ALMOST OPTIMAL SOLUTIONS TO k-CLUSTERING PROBLEMS

Kumar, Pankaj; Kumar, Piyush

doi:10.1142/s0218195910003372

Cited by 13 publications

(8 citation statements)

References 41 publications

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“…SUMOylation is reversed by six SENPs in mammalian cells, including SENP1–3 and SENP5–7. [ 28 ] Notably, SENP1 and SENP2 can deconjugate all SUMO isoforms, whereas SENP3 and SENP5–7 preferentially deconjugate SUMO2/3‐modified proteins and SUMO chains. [ 29,30 ] Since TEAD1 is modified by SUMO1, we examined the potential role of SENP1–2 in TEAD1 deSUMOylation.…”

Section: Resultsmentioning

confidence: 99%

SUMOylation of TEAD1 Modulates the Mechanism of Pathological Cardiac Hypertrophy

Shi,

Dang,

Huang

et al. 2024

Advanced Science

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Pathological cardiac hypertrophy is the leading cause of heart failure and has an extremely complicated pathogenesis. TEA domain transcription factor 1 (TEAD1) is recognized as an important transcription factor that plays a key regulatory role in cardiovascular disease. This study aimed to explore the role of TEAD1 in cardiac hypertrophy and to clarify the regulatory role of small ubiquitin‐like modifier (SUMO)‐mediated modifications. First, the expression level of TEAD1 in patients with heart failure, mice, and cardiomyocytes is investigated. It is discovered that TEAD1 is modified by SUMO1 during cardiac hypertrophy and that the process of deSUMOylation is regulated by SUMO‐specific protease 1 (SENP1). Lysine 173 is an essential site for TEAD1 SUMOylation, which affects the protein stability, nuclear localization, and DNA‐binding ability of TEAD1 and enhances the interaction between TEAD1 and its transcriptional co‐activator yes‐associated protein 1 in the Hippo pathway. Finally, adeno‐associated virus serotype 9 is used to construct TEAD1 wild‐type and KR mutant mice and demonstrated that the deSUMOylation of TEAD1 markedly exacerbated cardiomyocyte enlargement in vitro and in a mouse model of cardiac hypertrophy. The results provide novel evidence that the SUMOylation of TEAD1 is a promising therapeutic strategy for hypertrophy‐related heart failure.

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Section: Resultsmentioning

confidence: 99%

SUMOylation of TEAD1 Modulates the Mechanism of Pathological Cardiac Hypertrophy

Shi,

Dang,

Huang

et al. 2024

Advanced Science

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“…This algorithm gives a (1 + ǫ)-approximation factor algorithm which runs in 2 O((k log k)/ǫ 2 ) dn in R d . Another algorithm based on coresets runs in O(k n ) [19] and it is claimed that the running time is much less than the worst case and thus it's possible to solve some problems when k is small (say k < 5).…”

Section: Related Workmentioning

confidence: 99%

Improvements on the k-center Problem for Uncertain Data

Alipour

Jafari

2018

Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

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In real applications, database systems should be able to manage and process data with uncertainty. Any real dataset may have missing or rounded values, also the values of data may change by time. So, it becomes important to handle these uncertain data. An important problem in database technology is to cluster these uncertain data.In this paper, we study the k-center problem for uncertain points in a general metric space. First we present a greedy approximation algorithm that builds k centers using a farthest-first traversal in k iterations. This algorithm improves the approximation factor of the unrestricted assigned k-center problem from 10 to 6. Next we restrict the centers to be selected from a finite set of points and we show that the optimal solution for this restricted setting is a 2approximation factor solution for the optimal solution of the assigned k-center problem. Using this idea we improve the approximation factor of the unrestricted assigned k-center problem to 4 by increasing the running time mildly. ACM Subject ClassificationTheory of computation → Database theory, Theory of computation → Facility location and clustering

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“…For Euclidean space, an algorithm with time complexity O(|P | O( √ k) ) is known [14]. An (1 + ǫ) approximation algorithm is given in [16] that runs in O(k |X| ) time, and the authors show empirically that it is efficient for small k values, say k ≤ 5. The farthest point clustering method, which greedily picks the farthest point from the selected points until k centers are found, achieves 2-approximation [11].…”

Section: [Location Problems In a Spatial Setting]mentioning

confidence: 99%

Finding multiple new optimal locations in a road network

Liu

Chen

et al. 2016

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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We study the problem of optimal location querying for locationbased services in road networks, which aims to find locations for new servers or facilities. The existing optimal solutions on this problem consider only the cases with one new server. When two or more new servers are to be set up, the problem with minmax cost criteria, MinMax, becomes NP-hard. In this work we identify some useful properties about the potential locations for the new servers, from which we derive a novel algorithm for MinMax, and show that it is efficient when the number of new servers is small. When the number of new servers is large, we propose an efficient 3-approximate algorithm. We verify with experiments on real road networks that our solutions are effective and attains significantly better result quality compared to the existing greedy algorithms.

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ALMOST OPTIMAL SOLUTIONS TO k-CLUSTERING PROBLEMS

Cited by 13 publications

References 41 publications

SUMOylation of TEAD1 Modulates the Mechanism of Pathological Cardiac Hypertrophy

SUMOylation of TEAD1 Modulates the Mechanism of Pathological Cardiac Hypertrophy

Improvements on the k-center Problem for Uncertain Data

Finding multiple new optimal locations in a road network

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