Gene networks have gained considerable relevance in cancer research, enabling the representation of complex biological relationships that provide insights into the mechanisms driving tumor development and progression. The increasing availability of biological data facilitates the construction of clinically relevant gene networks by integrating multiple information sources. Specifically, we consider mutation data, patient survival data, and protein-protein interaction data to identify networks whose genes are recurrently mutated, significantly involved in patient survival, and functionally associated. To this end, we apply multi-objective optimization to simultaneously maximize survival impact, functional association, and mutation coverage. Herein, we introduce MOTEA-GENSU (Multi-Objective Two-archive Evolutionary Algorithm to discover GEne Networks involved in SUrvival), a novel method that employs two collaborative archives and intelligent evolutionary operators to guide the generation of high-quality gene networks. Evaluation across 27 real biological scenarios covering diverse cancer types shows that MOTEA-GENSU outperforms existing methods, achieving superior results in 92.6% of comparisons, with improvements of up to 315.8% over the best-performing competing approach, and consistently surpassing all state-of-the-art methods on average within each evaluated dataset. Biological analysis of the identified networks validates their functional coherence and significant impact on cancer patient survival, revealing clinically relevant networks composed of genes with demonstrated prognostic value.

Abstract The use of keywords is increasingly being applied across diverse domains, including the movie industry, whose main platforms are adopting advanced natural language processing techniques. Algorithms for automatic extraction of keywords can provide relevant information in this domain. The most novel approaches covering several categories (statistics, graphs, word embedding, and hybrid) have been considered in a model study framework. They have been implemented, applied, and evaluated with standard datasets. In addition, a movie dataset with gold standard keywords, based on textual metadata from synopses and reviews, has been specifically developed for this scope. Keyword extraction models have been evaluated in terms of F-score and computation time. Furthermore, content analysis, both quantitative and qualitative, of the extracted keywords in the movie context has been performed. Results show a great variability in model performance and computation time among the different models. Qualitative results, in addition to F-score and computation time, demonstrate that keyword extraction works better with synopses than with reviews. The quantitative content analysis revealed that EmbedRank effectively reduces redundancy and limits the use of proper nouns, leading to high-quality keywords.