5 research outputs found
DeePromoter: Robust Promoter Predictor Using Deep Learning
The promoter region is located near the transcription start sites and regulates transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region recognition is an important area of interest in the field of bioinformatics. Numerous tools for promoter prediction were proposed. However, the reliability of these tools still needs to be improved. In this work, we propose a robust deep learning model, called DeePromoter, to analyze the characteristics of the short eukaryotic promoter sequences, and accurately recognize the human and mouse promoter sequences. DeePromoter combines a convolutional neural network (CNN) and a long short-term memory (LSTM). Additionally, instead of using non-promoter regions of the genome as a negative set, we derive a more challenging negative set from the promoter sequences. The proposed negative set reconstruction method improves the discrimination ability and significantly reduces the number of false positive predictions. Consequently, DeePromoter outperforms the previously proposed promoter prediction tools. In addition, a web-server for promoter prediction is developed based on the proposed methods and made available at https://home.jbnu.ac.kr/NSCL/deepromoter.htm
Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing
In recent decades, the development of new drugs has become increasingly
expensive and inefficient, and the molecular mechanisms of most pharmaceuticals
remain poorly understood. In response, computational systems and network
medicine tools have emerged to identify potential drug repurposing candidates.
However, these tools often require complex installation and lack intuitive
visual network mining capabilities. To tackle these challenges, we introduce
Drugst.One, a platform that assists specialized computational medicine tools in
becoming user-friendly, web-based utilities for drug repurposing. With just
three lines of code, Drugst.One turns any systems biology software into an
interactive web tool for modeling and analyzing complex protein-drug-disease
networks. Demonstrating its broad adaptability, Drugst.One has been
successfully integrated with 21 computational systems medicine tools. Available
at https://drugst.one, Drugst.One has significant potential for streamlining
the drug discovery process, allowing researchers to focus on essential aspects
of pharmaceutical treatment research.Comment: 45 pages, 6 figures, 7 table
Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing
Information developed to understand the molecular mechanisms of SARS-CoV-2 infection for predicting drug repurposing candidates is time-consuming to integrate and explore. Here, the authors develop an interactive online platform for virus-host interactome exploration and drug (target) identification