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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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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Human Genetics01:28

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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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How data science and AI-based technologies impact genomics.

Jing Lin1, Kee Yuan Ngiam2

  • 1NUHS Corporate Office, National University Health System, Singapore.

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|February 1, 2023
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Summary
This summary is machine-generated.

Artificial intelligence (AI) and data science are revolutionizing genomic studies by analyzing vast amounts of genetic and electronic health record data. These technologies aid in risk prediction, phenotyping, and CRISPR guide RNA design, advancing precision medicine.

Keywords:
Artificial intelligencedeep learninggenome-wide association studypharmacogenomicsphenome-wide association study

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Area of Science:

  • Genomics
  • Bioinformatics
  • Artificial Intelligence

Background:

  • High-throughput sequencing generates massive genomic datasets, necessitating advanced analytical methods.
  • Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) identify genotype-phenotype links, impacting pharmacogenomics and clinical decision support.
  • Integrating large-scale genomic and electronic health record (EHR) data presents significant data science challenges.

Purpose of the Study:

  • To review the application of data science and AI in genomic research.
  • To highlight AI's role in overcoming data integration challenges in GWAS/PheWAS.
  • To discuss emerging AI techniques benefiting genomic studies.

Main Methods:

  • Leveraging machine learning and deep learning for GWAS/PheWAS data analysis.
  • Applying AI for risk prediction and causal single-nucleotide polymorphism identification.
  • Utilizing AI in EHR-based phenotyping and CRISPR guide RNA design.

Main Results:

  • AI and data science effectively address challenges posed by large genomic and EHR datasets.
  • Successful applications include risk prediction, causal SNP identification, phenotyping, and CRISPR guide RNA design.
  • Emerging AI technologies like transfer learning and multi-view learning show promise for future genomic studies.

Conclusions:

  • Data science and AI are crucial for unlocking the potential of big data in genomics.
  • AI facilitates advancements in precision medicine through improved genetic association studies and clinical applications.
  • Continued development and application of AI will further accelerate discoveries in genomic research.