Optimization Based Tumor Classification from Microarray Gene Expression Data

An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types.We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described.The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers

Synergy analysis of collaborative supply chain management in energy systems using multi-period

Abstract Energy, a fundamental entity of modern life, is usually produced using fossil fuels as the primary raw material. A consequence of burning fossil fuels is the emission of environmentally harmful substances. Energy production systems generate steam and electricity that are served to different process customers to satisfy their energy requirement. The improvement of economical and environmental performance of energy production systems is a major issue due to central role of energy in every industrial activity. A systematic approach to identify the synergy among different energy systems is addressed in this paper. The multi-period and discrete-continuous nature of the energy production systems including investment costs are modeled using MILP. The proposed approach is applied on two examples that are simplified versions of an industrial problem. It is shown that the approach presented in this paper is very effective in identifying the synergy among different companies to improve their economical and environmental performance significantly

Identification of novel small molecules targeting core clock proteins to regulate circadian rhythm

© 2021 Elsevier LtdThe circadian rhythms are physiological, biochemical, and behavioral oscillations that cycle every 24 hours to anticipate the daily changes in the external environment. Disruption of the circadian clock in mammals results in increased susceptibility to different types of diseases such as metabolic, mood and sleep disorders and cancer. To this end, different approaches have been taken to find small molecules that have the potential to correct the disrupted circadian clock. In this review, we highlight the recent developments in identifying novel molecules that regulate the activities of the core clock proteins. Finally, we discuss virtual screening-based methods using the crystal structures of core clock proteins for the discovery of small molecules that regulate the circadian rhythm

Recent Advances in Operations Research in Computational Biology, Bioinformatics and Medicine

The EURO Working Group on Operations Research in Computational Biology, Bioinformatics and Medicine held its fourth conference in Poznan-Biedrusko, Poland, June 26–28, 2014. The editorial board of RAIRO-OR invited submissions of papers to a special issue on Recent Advances in Operations Research in Computational Biology, Bioinformatics and Medicine. This special issue includes nine papers that were selected among forty presentations and included in this special issue after two rounds of reviewing

Milp-hyperbox classification for structure-based drug design in the discovery of small molecule inhibitors of SIRTUIN6

Virtual screening of chemical libraries following experimental assays of drug candidates is a common procedure in structure-based drug discovery. However, virtual screening of chemical libraries with millions of compounds requires a lot of time for computing and data analysis. A priori classification of compounds in the libraries as low-and high-binding free energy sets decreases the number of compounds for virtual screening experiments. This classification also reduces the required computational time and resources. Data analysis is demanding since a compound can be described by more than one thousand attributes that make any data analysis very challenging. In this paper, we use the hyperbox classification method in combination with partial least squares regression to determine the most relevant molecular descriptors of the drug molecules for an efficient classification. The effectiveness of the approach is illustrated on a target protein, SIRT6. The results indicate that the proposed approach outperforms other approaches reported in the literature with 83.55% accuracy using six common molecular descriptors (SC-5, SP-6, SHBd, minHaaCH, maxwHBa, FMF). Additionally, the top 10 hit compounds are determined and reported as the candidate inhibitors of SIRT6 for which no inhibitors have so far been reported in the literature