A modified genetic algorithm for forecasting fuzzy time series

Egrioglu, Erol/0000-0003-4301-4149; Bas, Eren/0000-0002-0263-8804WOS: 000341094000008Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time series methods consist of three stages, namely, fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are frequently used in these stages with genetic algorithms being the most popular of these algorithms owing to their rich operators and good performance. However, the mutation operator of a GA may cause some negative results in the solution set. Thus, we propose a modified genetic algorithm to find optimal interval lengths and control the effects of the mutation operator. The results of applying our new approach to real datasets show superior forecasting performance when compared with those obtained by other techniques

Recurrent type-1 fuzzy functions approach for time series forecasting

Tak, Nihat/0000-0001-8796-5101; Egrioglu, Erol/0000-0003-4301-4149WOS: 000419006000005Forecasting the future values of a time series is a common research topic and is studied using probabilistic and non-probabilistic methods. For probabilistic methods, the autoregressive integrated moving average and exponential smoothing methods are commonly used, whereas for non-probabilistic methods, artificial neural networks and fuzzy inference systems (FIS) are commonly used. There are numerous FIS methods. While most of these methods are rule-based, there are a few methods that do not require rules, such as the type-1 fuzzy function (T1FF) approach. While it is possible to encounter a method such as an autoregressive (AR) model integrated with a T1FF, no method that includes T1FF and the moving average (MA) model in one algorithm has yet been proposed. The aim of this study is to improve forecasting by taking the disturbance terms into account. The input dataset is organized using the following variables. First, the lagged values of the time series are used for the AR model. Second, a fuzzy c-means clustering algorithm is used to cluster the inputs. Third, for the MA, the residuals of fuzzy functions are used. Hence, AR, MA, and the degree of memberships of the objects are included in the input dataset. Because the objective function is not derivative, particle swarm optimization is preferable for solving it. The results on several datasets show that the proposed method outperforms most of the methods in literature

A synthetic lethal siRNA screen identifying genes mediating sensitivity to a PARP inhibitor

Inhibitors of poly (ADP-ribose)-polymerase-1 (PARP) are highly lethal to cells with deficiencies in BRCA1, BRCA2 or other components of the homologous recombination pathway. This has led to PARP inhibitors entering clinical trials as a potential therapy for cancer in carriers of BRCA1 and BRCA2 mutations. To discover new determinants of sensitivity to these drugs, we performed a PARP-inhibitor synthetic lethal short interfering RNA (siRNA) screen. We identified a number of kinases whose silencing strongly sensitised to PARP inhibitor, including cyclin-dependent kinase 5 (CDK5), MAPK12, PLK3, PNKP, STK22c and STK36. How CDK5 silencing mediates sensitivity was investigated. Previously, CDK5 has been suggested to be active only in a neuronal context, but here we show that CDK5 is required in non-neuronal cells for the DNA-damage response and, in particular, intra-S and G2/M cell-cycle checkpoints. These results highlight the potential of synthetic lethal siRNA screens with chemical inhibitors to define new determinants of sensitivity and potential therapeutic targets