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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Synthetic Biology02:55

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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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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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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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Related Experiment Video

Updated: Apr 17, 2026

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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Explainable artificial intelligence for multi-omics data.

Sudipto Bhattacharjee1

  • 1Department of Computer Science and Engineering, University of Calcutta, Kolkata, India; Artificial Intelligence for Operations Research (AI4OR) group, Department of Materials and Production, Aalborg University, Aalborg, Denmark.

Progress in Molecular Biology and Translational Science
|April 15, 2026
PubMed
Summary
This summary is machine-generated.

Multi-omics data combined with machine learning offers powerful biomedical predictions. eXplainable Artificial Intelligence (XAI) is essential for understanding these complex model outcomes, enhancing trust and transparency in multi-omics research.

Keywords:
Deep learningExplainable artificial intelligenceInterpretabilityMachine learningMulti-omics

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Artificial Intelligence

Background:

  • Multi-omics data integration is advancing biomedical predictions using machine learning (ML).
  • High-dimensional omics data presents challenges in model interpretability.
  • Next-generation sequencing and computing technologies enable complex data analysis.

Purpose of the Study:

  • To discuss eXplainable Artificial Intelligence (XAI) algorithms and models for multi-omics biomedical predictions.
  • To highlight the importance of interpretability in ML models for healthcare.
  • To demonstrate how XAI enhances transparency and trustworthiness in multi-omics research.

Main Methods:

  • Review of XAI algorithms applicable to multi-omics data.
  • Discussion of XAI models for enhancing ML interpretability.
  • Integration of multi-omics data for comprehensive biomedical insights.

Main Results:

  • XAI approaches can effectively address the lack of interpretability in ML models.
  • Multi-omics data provides a holistic view of biological processes.
  • XAI models improve the transparency and trustworthiness of biomedical predictions.

Conclusions:

  • Multi-omics XAI models are vital for accurate and interpretable biomedical predictions.
  • XAI is crucial for understanding complex patterns in multi-omics data.
  • Integrating multi-omics and XAI fosters trust and clinical utility in AI-driven healthcare.