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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
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...
5.7K
RNA-seq03:21

RNA-seq

9.9K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.9K
Gene Families01:57

Gene Families

8.8K
Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
8.8K
Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

6.6K
Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
6.6K
Molecular Models02:00

Molecular Models

38.2K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
38.2K
Globular and Fibrous Proteins02:21

Globular and Fibrous Proteins

43.6K
Many proteins can be classified into two distinct subtypes - globular or fibrous. These two types differ in their shapes and solubilities.
Globular proteins are also known as spheroproteins and typically are approximately round in shape. They contain a mix of amino acid types and contain differing sequences in their primary structures. Globular proteins have many different functions, such as enzymes, cellular messengers, and molecular transporters. These roles often require the proteins to be...
43.6K

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相关实验视频

Updated: Jun 25, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

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特性工程嵌入用于分类分子数据的嵌入.

Claudio Jardim1, Alta de Waal1, Inger Fabris-Rotelli1

  • 1University of Pretoria, Pretoria, South Africa.

Computational biology and chemistry
|May 25, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用化学文本数据的自然语言处理 (NLP) 进行分子分类的更快,更高效的计算方法. 这种方法避免了漫长的深度学习培训,为药物发现和生物信息学提供了强大的替代方案.

关键词:
隐藏的迪里克莱特分配房地产预测的预测.

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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

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相关实验视频

Last Updated: Jun 25, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

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科学领域:

  • 计算化学是一种计算化学.
  • 生物信息学是一种生物信息学.
  • 机器学习是机器学习.

背景情况:

  • 分子分类对于药物发现至关重要.
  • 目前使用结构数据的深度学习方法是计算密集的,训练很慢.
  • 分子数据存在于结构和序列/文本信息.

研究的目的:

  • 开发一种计算效率高,可重复的分子分类方法.
  • 探索自然语言处理 (NLP) 技术用于特征工程分子文本数据的使用.
  • 为机器学习模型创建快速,化学文本数据依赖的分子嵌入.

主要方法:

  • 使用NLP技术的特征工程:计数矢量化,术语频率逆向文档频率 (TF-IDF),word2vec和隐藏的迪里克莱特分配 (LDA).
  • 这些技术应用于FASTA序列数据和简化分子输入线输入规范 (SMILES) 数据.
  • 在机器学习分类任务中评估生成的嵌入式的性能.

主要成果:

  • 基于NLP的特征工程创造了强大的和可重复的分子嵌入.
  • 开发的嵌入式很快实现,并且完全依赖于化学文本数据.
  • 这些嵌入式在应用到FASTA和SMILES数据时,在分子分类任务中表现出色.

结论:

  • 自然语言处理技术为分子分类提供了传统深度学习的有效替代方案.
  • 拟议的方法提供了一种快速,计算上便宜,有效的方法,用于从文本数据中生成分子嵌入.
  • 这种方法对加速药物发现和其他需要分子分类的应用具有重大意义.