iDNA-ABT – Detecting DNA Methylation with Adaptive Features and Transductive Information Maximization

iDNA-ABT

:: DESCRIPTION

iDNA-ABT is an advanced deep learning model that utilizes adaptive embedding based on bidirectional transformers for language understanding (BERT) together with a novel transductive information maximization (TIM) loss.

::DEVELOPER

iDNA-ABT team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python
:: DOWNLOAD

iDNA-ABT

:: MORE INFORMATION

Citation

Yu Y, He W, Jin J, Cui L, Zeng R, Wei L.
iDNA-ABT : advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization.
Bioinformatics. 2021 Oct 2:btab677. doi: 10.1093/bioinformatics/btab677. Epub ahead of print. PMID: 34601568.

TimeTP 1.0 – Influence Maximization in Time bounded network Identifies Transcription Factors Regulating Perturbed Pathways

TimeTP 1.0

:: DESCRIPTION

TimeTP is a novel time-series analysis method for determining transcription factors (TFs) regulating pathway perturbation, which narrows the focus to perturbed sub-pathways and utilizes the gene regulatory network and protein-protein interaction network to locate TFs triggering the perturbation.

::DEVELOPER

Bio & Health Informatics Lab , Seoul National University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

TimeTP

:: MORE INFORMATION

Citation

Jo K, Jung I, Moon JH, Kim S.
Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways.
Bioinformatics. 2016 Jun 15;32(12):i128-i136. doi: 10.1093/bioinformatics/btw275. PMID: 27307609; PMCID: PMC4908359.