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Luo H, Tang L, Zeng M, Yin R, Ding P, Luo L, Li M. BertSNR: an interpretable deep learning framework for single-nucleotide resolution identification of transcription factor binding sites based on DNA language model. Bioinformatics 2024; 40:btae461. [PMID: 39107889 PMCID: PMC11310455 DOI: 10.1093/bioinformatics/btae461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 06/07/2024] [Indexed: 08/10/2024] Open
Abstract
MOTIVATION Transcription factors are pivotal in the regulation of gene expression, and accurate identification of transcription factor binding sites (TFBSs) at high resolution is crucial for understanding the mechanisms underlying gene regulation. The task of identifying TFBSs from DNA sequences is a significant challenge in the field of computational biology today. To address this challenge, a variety of computational approaches have been developed. However, these methods face limitations in their ability to achieve high-resolution identification and often lack interpretability. RESULTS We propose BertSNR, an interpretable deep learning framework for identifying TFBSs at single-nucleotide resolution. BertSNR integrates sequence-level and token-level information by multi-task learning based on pre-trained DNA language models. Benchmarking comparisons show that our BertSNR outperforms the existing state-of-the-art methods in TFBS predictions. Importantly, we enhanced the interpretability of the model through attentional weight visualization and motif analysis, and discovered the subtle relationship between attention weight and motif. Moreover, BertSNR effectively identifies TFBSs in promoter regions, facilitating the study of intricate gene regulation. AVAILABILITY AND IMPLEMENTATION The BertSNR source code can be found at https://github.com/lhy0322/BertSNR.
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Affiliation(s)
- Hanyu Luo
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
- School of Computer Science, University of South China, Hengyang, Hunan 421001, China
| | - Li Tang
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Min Zeng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Rui Yin
- Department of Health Outcome and Biomedical Informatics, University of Florida, Gainesville, FL 32611, United States
| | - Pingjian Ding
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Lingyun Luo
- School of Computer Science, University of South China, Hengyang, Hunan 421001, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
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Li F, Liu S, Li K, Zhang Y, Duan M, Yao Z, Zhu G, Guo Y, Wang Y, Huang L, Zhou F. EpiTEAmDNA: Sequence feature representation via transfer learning and ensemble learning for identifying multiple DNA epigenetic modification types across species. Comput Biol Med 2023; 160:107030. [PMID: 37196456 DOI: 10.1016/j.compbiomed.2023.107030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 04/21/2023] [Accepted: 05/10/2023] [Indexed: 05/19/2023]
Abstract
Methylation is a major DNA epigenetic modification for regulating the biological processes without altering the DNA sequence, and multiple types of DNA methylations have been discovered, including 6mA, 5hmC, and 4mC. Multiple computational approaches were developed to automatically identify the DNA methylation residues using machine learning or deep learning algorithms. The machine learning (ML) based methods are difficult to be transferred to the other predicting tasks of the DNA methylation sites using additional knowledge. Deep learning (DL) may facilitate the transfer learning of knowledge from similar tasks, but they are often ineffective on small datasets. This study proposes an integrated feature representation framework EpiTEAmDNA based on the strategies of transfer learning and ensemble learning, which is evaluated on multiple DNA methylation types across 15 species. EpiTEAmDNA integrates convolutional neural network (CNN) and conventional machine learning methods, and shows improved performances than the existing DL-based methods on small datasets when no additional knowledge is available. The experimental data suggests that the EpiTEAmDNA models may be further improved via transfer learning based on additional knowledge. The evaluation experiments on the independent test datasets also suggest that the proposed EpiTEAmDNA framework outperforms the existing models in most prediction tasks of the 3 DNA methylation types across 15 species. The source code, pre-trained global model, and the EpiTEAmDNA feature representation framework are freely available at http://www.healthinformaticslab.org/supp/.
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Affiliation(s)
- Fei Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China; College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China
| | - Shuai Liu
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China; College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China
| | - Kewei Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China; College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China
| | - Yaqi Zhang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China; College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China
| | - Meiyu Duan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China; College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China.
| | - Zhaomin Yao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Gancheng Zhu
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China; College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China
| | - Yutong Guo
- College of Life Sciences, Jilin University, Changchun, Jilin, 130012, China
| | - Ying Wang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China; College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China
| | - Lan Huang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China; College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China; College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China.
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Agarwal A, Chen L. DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach. Bioinformatics 2023; 39:6887158. [PMID: 36495179 PMCID: PMC9825766 DOI: 10.1093/bioinformatics/btac801] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 11/23/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION Promoter-centered chromatin interactions, which include promoter-enhancer (PE) and promoter-promoter (PP) interactions, are important to decipher gene regulation and disease mechanisms. The development of next-generation sequencing technologies such as promoter capture Hi-C (pcHi-C) leads to the discovery of promoter-centered chromatin interactions. However, pcHi-C experiments are expensive and thus may be unavailable for tissues/cell types of interest. In addition, these experiments may be underpowered due to insufficient sequencing depth or various artifacts, which results in a limited finding of interactions. Most existing computational methods for predicting chromatin interactions are based on in situ Hi-C and can detect chromatin interactions across the entire genome. However, they may not be optimal for predicting promoter-centered chromatin interactions. RESULTS We develop a supervised multi-modal deep learning model, which utilizes a comprehensive set of features such as genomic sequence, epigenetic signal, anchor distance, evolutionary features and DNA structural features to predict tissue/cell type-specific PE and PP interactions. We further extend the deep learning model in a multi-task learning and a transfer learning framework and demonstrate that the proposed approach outperforms state-of-the-art deep learning methods. Moreover, the proposed approach can achieve comparable prediction performance using predefined biologically relevant tissues/cell types compared to using all tissues/cell types in the pretraining especially for predicting PE interactions. The prediction performance can be further improved by using computationally inferred biologically relevant tissues/cell types in the pretraining, which are defined based on the common genes in the proximity of two anchors in the chromatin interactions. AVAILABILITY AND IMPLEMENTATION https://github.com/lichen-lab/DeepPHiC. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Aman Agarwal
- Department of Computer Science, Indiana University, Bloomington, IN 47405, USA
| | - Li Chen
- To whom correspondence should be addressed.
