Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Genomics02:02

Genomics

36.8K
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...
36.8K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

138
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...
138
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

4.0K
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...
4.0K
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...
6.1K
Biostatistics: Overview01:20

Biostatistics: Overview

323
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
323
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

59
Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
59

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
Same author

Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression.

Statistics in biosciences·2026
Same author

Bacterial collagenase harnesses collagen geometry for processive cleavage.

Nature communications·2026
Same author

Targeting the ferritinophagy-lysosome axis as a therapeutic vulnerability in gastroenteropancreatic neuroendocrine tumors.

Cell reports. Medicine·2026
Same author

Splicing of HPV16 E6 promotes aggressive invasion in oropharyngeal cancer via endocytosis of E-cadherin.

bioRxiv : the preprint server for biology·2025
Same author

T<sub>H</sub>1 effector CD4 T cells rely on IFN-γ production to induce alopecia areata.

Science advances·2025
Same journal

Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

The Evolving Cyberinfrastructure at the National Institutes of Health to Support Data and AI in Biomedical Research.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Applications of AI & ML in Biomanufacturing of Cell and Gene Therapies.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

AI for Health: Leveraging Artificial Intelligence to Revolutionize Healthcare.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Workshop Introduction: Advances of AI Methods in Single Cell Spatial Omics.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
See all related articles

Related Experiment Video

Updated: Aug 16, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.4K

BaySyn: Bayesian Evidence Synthesis for Multi-system Multiomic Integration.

Rupam Bhattacharyya1, Nicholas Henderson, Veerabhadran Baladandayuthapani

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA, rupamb@umich.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 21, 2022
PubMed
Summary
This summary is machine-generated.

Bayesian evidence synthesis integrates multiomic data from diverse cancer models to identify driver genes and drug targets. This framework enhances the discovery of therapeutic associations and cancer mechanisms.

More Related Videos

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

489

Related Experiment Videos

Last Updated: Aug 16, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.4K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

489

Area of Science:

  • Computational Biology
  • Genomics
  • Cancer Research

Background:

  • Discovering cancer drivers and drug targets is limited by sparse drug response data in patient multiomic databases and low statistical power in model system databases.
  • Integrating evidence across different cancer model systems and multiomic data is crucial for deconvolving cancer mechanisms and identifying therapeutic associations.

Purpose of the Study:

  • To propose and validate BaySyn, a hierarchical Bayesian evidence synthesis framework for multi-system multiomic integration in cancer research.
  • To improve the detection of functional driver genes and their association with drug sensitivity profiles by integrating diverse data sources.

Main Methods:

  • Developed BaySyn, a hierarchical Bayesian framework for synthesizing multi-system and multiomic data.
  • Utilized additive Gaussian process models to detect driver genes associated with upstream regulators.
  • Calibrated Bayesian variable selection models for drug outcome prediction using synthesized evidence.
  • Applied the framework to Cancer Cell Line Encyclopedia and The Cancer Genome Atlas pan-gynecological cancer datasets.

Main Results:

  • Mechanistic models identified functional genes, including PTPN6 and ERBB2 within the KEGG adherens junction gene set, across gynecological cancers.
  • The BaySyn outcome model achieved a higher number of discoveries in drug response prediction compared to uncalibrated models.
  • Successfully detected known lineage-specific biomarker associations, such as BCL11A in breast and FGFRL1 in ovarian cancers.

Conclusions:

  • BaySyn provides an efficient framework for multi-system multiomic integration, enhancing the discovery of cancer drivers and therapeutic targets.
  • The framework demonstrates improved statistical power for identifying gene-drug associations and understanding cancer biology.
  • All results and code are available via an interactive R Shiny dashboard for broader accessibility.