Research Code
PRIME
A probabilistic imputation method for reducing dropout effects in single-cell RNA sequencing data.
PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing
Bioinformatics, 2020
Research software and computational tools developed by MIDAS Lab.
Selected projects
Selected software and computational resources developed by MIDAS Lab.
Research Code
A probabilistic imputation method for reducing dropout effects in single-cell RNA sequencing data.
PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing
Bioinformatics, 2020
Benchmark / Resource
A benchmark and network synthesis resource for generating realistic protein-protein interaction network families and evaluating network alignment algorithms.
NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families
PLOS ONE, 2020
Research Code
A network alignment method that estimates protein correspondence using steady-state network flow over integrated protein-protein interaction networks.
Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model
BMC Bioinformatics, 2016
Research outputs
Browse software, source code, web applications, pipelines, and computational resources.
Web Application
A zero-configuration tool that uses computer vision to predict 3′-end trimming positions from FastQC quality reports for 16S rRNA sequencing.
PixelCut: A Unified Solution for Zero-Configuration 16S rRNA Trimming via Computer Vision
Current Issues in Molecular Biology, 2025
Web Application
A deep-learning method and web application for accurate RNA structural alignment using a residual encoder-decoder network.
REDalign: accurate RNA structural alignment using residual encoder-decoder network
BMC Bioinformatics, 2024
Research Code
A graph-autoencoder-based method for single-cell clustering through ensemble cell-to-cell similarity learning.
GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning
PLOS ONE, 2023
Web Application
A deep-learning web application based on long short-term memory networks for efficient piRNA detection in large-scale genome databases.
LSTM4piRNA: Efficient piRNA Detection in Large-Scale Genome Databases Using a Deep Learning-Based LSTM Network
International Journal of Molecular Sciences, 2023
Research Code
A single-cell clustering method that constructs an ensemble cell-to-cell similarity network for robust cluster identification.
Accurate Single-Cell Clustering through Ensemble Similarity Learning
Genes, 2021
Research Code
A probabilistic imputation method for reducing dropout effects in single-cell RNA sequencing data.
PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing
Bioinformatics, 2020
Benchmark / Resource
A benchmark and network synthesis resource for generating realistic protein-protein interaction network families and evaluating network alignment algorithms.
NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families
PLOS ONE, 2020
Research Code
An ensemble feature-selection and similarity-learning method for robust clustering of single-cell RNA sequencing data.
Effective single-cell clustering through ensemble feature selection and similarity measurements
Computational Biology and Chemistry, 2020
Research Code
A scalable algorithm for predicting conserved protein complexes across multiple protein-protein interaction networks.
ClusterM: a scalable algorithm for computational prediction of conserved protein complexes across multiple protein interaction networks
BMC Genomics, 2020
Research Code
A network-based algorithm for fast and accurate structural alignment of RNA sequences using sequence and predicted secondary-structure information.
TOPAS: network-based structural alignment of RNA sequences
Bioinformatics, 2019
Research Code
A network alignment method that estimates protein correspondence using steady-state network flow over integrated protein-protein interaction networks.
Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model
BMC Bioinformatics, 2016