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ALL Panel Deletion/Duplication Detection Test

1,990 د.إ

-10%

The “ALL Panel Deletion/Duplication Detection Test” is a specialized genetic test offered by DNA Labs UAE, designed to identify deletions or duplications in the genome that are associated with Acute Lymphoblastic Leukemia (ALL). This advanced diagnostic tool is crucial for understanding the genetic basis of ALL and can provide valuable information for tailoring patient-specific treatment strategies. The test employs cutting-edge technology to scan the genome for abnormalities that could influence the course of the disease or impact the effectiveness of certain treatments. Priced at 1990 AED, this test represents a significant step forward in personalized medicine, offering hope for more effective management of ALL through targeted therapy options.

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ALL Panel DeletionDuplication detection Test

At DNA Labs UAE, we offer the ALL Panel DeletionDuplication detection Test for AED 1990.0. This test is used for the diagnosis of various genetic conditions.

Test Details

The ALL Panel DeletionDuplication detection Test is performed using the MLPA method. It is a genetics test that helps in the detection of panel deletions and duplications. The test requires a sample of bone marrow or peripheral blood, which should be transported immediately for accurate results. The report delivery time is approximately 9-10 days.

Pre Test Information

A Doctors prescription is required for the ALL Panel DeletionDuplication detection Test. However, please note that the prescription is not applicable for surgery and pregnancy cases or individuals planning to travel abroad.

Symptoms and Diagnosis

Panel deletion/duplication detection is a crucial process in maintaining data accuracy and integrity. It involves identifying and flagging duplicate or deleted panels in a dataset or system. Incorrect analysis and results can occur if duplicate or deleted panels are not detected.

There are several approaches and techniques that can be used for panel deletion/duplication detection:

  1. Record-level comparison: This involves comparing each panel record against all other records in the dataset to identify duplicates. Various matching algorithms, such as exact matching, fuzzy matching, or phonetic matching, can be used.
  2. Time-based comparison: By comparing the time periods of panel records, duplicate or overlapping panels can be identified.
  3. Metadata analysis: Analyzing metadata associated with each panel, such as unique identifiers, creation dates, or modification timestamps, can help identify deleted panels or panels that have been modified.
  4. Statistical analysis: Statistical techniques can be used to detect anomalies in panel datasets. Significant differences in values compared to other records in the same time period may indicate a duplicate or deleted panel.
  5. Machine learning: Machine learning algorithms can be trained to detect panel deletion/duplication patterns. These algorithms learn from historical data and identify similar patterns in new datasets.

Regular panel deletion/duplication detection is essential for data quality assurance, ensuring the accuracy and reliability of panel datasets.

Contact Information

If you have any questions or would like to schedule the ALL Panel DeletionDuplication detection Test, please contact our Doctor specializing in Oncology. The test is available in our Genetics Test Department.

Test Name ALL Panel DeletionDuplication detection Test
Components EDTA Vacutainer (2ml)
Price 1990.0 AED
Sample Condition Bone marrow \/ Peripheral blood (Transport immediately)
Report Delivery 9-10 days
Method MLPA
Test type Genetics
Doctor Oncology
Test Department:
Pre Test Information ALL Panel Deletion/Duplication detection can be done with a Doctors prescription. Prescription is not applicable for surgery and pregnancy cases or people planing to travel abroad.
Test Details

Panel deletion/duplication detection refers to the process of identifying and flagging duplicate or deleted panels in a dataset or system. This is important for maintaining data accuracy and integrity, as duplicate or deleted panels can lead to incorrect analysis and results.

There are several approaches and techniques that can be used for panel deletion/duplication detection:

1. Record-level comparison: This involves comparing each panel record against all other records in the dataset to identify duplicates. Various matching algorithms can be used, such as exact matching, fuzzy matching, or phonetic matching.

2. Time-based comparison: Panel datasets often have a time dimension, where each record represents a specific time period. By comparing the time periods of panel records, duplicate or overlapping panels can be identified.

3. Metadata analysis: Panel datasets often have metadata associated with each panel, such as unique identifiers, creation dates, or modification timestamps. Analyzing this metadata can help identify deleted panels or panels that have been modified.

4. Statistical analysis: Statistical techniques can be used to detect anomalies in panel datasets. For example, if a panel record has significantly different values compared to other records in the same time period, it may indicate a duplicate or deleted panel.

5. Machine learning: Machine learning algorithms can be trained on labeled data to detect panel deletion/duplication patterns. These algorithms can learn from historical data and identify similar patterns in new datasets.

Overall, panel deletion/duplication detection is an important step in data quality assurance and should be performed regularly to ensure the accuracy and reliability of panel datasets.