找回密碼
 To register

QQ登錄

只需一步,快速開(kāi)始

掃一掃,訪(fǎng)問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Hidden Markov Models; Methods and Protocol David R. Westhead,M. S. Vijayabaskar Book 2017 Springer Science+Business Media LLC 2017 protein.

[復(fù)制鏈接]
樓主: 不讓做的事
21#
發(fā)表于 2025-3-25 04:58:46 | 只看該作者
22#
發(fā)表于 2025-3-25 10:02:36 | 只看該作者
23#
發(fā)表于 2025-3-25 13:38:45 | 只看該作者
Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster VisualThe Sequence Data Density Display (SDDD) and Sequence Likelihood Projection (SLP) visualizations represent the input symbolical sequences in a lower-dimensional space in such a way that the clusters and relations of data elements are depicted graphically. Both operate in combination/synergy with the
24#
發(fā)表于 2025-3-25 18:08:00 | 只看該作者
Analyzing Single Molecule FRET Trajectories Using HMM,and such dynamics in recent biology. The single-molecule F?rster resonance energy transfer (smFRET) measurement is one of few methods that enable us to observe structural changes of biomolecules in realtime. Time series data of smFRET, however, typically contains significant fluctuation, making anal
25#
發(fā)表于 2025-3-25 23:31:01 | 只看該作者
Modelling ChIP-seq Data Using HMMs, chapter, we show how hidden Markov models can be used for the analysis of data generated by ChIP-seq experiments. We show how a hidden Markov model can naturally account for spatial dependencies in the ChIP-seq data, how it can be used in the presence of data from multiple ChIP-seq experiments unde
26#
發(fā)表于 2025-3-26 01:42:15 | 只看該作者
Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence,s (HMMs) have wide applications in pattern recognition as well as Bioinformatics such as transcription factor binding sites and cis-regulatory modules detection. An application of HMM is introduced in this chapter with the in-deep developing of NGS. Single nucleotide variants (SNVs) inferred from NG
27#
發(fā)表于 2025-3-26 05:34:58 | 只看該作者
28#
發(fā)表于 2025-3-26 09:14:18 | 只看該作者
Hidden Markov Models in Population Genomics, the genome, sequenced in dozens of individuals, to collections of complete genomes, virtually comprising all available loci. Initially sequenced in a few individuals, such genomic data sets are now reaching and even exceeding the size of traditional data sets in the number of haplotypes sequenced.
29#
發(fā)表于 2025-3-26 15:11:11 | 只看該作者
30#
發(fā)表于 2025-3-26 17:33:05 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛(ài)論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-10 03:37
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
台前县| 静安区| 鄂州市| 康平县| 正镶白旗| 乐清市| 隆德县| 道真| 长海县| 三亚市| 修武县| 拜泉县| 丽江市| 剑川县| 安庆市| 大足县| 威海市| 大埔区| 齐河县| 永清县| 明光市| 沙坪坝区| 昆山市| 韶山市| 洪洞县| 高密市| 会理县| 浦江县| 汨罗市| 高密市| 兰溪市| 始兴县| 南宫市| 崇文区| 沁源县| 巴林右旗| 南康市| 冷水江市| 邵阳县| 光泽县| 海城市|