ArticleBMC bioinformatics2017
Protein remote homology detection based on bidirectional long short-term memory.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 70 citations in OpenAlex.
- Exploring protein natural diversity in environmental microbiomes with DeepMetagenome.Cell reports methods · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- pLM-BLAST: distant homology detection based on direct comparison of sequence representations from protein language models.Bioinformatics (Oxford, England) · 2023Article
- Principles and challenges of modeling temporal and spatial omics data.Nature methods · 2023Review
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
- Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence.FEMS microbiology reviews · 2023Review
- PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization.PLoS computational biology · 2022Article
- Deep Neural Network Framework Based on Word Embedding for Protein Glutarylation Sites Prediction.Life (Basel, Switzerland) · 2022Article
- Evolutionary analyses of genes in Echinodermata offer insights towards the origin of metazoan phyla.Genomics · 2022Article
- ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.Briefings in bioinformatics · 2022Article
- Computational analysis and prediction of PE_PGRS proteins using machine learning.Computational and structural biotechnology journal · 2022Article
- Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure.Biophysical journal · 2021Article
- PARROT is a flexible recurrent neural network framework for analysis of large protein datasets.eLife · 2021Article
- Remote homology clustering identifies lowly conserved families of effector proteins in plant-pathogenic fungi.Microbial genomics · 2021Article
- DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites.Briefings in bioinformatics · 2021Article
- Improving protein domain classification for third-generation sequencing reads using deep learning.BMC genomics · 2021Article
- A Deep Learning Approach for Predicting Antigenic Variation of Influenza A H3N2.Computational and mathematical methods in medicine · 2021Article
- UDSMProt: universal deep sequence models for protein classification.Bioinformatics (Oxford, England) · 2020Article
- Article
- Gene2vec: gene subsequence embedding for prediction of mammalianRNA (New York, N.Y.) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProtein remote homology detection plays a vital role in studies of protein structures and functions. Almost all of the traditional machine leaning methods require fixed length features to represent the protein sequences. However, it is never an easy task to extract the discriminative features with limited knowledge of proteins. On the other hand, deep learning technique has demonstrated its advantage in automatically learning representations. It is worthwhile to explore the applications of deep learning techniques to the protein remote homology detection.
resultsIn this study, we employ the Bidirectional Long Short-Term Memory (BLSTM) to learn effective features from pseudo proteins, also propose a predictor called ProDec-BLSTM: it includes input layer, bidirectional LSTM, time distributed dense layer and output layer. This neural network can automatically extract the discriminative features by using bidirectional LSTM and the time distributed dense layer.
conclusionExperimental results on a widely-used benchmark dataset show that ProDec-BLSTM outperforms other related methods in terms of both the mean ROC and mean ROC50 scores. This promising result shows that ProDec-BLSTM is a useful tool for protein remote homology detection. Furthermore, the hidden patterns learnt by ProDec-BLSTM can be interpreted and visualized, and therefore, additional useful information can be obtained.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.