Prediction of Neuropeptides from Sequence Information Using Ensemble Classifier and Hybrid Features

Bin, Yannan; Zhang, Wei; Tang, Wending; Dai, Ruyu; Li, Menglu; Zhu, Qizhi; Xia, Junfeng

doi:10.1021/acs.jproteome.0c00276

Cited by 43 publications

(34 citation statements)

References 56 publications

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“…To identify whether BIP susceptibility genes are co-expressed in the human brain, we performed gene co-expression analyses using the RNA-seq expression data of BIP cases ( N = 144) from the PsychENCODE Consortium ( 62 ). Pearson correlation coefficients were then calculated, as described previously ( 77 ), and those genes with correlation coefficients ≥0.8 were retained in the dbBIP.…”

Section: Methodsmentioning

confidence: 99%

dbBIP: a comprehensive bipolar disorder database for genetic research

Yan

et al. 2022

Database

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Bipolar disorder (BIP) is one of the most common hereditary psychiatric disorders worldwide. Elucidating the genetic basis of BIP will play a pivotal role in mechanistic delineation. Genome-wide association studies (GWAS) have successfully reported multiple susceptibility loci conferring BIP risk, thus providing insight into the effects of its underlying pathobiology. However, difficulties remain in the extrication of important and biologically relevant data from genetic discoveries related to psychiatric disorders such as BIP. There is an urgent need for an integrated and comprehensive online database with unified access to genetic and multi-omics data for in-depth data mining. Here, we developed the dbBIP, a database for BIP genetic research based on published data. The dbBIP consists of several modules, i.e.: (i) single nucleotide polymorphism (SNP) module, containing large-scale GWAS genetic summary statistics and functional annotation information relevant to risk variants; (ii) gene module, containing BIP-related candidate risk genes from various sources and (iii) analysis module, providing a simple and user-friendly interface to analyze one’s own data. We also conducted extensive analyses, including functional SNP annotation, integration (including summary-data-based Mendelian randomization and transcriptome-wide association studies), co-expression, gene expression, tissue expression, protein–protein interaction and brain expression quantitative trait loci analyses, thus shedding light on the genetic causes of BIP. Finally, we developed a graphical browser with powerful search tools to facilitate data navigation and access. The dbBIP provides a comprehensive resource for BIP genetic research as well as an integrated analysis platform for researchers and can be accessed online at http://dbbip.xialab.info. Database URL: http://dbbip.xialab.info

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Section: Methodsmentioning

confidence: 99%

dbBIP: a comprehensive bipolar disorder database for genetic research

Yan

et al. 2022

Database

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“…If the count value was greater than 10, the peptides were split into the 10 bins, and if the count value was lower than 10, the peptides were uniformly distributed at random across the bins. Like in Reference [ 11 ], all peptide sequences were one-hot encoded before introduced to the MultiPep. All sequences were zero-padded until they reached a length of 200 one-hot encoded amino acids.…”

Section: Methodsmentioning

confidence: 99%

MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

2021

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Peptide-based therapeutics are here to stay and will prosper in the future. A key step in identifying novel peptide-drugs is the determination of their bioactivities. Recent advances in peptidomics screening approaches hold promise as a strategy for identifying novel drug targets. However, these screenings typically generate an immense number of peptides and tools for ranking these peptides prior to planning functional studies are warranted. Whereas a couple of tools in the literature predict multiple classes, these are constructed using multiple binary classifiers. We here aimed to use an innovative deep learning approach to generate an improved peptide bioactivity classifier with capacity of distinguishing between multiple classes. We present MultiPep: a deep learning multi-label classifier that assigns peptides to zero or more of 20 bioactivity classes. We train and test MultiPep on data from several publically available databases. The same data are used for a hierarchical clustering, whose dendrogram shapes the architecture of MultiPep. We test a new loss function that combines a customized version of Matthews correlation coefficient with binary cross entropy (BCE), and show that this is better than using class-weighted BCE as loss function. Further, we show that MultiPep surpasses state-of-the-art peptide bioactivity classifiers and that it predicts known and novel bioactivities of FDA-approved therapeutic peptides. In conclusion, we present innovative machine learning techniques used to produce a peptide prediction tool to aid peptide-based therapy development and hypothesis generation.

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“…AUC is defined as the area bounded by the receiver operating characteristic (ROC) curve and coordinate axis. The closer the AUC is to 1.0, the higher the authenticity of the detection method ( Bin et al, 2020 ; McDermott et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion

Cao

Wang

Bin

et al. 2021

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An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, we developed a new and efficient algorithm that predicts ACPs by fusing multi-view features based on dual-channel deep neural network ensemble model. In the model, one channel used the convolutional neural network CNN to automatically extract the potential spatial features of a sequence. Another channel was used to process and extract more effective features from handcrafted features. Additionally, an effective feature fusion method was explored for the mutual fusion of different features. Finally, we adopted the neural network to predict ACPs based on the fusion features. The performance comparisons across the single and fusion features showed that the fusion of multi-view features could effectively improve the model’s predictive ability. Among these, the fusion of the features extracted by the CNN and composition of k-spaced amino acid group pairs achieved the best performance. To further validate the performance of our model, we compared it with other existing methods using two independent test sets. The results showed that our model’s area under curve was 0.90, which was higher than that of the other existing methods on the first test set and higher than most of the other existing methods on the second test set. The source code and datasets are available at https://github.com/wame-ng/DLFF-ACP.

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Prediction of Neuropeptides from Sequence Information Using Ensemble Classifier and Hybrid Features

Cited by 43 publications

References 56 publications

dbBIP: a comprehensive bipolar disorder database for genetic research

dbBIP: a comprehensive bipolar disorder database for genetic research

MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion

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