Correlation-compressed direct-coupling analysis
文献类型：期刊论文
| 作者 | Gao, CY; Zhou, HJ ; Aurell, E2,3,4
|
| 刊名 | PHYSICAL REVIEW E
 |
| 出版日期 | 2018 |
| 卷号 | 98期号:3页码:32407 |
| 关键词 | STRUCTURE PREDICTION PROTEIN-STRUCTURE CONTACTS IDENTIFICATION MODEL SEQUENCES FAMILIES HUMANS |
| ISSN号 | 2470-0045 |
| DOI | 10.1103/PhysRevE.98.032407 |
| 英文摘要 | Learning Ising or Potts models from data has become an important topic in statistical physics and computational biology, with applications to predictions of structural contacts in proteins and other areas of biological data analysis. The corresponding inference problems are challenging since the normalization constant (partition function) of the Ising or Potts distribution cannot be computed efficiently on large instances. Different ways to address this issue have resulted in a substantial amount of methodological literature. In this paper we investigate how these methods could be used on much larger data sets than studied previously. We focus on a central aspect, that in practice these inference problems are almost always severely under-sampled, and the operational result is almost always a small set of leading predictions. We therefore explore an approach where the data are prefiltered based on empirical correlations, which can be computed directly even for very large problems. Inference is only used on the much smaller instance in a subsequent step of the analysis. We show that in several relevant model classes such a combined approach gives results of almost the same quality as inference on the whole data set. It can therefore provide a potentially very large computational speedup at the price of only marginal decrease in prediction quality. We also show that the results on whole-genome epistatic couplings that were obtained in a recent computation-intensive study can be retrieved by our approach. The method of this paper hence opens up the possibility to learn parameters describing pairwise dependences among whole genomes in a computationally feasible and expedient manner. |
| 学科主题 | Physics |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/22820]  |
| 专题 | 理论物理研究所_理论物理所1978-2010年知识产出 计算平台成果 |
| 作者单位 | 1.Aalto Univ, Dept Comp Sci, Aalto 00076, Finland 2.Aalto Univ, Dept Appl Phys, Aalto 00076, Finland 3.KTH Royal Inst Technol, Dept Computat Biol, S-10044 Stockholm, Sweden 4.Hunan Normal Univ, Synerget Innovat Ctr Quantum Effects & Applicat, Changsha 410081, Hunan, Peoples R China 5.Chinese Acad Sci, Inst Theoret Phys, Key Lab Theoret Phys, Beijing 100190, Peoples R China 6.Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, CY,Zhou, HJ,Aurell, E. Correlation-compressed direct-coupling analysis[J]. PHYSICAL REVIEW E,2018,98(3):32407. |
| APA | Gao, CY,Zhou, HJ,&Aurell, E.(2018).Correlation-compressed direct-coupling analysis.PHYSICAL REVIEW E,98(3),32407. |
| MLA | Gao, CY,et al."Correlation-compressed direct-coupling analysis".PHYSICAL REVIEW E 98.3(2018):32407. |
入库方式： OAI收割
来源：理论物理研究所
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