PRISMΔDB
SAE MODEL #2

FEATURE 708

/ Drug Resistance Mechanisms in Cancer
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This page shows the detailed analysis of a specific feature in the Sparse Autoencoder (SAE). It includes the semantic interpretation generated by the LLM, the top activating documents that trigger this feature, and statistical metrics like density and activation distribution.

Semantic Interpretation
gemma3:12b #12

The neuron activates when text discusses mechanisms of drug resistance, particularly in the context of cancer treatment. This includes clonal selection of mutated cells, resistance to tyrosine kinase inhibitors (TKIs) like imatinib, and the development of nonoverlapping resistance patterns with sequential drug exposure. The focus is on the molecular and cellular level, often involving specific gene mutations (e.g., BCR-ABL mutations) and their impact on drug efficacy. The concept also encompasses strategies to overcome or manage resistance, such as dose adjustments, combination therapies, and drug selection schemes. The level of granularity is at the molecular and clinical level, describing specific drug interactions and patient responses.

STATISTICS & DISTRIBUTION
Statistics Explained

Density
Fraction of documents where this feature activates at least once.
Higher density = feature appears frequently across the dataset.

Peak Activation
Maximum activation value observed for this feature over all documents.

Activation Histogram
Distribution of all activation values for this feature. Each bar represents a bin (range) of values, and its height shows how many documents fall in that range.

Density
0.04400
Peak Act
3.70
0.0 Max
Global Context
TOP ACTIVATING CONTEXTS
DOC #477 ANALYZE
ACT: 3.7012
Identification and functional signature of genes regulated by structurally different ABL kinase inhibitors. Dasatinib is an ATP-competitive, multi-targeted SRC and ABL kinase inhibitor that can bind BCR-ABL in both the active and inactive conformations. From a clinical standpoint, dasatinib is particularly attractive because it has been shown to i…
DOC #471 ANALYZE
ACT: 3.3842
Efficacy of various doses and schedules of second-generation tyrosine kinase inhibitors. Imatinib is one of the most potent cancer therapeutic agents identified to date. Before the introduction of this tyrosine kinase inhibitor (TKI), 5-year survival in chronic myeloid leukemia (CML) was approximately 40%-60%, but since the introduction of imatini…
DOC #980 ANALYZE
ACT: 3.2432
Phase I study of N(1),N(11)-diethylnorspermine in patients with non-small cell lung cancer. Polyamines are essential for tumor growth; consequently, agents that interfere with their metabolisms have been developed as antineoplastic agents. Diethylnorspermine (DENSPM) is one such agent. A focused Phase I clinical trial in patients with advanced non…
DOC #34 ANALYZE
ACT: 3.0251
Methylglyoxal bis(butylamidinohydrazone) exhibits antitumor effect on human malignant melanoma cells but reduces the antitumor action of cisplatin. The antitumor effect of a polyamine biosynthetic pathway inhibitor methylglyloxal bis(butylamidinohydrazone) (MGBB) on human malignant melanoma (HMG) cells and its combination effect with cisplatin wer…
DOC #876 ANALYZE
ACT: 2.7112
[Resistance to tumor specific therapy with imatinib by clonal selection of mutated cells]. A 60-year-old woman presented with night-sweats and increasing weakness. Physical examination revealed no abnormalities. For 27 years she had been treated for Philadelphia-positive chronic myeloid leukemia (CML). Because of progressive disease treatment with…
DOC #722 ANALYZE
ACT: 2.6786
2,4-Diaminopyrimidine inhibitors of c-Met kinase bearing benzoxazepine anilines. Elaboration of the SAR around a series of 2,4-diaminopyrimidines led to a number of c-Met inhibitors in which kinase selectivity was modulated by substituents appended on the C4-aminobenzamide ring and the nature of the C2-aminoaryl ring. Further lead optimization of …
TOPOLOGY
CORRELATIONS
W – Weight-space · similarity between decoder vectors (features that point in similar directions in the embedding space).
D – Data / co-activation · features that tend to fire together on the same documents (co-occurrence in the dataset).