Until recently, understanding the regulatory behavior of cells has been pursued through independent analysis of the transcriptome or the proteome. Based on the central dogma, it was generally assumed that there exist a direct correspondence between mRNA transcripts and generated protein expressions. However, recent studies have shown that the correlation between mRNA and Protein expressions can be low due to various factors such as different half lives and post transcription machinery. Thus, a joint analysis of the transcriptomic and proteomic data can provide useful insights that may not be deciphered from individual analysis of mRNA or protein expressions. This article reviews the existing major approaches for joint analysis of transcriptomic and proteomic data. We categorize the different approaches into eight main categories based on the initial algorithm and final analysis goal. We further present analogies with other domains and discuss the existing research problems in this area.

A framework for design of personalized cancer therapy requires the ability to predict the sensitivity of a tumor to anticancer drugs. The predictive modeling of tumor sensitivity to anti-cancer drugs has primarily focused on generating functions that map gene expressions and genetic mutation profiles to drug sensitivity. In this paper, we present a new approach for drug sensitivity prediction and combination therapy design based on integrated functional and genomic characterizations. The modeling approach when applied to data from the Cancer Cell Line Encyclopedia shows a significant gain in prediction accuracy as compared to elastic net and random forest techniques based on genomic characterizations. Utilizing a Mouse Embryonal Rhabdomyosarcoma cell culture and a drug screen of 60 targeted drugs, we show that predictive modeling based on functional data alone can also produce high accuracy predictions. The framework also allows us to generate personalized tumor proliferation circuits to gain further insights on the individualized biological pathway.

The paper describes a newly proposed combination of the two existing Duval Pentagons method utilized for the identification of mineral oil-insulated transformers. The aim of the combination is to facilitate automatic fault identification through computer programs, and at the same time, apply the full capability of both original Pentagons, now reduced to a single geometry. The thorough classification of a given fault (say, of the electrical or thermal kind), employing individual Pentagons 1 and 2, as originally defined, involves a complex geometrical problem that requires the build-up of a convoluted geometry (a regular Pentagon whose axes represent each of five possible combustible gases) to be constructed using computer language code and programming, followed by the logical localization of the geometrical centroid of an irregular pentagon, formed by the partial contribution of individual combustibles, inside two similar structures (Pentagons 1 and 2) that, nonetheless, have different classification zones and boundaries, as more thoroughly explained and exemplified in the main body of this article. The proposed combined approach results in a lower number of total fault zones (10 in the combined Pentagons against 14 when considering Pentagons 1 and 2 separately, although zones PD, S, D1 and D2 are common to both Pentagons 1 and 2), and therefore eliminates the need to solve for two separate Pentagons.

Modeling sensitivity to drugs based on genetic characterizations is a significant challenge in the area of systems medicine. Ensemble based approaches such as Random Forests have been shown to perform well in both individual sensitivity prediction studies and team science based prediction challenges. However, Random Forests generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. This application motivates the need for generation of multivariate ensemble learning techniques that can increase prediction accuracy and improve variable importance ranking by incorporating the relationships between different output responses. In this article, we propose a novel cost criterion that captures the dissimilarity in the output response structure between the training data and node samples as the difference in the two empirical copulas. We illustrate that copulas are suitable for capturing the multivariate structure of output responses independent of the marginal distributions and the copula based multivariate random forest framework can provide higher accuracy prediction and improved variable selection. The proposed framework has been validated on genomics of drug sensitivity for cancer and cancer cell line encyclopedia database.

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