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Phylogenetic Trees03:21

Phylogenetic Trees

46.4K
Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
46.4K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.1K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Phylogeny01:23

Phylogeny

46.9K
Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.
46.9K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.4K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.4K
Survival Tree01:19

Survival Tree

159
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
159
The Tree of Life - Bacteria, Archaea, Eukaryotes02:40

The Tree of Life - Bacteria, Archaea, Eukaryotes

33.7K
The “tree of life” describes the evolution of life and the evolutionary relationships between organisms. The root of the tree is the common ancestor to all life on Earth. All other species radiate from this point, much like the branches of a tree. The numerous tips of these branches on the tree of life represent every living, or extant, species. Extinct species, which are species that no longer exist, can be found towards the center of the tree. Currently, these organisms, both...
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Updated: Sep 9, 2025

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

35.5K

神経ネットワークとアンサンブル学習を用いた系統樹のパラメータ推定

Tianjian Qin1, Koen J van Benthem1, Luis Valente1,2

  • 1Groningen Institute for Evolutionary Life Sciences, University of Groningen, Nijenborgh 7, Groningen, 9747 AG, The Netherlands.

Systematic biology
|September 3, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,多くのシナリオで最大確率推定 (MLE) に対して,より速く,より偏った代替案を提供して,系統樹からの種多様化率を推定するための集合ニューラルネットワークを導入します.

キーワード:
グラフニューラルネットワーク機械学習再発性ニューラルネットワークリグレーション

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing
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Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

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関連する実験動画

Last Updated: Sep 9, 2025

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing
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Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

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科学分野:

  • 進化生物学
  • コンピュータ生物学
  • 系統遺伝学

背景:

  • 種の多様化は 種化と絶滅によって引き起こされます
  • これらの割合を図形から推定することは極めて重要ですが,困難です.
  • 現在の最大確率推定 (MLE) 方法は,複雑なモデルと小さな系統に制限があります.

研究 の 目的:

  • 系統樹から多様化パラメータを推定するための新しい集合ニューラルネットワークアプローチを開発し,評価する.
  • MLEと既存のディープラーニングのアプローチとの間にニューラルネットワークのパフォーマンスを比較する.
  • 神経ネットワークの方法の強さを評価する.

主な方法:

  • 集積ニューラルネットワークは,密集ニューラルネットワーク,グラフニューラルネットワーク,および再帰ニューラルネットワークを組み合わせて開発されました.
  • ネットワークはグラフ表現,分岐時間,および系統の概要統計から学習します.
  • 性能は,シミュレートされた系統遺伝データを用いて,MLEとコンボリューションネットワークアプローチで評価された.

主要な成果:

  • アンサンブルニューラルネットワークは,MLEよりも速い見積もりを提供し,恒定率および多様性依存モデルでは木のサイズに敏感ではありません.
  • 既存のコンボリュショナル・ネットワークの方法と比べると性能が良い.
  • ニューラルネットワークアプローチとMLEは,長時間の出生死亡プロセス下でパラメータの回復に苦労します.

結論:

  • アンサンブルニューラルネットワークは,特にMLEが実現不可能な場合,多様化パラメータを推定するためのMLEの有望で,より速く,より偏った代替手段です.
  • 系統発生の大きさと進化信号の強さは,正確なパラメータ推定のための重要な制限です.
  • この方法は,十分な遺伝信号がある場合に良好な性能を示す.