ArticleBiomolecules2020
Ranking Series of Cancer-Related Gene Expression Data by Means of the Superposing Significant Interaction Rules Method.
Article in Biomolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
- Improving the SSIR Method: Implementation of an Exhaustive Multilevel Scan for Categorical Variables.International journal of molecular sciences · 2026Article
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2 authors.
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Abstract
The Superposing Significant Interaction Rules (SSIR) method is a combinatorial procedure that deals with symbolic descriptors of samples. It is able to rank the series of samples when those items are classified into two classes. The method selects preferential descriptors and, with them, generates rules that make up the rank by means of a simple voting procedure. Here, two application examples are provided. In both cases, binary or multilevel strings encoding gene expressions are considered as descriptors. It is shown how the SSIR procedure is useful for ranking the series of patient transcription data to diagnose two types of cancer (leukemia and prostate cancer) obtaining Area Under Receiver Operating Characteristic (AU-ROC) values of 0.95 (leukemia prediction) and 0.80-0.90 (prostate). The preferential selected descriptors here are specific gene expressions, and this is potentially useful to point to possible key genes.
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