_Applied_  
_sciences_  
_Intelligent Microarray Data Analysis through_  
_Non-negative Matrix Factorization to Study Human_  
_Multiple Myeloma Cell Lines_  
**[PERSON] 1,***_**2, [PERSON] 2, [PERSON] 2, [PERSON] 2,**  
**[PERSON] 3, [PERSON] 2,* and [PERSON] 2,*_  
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process. The former pertains to the domain expertise that is used to design the research questions that drive the analysis. The latter use automatic techniques to extract unknown patterns from data. However, there is a language gap between these two figures. Data visualization, dimensionality reduction, and clustering techniques allow the simplification of original data, making them more understandable and easier to analyze by non-technicians [3]. In this scenario, the non-negative matrix factorization (NMF) was shown to be suitable for IDA [4]. NMF is a low-rank approximation technique that, due to its non-negativity constraint, is able to describe original data as a linear additive combination of hidden factors: i.e., pieces of images in image recognition, topics in text mining, and metagenes (MGs) in the micro-array analysis [5; 6; 7; 8]. NMF allows dimensionality reduction and clustering of original data, both useful for data understanding. On one hand clustering methods are used to group data according to similarity criteria [9]. On the other hand, dimensionality reduction methods represent data in a low dimensional space by discarding noise and worthless information and highlighting noteworthy elements. Both methods allow simplifying the complexity of original data, to bring out hidden structures among them. Differently from other similar methods, such as singular value decomposition (SVD), principal component analysis (PCA), and k-means, NMF gives easily interpretable results due to its non-negativity constraint [3]. In the last decade, NMF was widely used to analyze biological data [9; 10]. Gene expression profiles, coming from microarray chips, are represented as numerical non-negative high-dimensional data. Thus, dimensionality reduction and clustering techniques are commonly used to discard unimportant information and highlight the relevant data. In this work, 40 human myeloma cell lines (HMCLs) were investigated. Particularly a selection of gene expression profiles (GEPs) that are representative of the human multiple myeloma diseases were analyzed through NMF. The NMF part-based representation simplifies the data and the relationships in them. Moreover, result visualizations are used with the aim to facilitate the data understanding from the domain experts. Multiple myelomas (MM) is still an unmet clinical need, being an incurable malignant disease of plasma cells affecting approximately 25,000 new patients per year in Europe and characterized by a marked genetic heterogeneity [11; 12]. The major limitation to identify a proper MM therapy is mainly caused by a wide inter individual variation in response to the clinically available drugs [11; 13; 14; 15]. This heterogeneous response to the treatment is mostly due to the molecular characteristics of the tumor, i.e., the differences in the involved GEP could determine the drug-resistance [16; 17; 18]. Deciphering key changes in gene expression levels underlying personalized sensitivity to chemotherapy is, therefore, essential to predict the efficacy of anticancer drugs and to prevent delay in the selection of more effective treatment strategies [19]. Cell lines are currently preferred in vitro pre-clinical tools to screen the effects of new drugs or combinations of drugs. Often, the efficacy of a drug candidate may vary depending on the cell line based model used for testing, probably also due to the different GEP of the used cells.