Fig. 3: Expert curation of recommended hits along with features used for hit triaging. | Nature Communications

Fig. 3: Expert curation of recommended hits along with features used for hit triaging.

From: Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer

Fig. 3

Evaluation of top recommended genes by five independent experts indicates that majority of genes can be classified either as A “known resistance markers” or C “previously unknown/less known, credible hits”. Eight genes were labeled as “previously unknown/less known hits with unclear tractability” by the experts (B). Here, unclear tractability refers to the absence of clear path to validate predictions experimentally. “Previously unknown/less known” is definned within the biological context of this study. Features used to generate predictions: full_screen—overall consistency across all conditions in the CRISPR screen; RNASeq_LFC-log2 fold-change from internal RNASeq study; clinical_ES1, clinical_ES2, clinical_ES3-enrichment scores from clinical studies where resistant patients were compared against responders; lit_EGFR, lit_NSCLC-co-occurrence estimates from the literature; pagerank—“popularity” measure of a node; betweenness—centrality estimate of a node. “Agreement” column indicates the number of experts assigning a certain label to a gene. Size of a bubble reflects value normalized across the full set of features for all genes.

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