Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Author: search.filters.author.Gönen, MehmetWoS Q: search.filters.wosQuartile.Q1

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  • Article
    Citation - WoS: 560
    Citation - Scopus: 607
    A Community Effort To Assess and Improve Drug Sensitivity Prediction Algorithms
    (Nature Publishing Group, 2014) Costello, James C.; Heiser, Laura M.; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P.; Wang, Nicholas J.; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A.; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; NCI-DREAM Community; Karaçalı, Bilge; Collins, James J.; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W.; Stolovitzky, Gustavo
    Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.
  • Article
    Citation - WoS: 15
    Citation - Scopus: 18
    Preparation and Characterization of Magnesium Stearate, Cobalt Stearate, and Copper Stearate and Their Effects on Poly(vinyl Chloride) Dehydrochlorination
    (John Wiley and Sons Inc., 2015) Gönen, Mehmet; Egbuchunam, Theresa Obuajulu; Balköse, Devrim; İnal, Fikret; Ülkü, Semra
    Preparation and characterization of pure metal soaps and investigation of their effects on poly(vinyl chloride) (PVC) dehydrochlorination were the objectives of the present study. Magnesium stearate (MgSt2), cobalt stearate (CoSt2), and copper stearate (CuSt2) were prepared by a precipitation method. An aqueous sodium stearate (NaSt) solution was mixed at 500 rpm with respective metal salt solutions at 75oC. The precipitates that formed were collected by filtration, washed with water, and ultimately dried at 105oC under reduced pressure. Lamellar crystals that melted on heating were obtained. Solid-liquid phase transitions were observed by optical microscopy at 160oC, 159oC, and 117oC for MgSt2, CoSt2, and CuSt2, respectively. However, the melting points of MgSt2, CoSt2, and CuSt2 were determined as 115oC, 159oC, and 111oC, respectively, by analysis by differential scanning calorimetry. The onset temperature of the mass loss was the lowest at 255oC for CuSt2 and the lowest activation energy for thermal decomposition was 18 kJ/mol for CuSt2. CoSt2 was effective in extending the induction time of PVC dehydrochlorination at both 140oC and 160oC. The activation energy calculated from stability time decreased from 175 kJ/mol for a blank PVC sample to 114, 105, and 107 kJ/mol for MgSt2, CoSt2, and CuSt2-containing PVC samples, respectively. All three metal soaps accelerated the dehydrochlorination of PVC. J. VINYL ADDIT. TECHNOL., 21:235-244, 2015.