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

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

9.0K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
9.0K
Cancer Survival Analysis01:21

Cancer Survival Analysis

478
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
478

You might also read

Related Articles

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

Sort by
Same author

Massive scabies outbreak in Rohingya refugee camps, Cox's Bazar: Epidemiology and impact of a mass drug administration (MDA) campaign - A retrospective study.

PLoS neglected tropical diseases·2026
Same author

Neutrophil-Membrane Biomimetic Hollow Mesoporous Silica Nanoparticles for Targeted Delivery of Imperatorin to Alleviate Cerebral Ischemia-Reperfusion Injury via Nrf2/ARE/Keap1 Pathway.

International journal of nanomedicine·2026
Same author

Seasonal Variation of Microplastic Contamination in Edible Bivalves (Green Mussels and Clams) from the Bay of Bengal Coast, Bangladesh.

Environmental monitoring and assessment·2026
Same author

MADSurv: An Uncertainty-Aware Framework for Multimodal Cancer Survival Analysis.

ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine·2026
Same author

Drought-induced shifts in soil microbial communities and carbon dynamics in agricultural and forest soils of Bangladesh.

3 Biotech·2026
Same author

Burkholderia cepacia complex (Bcc) in goats: First report in Bangladesh.

PloS one·2026
Same journal

RETRACTED: Kim et al. The Angiogenesis Inhibitor ALS-L1023 from Lemon-Balm Leaves Attenuates High-Fat Diet-Induced Nonalcoholic Fatty Liver Disease Through Regulating the Visceral Adipose-Tissue Function. <i>Int. J. Mol. Sci.</i> 2017, <i>18</i>, 846.

International journal of molecular sciences·2026
Same journal

Correction: Mahmud et al. Thymoquinone Attenuates NF-κβ Signalling Activation in Retinal Pigment Epithelium Cells Under AMD-Mimicking Conditions. <i>Int. J. Mol. Sci.</i> 2025, <i>26</i>, 11473.

International journal of molecular sciences·2026
Same journal

Correction: Borovikov et al. The Twisting and Untwisting of Actin and Tropomyosin Filaments Are Involved in the Molecular Mechanisms of Muscle Contraction, and Their Disruption Can Result in Muscle Disorders. <i>Int. J. Mol. Sci</i>. 2025, <i>26</i>, 6705.

International journal of molecular sciences·2026
Same journal

Correction: Molagoda et al. Flavonoid Glycosides from <i>Ziziphus jujuba</i> var. <i>inermis</i> (Bunge) Rehder Seeds Inhibit α-Melanocyte-Stimulating Hormone-Mediated Melanogenesis. <i>Int. J. Mol. Sci.</i> 2021, <i>22</i>, 7701.

International journal of molecular sciences·2026
Same journal

Correction: Guo et al. Integrated Transcriptomic and Metabolomic Analysis Reveals the Molecular Regulatory Mechanism of Flavonoid Biosynthesis in Maize Roots Under Lead Stress. <i>Int. J. Mol. Sci.</i> 2024, <i>25</i>, 6050.

International journal of molecular sciences·2026
Same journal

Correction: Chang et al. Improvement of Carbon Tetrachloride-Induced Acute Hepatic Failure by Transplantation of Induced Pluripotent Stem Cells Without Reprogramming Factor c-Myc. <i>Int. J. Mol. Sci.</i> 2012, <i>13</i>, 3598-3617.

International journal of molecular sciences·2026
See all related articles

Related Experiment Video

Updated: Oct 13, 2025

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

1.3K

Multi-Run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers.

Abdullah Al Mamun1, Raihanul Bari Tanvir1, Masrur Sobhan1

  • 1Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA.

International Journal of Molecular Sciences
|November 13, 2021
PubMed
Summary
This summary is machine-generated.

A novel multi-run concrete autoencoder (mrCAE) identifies 128 key long non-coding RNAs (lncRNAs) for cancer origin detection. 76 of these lncRNAs show prognostic value, aiding precision medicine and cancer therapy development.

Keywords:
autoencoderconcrete autoencoderdeep learningfeature selectionlncRNAmrCAE

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

252
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.1K

Related Experiment Videos

Last Updated: Oct 13, 2025

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

1.3K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

252
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.1K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Long non-coding RNAs (lncRNAs) are crucial in regulating gene expression and are implicated in cancer development.
  • Identifying prognostic lncRNAs can significantly advance cancer diagnosis and therapeutic strategies.

Purpose of the Study:

  • To develop a robust method for identifying key long non-coding RNAs (lncRNAs) capable of distinguishing between 12 different cancer types.
  • To assess the prognostic value of identified lncRNAs in patient stratification.

Main Methods:

  • Utilized a multi-run concrete autoencoder (mrCAE), a deep learning algorithm, for unsupervised feature selection on genome-wide lncRNA expression data from The Cancer Genome Atlas (TCGA).
  • Analyzed 4768 samples across 12 cancer types to identify stable and informative lncRNAs.
  • Compared mrCAE performance against single-run CAE, standard autoencoder (AE), and other feature selection methods.

Main Results:

  • mrCAE demonstrated superior performance in feature selection compared to existing methods.
  • Identified 128 top-ranking lncRNAs with 95% accuracy in distinguishing the origin of 12 different cancers.
  • Survival analysis confirmed that 76 of these lncRNAs possess prognostic capabilities, differentiating patient risk groups.

Conclusions:

  • The proposed mrCAE effectively identifies actual, reproducible features, outperforming AE in selecting latent features, making it valuable for precision medicine.
  • The discovered set of prognostic lncRNAs holds potential for further investigation in developing targeted cancer therapies.