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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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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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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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Introduction to artificial intelligence in multi-omics analysis.

Arpan Saha Mondal1, Rajat Kumar Pal1, Sudipto Saha2

  • 1Department of Computer Science and Engineering, University of Calcutta, Kolkata, India.

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PubMed
Summary
This summary is machine-generated.

This study explores multi-omics data analysis using machine learning and artificial intelligence. It covers tools, pipelines, and strategies for integrating diverse omics datasets to predict disease phenotypes and identify biomarkers.

Keywords:
AIMulti-omicsdata integrationmachine learning algorithmperformance metrics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Multi-omics integrates genomics, transcriptomics, proteomics, and metabolomics for comprehensive biological system analysis.
  • Understanding complex biological systems requires analyzing diverse molecular data at multiple levels.

Purpose of the Study:

  • To review open-source tools, databases, and pipelines for multi-omics data analysis.
  • To explore machine learning algorithms for feature extraction, disease prediction, and biomarker discovery.
  • To discuss challenges and strategies for integrating multi-omics data with machine learning and AI.

Main Methods:

  • Utilizes various machine learning algorithms (supervised, unsupervised, reinforcement learning).
  • Examines AI-based tools and frameworks for multi-omics data analysis.
  • Focuses on standardized bioinformatics pipelines for omics data integration.

Main Results:

  • Identifies key machine learning approaches for extracting meaningful features from multi-omics data.
  • Highlights strategies for effective multi-omics data integration with machine learning.
  • Showcases AI-driven tools for advanced omics data analysis.

Conclusions:

  • Multi-omics analysis with AI and machine learning offers powerful insights into complex biological systems.
  • Addresses current challenges and outlines future research directions in AI-driven omics data analysis.