From Data Fusion to Knowledge Fusion

The task of {\em data fusion} is to identify the true values of data items (eg, the true date of birth for {\em Tom Cruise}) among multiple observed values drawn from different sources (eg, Web sites) of varying (and unknown) reliability. A recent survey\cite{LDL+12} has provided a detailed comparison of various fusion methods on Deep Web data. In this paper, we study the applicability and limitations of different fusion techniques on a more challenging problem: {\em knowledge fusion}. Knowledge fusion identifies true subject-predicate-object triples extracted by multiple information extractors from multiple information sources. These extractors perform the tasks of entity linkage and schema alignment, thus introducing an additional source of noise that is quite different from that traditionally considered in the data fusion literature, which only focuses on factual errors in the original sources. We adapt state-of-the-art data fusion techniques and apply them to a knowledge base with 1.6B unique knowledge triples extracted by 12 extractors from over 1B Web pages, which is three orders of magnitude larger than the data sets used in previous data fusion papers. We show great promise of the data fusion approaches in solving the knowledge fusion problem, and suggest interesting research directions through a detailed error analysis of the methods.Comment: VLDB'201

MSI testing What is new? What should be considered? German version

Based on new trial data regarding immune checkpoint inhibitors (ICIs), the detection of high-grade microsatellite instability (MSI-H) or underlying deficient mismatch repair protein (dMMR) is now becoming increasingly important for predicting treatment response. For the first time, a PD-1 ICI (pembrolizumab) has been approved by the European Medicines Agency (EMA) for first-line treatment of advanced (stage IV) dMMR/MSI-H colorectal cancer (CRC). Further indications, such as dMMR/MSI-H endometrial carcinoma (EC), have already succeeded (Dostarlimab, 2nd line treatment) and others are expected to follow before the end of 2021. The question of optimal testing in routine diagnostics should therefore be re-evaluated. Based on a consideration of the strengths and weaknesses of the widely available methods (immunohistochemistry and PCR), a test algorithm is proposed that allows quality assured, reliable, and cost-effective dMMR/MSI-H testing. For CRC and EC, testing is therefore already possible at the primary diagnosis stage, in line with international recommendations (NICE, NCCN). The clinician is therefore enabled from the outset to consider not only the predictive but also the prognostic and predispositional implications of such a test when counseling patients and formulating treatment recommendations. As a basis for quality assurance, participation in interlaboratory comparisons and continuous documentation of results (e.g., QuIP Monitor) are strongly recommended

MSI testing What's new? What should be considered?

Based on new trial data regarding immune checkpoint inhibitors (ICIs), the detection of high-grade microsatellite instability (MSI-H) or underlying deficient mismatch repair protein (dMMR) is now becoming increasingly important for predicting treatment response. For the first time, a PD-1 ICI (pembrolizumab) has been approved by the European Medicines Agency (EMA) for first-line treatment of advanced (stage IV) dMMR/MSI-H colorectal cancer (CRC). Further indications, such as dMMR/MSI-H endometrial carcinoma (EC), have already succeeded (Dostarlimab, 2nd line treatment) and others are expected to follow before the end of 2021. The question of optimal testing in routine diagnostics should therefore be re-evaluated. Based on a consideration of the strengths and weaknesses of the widely available methods (immunohistochemistry and PCR), a test algorithm is proposed that allows quality assured, reliable, and cost-effective dMMR/MSI-H testing. For CRC and EC, testing is therefore already possible at the primary diagnosis stage, in line with international recommendations (NICE, NCCN). The clinician is therefore enabled from the outset to consider not only the predictive but also the prognostic and predispositional implications of such a test when counseling patients and formulating treatment recommendations. As a basis for quality assurance, participation in interlaboratory comparisons and continuous documentation of results (e.g., QuIP Monitor) are strongly recommended