Browsing by Author "Zhu, G."
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Item Integrating Hi-C and FISH Data for Modeling of the 3D Organization of Chromosomes(Nature Publishing Group, 2019-05-03) Abbas, A.; He, X.; Niu, J.; Zhou, B.; Zhu, G.; Ma, T.; Song, J.; Gao, J.; Zhang, Michael Q.; Zeng, J.; Zhang, Michael Q.The new advances in various experimental techniques that provide complementary information about the spatial conformations of chromosomes have inspired researchers to develop computational methods to fully exploit the merits of individual data sources and combine them to improve the modeling of chromosome structure. Here we propose GEM-FISH, a method for reconstructing the 3D models of chromosomes through systematically integrating both Hi-C and FISH data with the prior biophysical knowledge of a polymer model. Comprehensive tests on a set of chromosomes, for which both Hi-C and FISH data are available, demonstrate that GEM-FISH can outperform previous chromosome structure modeling methods and accurately capture the higher order spatial features of chromosome conformations. Moreover, our reconstructed 3D models of chromosomes revealed interesting patterns of spatial distributions of super-enhancers which can provide useful insights into understanding the functional roles of these super-enhancers in gene regulation. © 2019, The Author(s).Item Optimizing Data Distribution for Loops on Embedded Multicore with Scratch-Pad Memory(Academy Publisher) Gao, Q.; Zhuge, Q.; Zhang, J.; Zhu, G.; Sha, Edwin Hsing-Mean; 0000 0000 3259 5943 (Sha, EHM); 2003002797 (Sha, EHM)Software-controlled Scratch-Pad Memory (SPM) is a desirable candidate for on-chip memory units in embedded multi-core systems due to its advantages of small die area and low power consumption. In particular, data placement on SPMs can be explicitly controlled by software. Therefore, the technique of data distribution on SPMs for multi-core system becomes critical in exploiting the advantages of SPM. Previous research efforts on data allocation did not consider the placement of array data accessed in loops. Loops are the most time-consuming and energy-consuming part for most of the computationintensive applications. In this paper, we propose a highperformance, low-overhead data distribution technique, the Iterational Optimal Loop Data Distribution Algorithm based on dynamic programming. It optimizes data allocation of both scalar and array data for embedded multi-core systems with SPMs. The experimental results show that the IOLDD algorithm reduces the energy consumption by 30.12% and 14.52% on average compared with random data distribution and greedy stretagy, respectively. It also reduces the memory access time by 18.45% and 18.38% on average compared with the random distribution strategy and the greedy strategy, respectively.