Requirements*: Develop SQL Logic, Read the file c360_s_field_report_activity_sales_team_brand_interactions_intmd_union_all. Filter the dataset for the following values, business_unit_code = *BCBU,* sales_team_grouping = *BLUE BALL,* classification_type = *BLUE BALL ALL PORTFOLIO TARGET,* interaction_channel = *ALL,* time_bucket_id = *CQTDW,* classification_value IN (‘EB’, ‘ED’, ‘ALL_TARGETS_PROFS’) Use the CPD formula as Total_CPD = SUM(rep_calls_total_calls / rep_calls_total_days_in_territory). Round off CPD value to *one decimal*. Group data by all non-aggregated columns. Return the dataset so the inflated CPD values can be compared with dashboard values.
Unity catalog: c360_s_field_report_activity_sales_team_brand_interactions_intmd_union_all