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Jing Zhang F, Zhang SW, Zhang S. Prediction of Transcription Factor Binding Sites With an Attention Augmented Convolutional Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3614-3623. [PMID: 34752400 DOI: 10.1109/tcbb.2021.3126623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Identification of transcription factor binding sites (TFBSs) is essential for revealing the rules of protein-DNA binding. Although some computational methods have been presented to predict TFBSs using epigenomic and sequence features, most of them ignore the common features among cross-cell types. It is still unclear to what extent the common features could help for this task. To this end, we proposed a new method (named Attention-augmented Convolutional Neural Network, or ACNN) to predict TFBSs. ACNN uses attention-augmented convolutional layers to capture global and local contexts in DNA sequences and employs the convolutional layers to capture features of histone modification markers. In addition, ACNN adopts the private and shared convolutional neural network (CNN) modules to learn specific and common features, respectively. To encourage the shared CNN module to learn the common features, adversarial training is applied in ACNN. The results on 253 ChIP-seq datasets show that ACNN outperforms other existing methods. The attention-augmented convolutional layers and adversarial training mechanism in ACNN can effectively improve the prediction performance. Moreover, in the case of limited labeled data, ACNN also performs better than a baseline method. We further visualize the convolution kernels as motifs to explain the interpretability of ACNN.
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Liao J, Wang Q, Wu F, Huang Z. In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets. Molecules 2022; 27:7103. [PMID: 36296697 PMCID: PMC9609013 DOI: 10.3390/molecules27207103] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/12/2022] [Accepted: 08/25/2022] [Indexed: 07/30/2023] Open
Abstract
Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.
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Affiliation(s)
- Jianbo Liao
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory of Computer-Aided Drug Design of Dongguan City, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan 523808, China
| | - Qinyu Wang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory of Computer-Aided Drug Design of Dongguan City, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, China
| | - Fengxu Wu
- Hubei Key Laboratory of Wudang Local Chinese Medicine Research, School of Pharmaceutical Sciences, Hubei University of Medicine, Shiyan 442000, China
| | - Zunnan Huang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory of Computer-Aided Drug Design of Dongguan City, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, China
- Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang 524023, China
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Wang H, Zhao S, Cheng Y, Bi S, Zhu X. MTDeepM6A-2S: A two-stage multi-task deep learning method for predicting RNA N6-methyladenosine sites of Saccharomyces cerevisiae. Front Microbiol 2022; 13:999506. [PMID: 36274691 PMCID: PMC9579691 DOI: 10.3389/fmicb.2022.999506] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/16/2022] [Indexed: 11/13/2022] Open
Abstract
N6-methyladenosine (m6A) is one of the most important RNA modifications, which is involved in many biological activities. Computational methods have been developed to detect m6A sites due to their high efficiency and low costs. As one of the most widely utilized model organisms, many methods have been developed for predicting m6A sites of Saccharomyces cerevisiae. However, the generalization of these methods was hampered by the limited size of the benchmark datasets. On the other hand, over 60,000 low resolution m6A sites and more than 10,000 base resolution m6A sites of Saccharomyces cerevisiae are recorded in RMBase and m6A-Atlas, respectively. The base resolution m6A sites are often obtained from low resolution results by post calibration. In view of these, we proposed a two-stage deep learning method, named MTDeepM6A-2S, to predict RNA m6A sites of Saccharomyces cerevisiae based on RNA sequence information. In the first stage, a multi-task model with convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) deep framework was built to not only detect the low resolution m6A sites but also assign a reasonable probability for the predicted site. In the second stage, a transfer-learning strategy was used to build the model to predict the base resolution m6A sites from those low resolution m6A sites. The effectiveness of our model was validated on both training and independent test sets. The results show that our model outperforms other state-of-the-art models on the independent test set, which indicates that our model holds high potential to become a useful tool for epitranscriptomics analysis.
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Towards a better understanding of TF-DNA binding prediction from genomic features. Comput Biol Med 2022; 149:105993. [DOI: 10.1016/j.compbiomed.2022.105993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/12/2022] [Accepted: 08/14/2022] [Indexed: 11/17/2022]
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Zhang Y, Wang Z, Zeng Y, Zhou J, Zou Q. High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method. Brief Bioinform 2021; 22:6322761. [PMID: 34272562 DOI: 10.1093/bib/bbab273] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/19/2021] [Accepted: 06/25/2021] [Indexed: 11/14/2022] Open
Abstract
Transcription factors (TFs) are essential proteins in regulating the spatiotemporal expression of genes. It is crucial to infer the potential transcription factor binding sites (TFBSs) with high resolution to promote biology and realize precision medicine. Recently, deep learning-based models have shown exemplary performance in the prediction of TFBSs at the base-pair level. However, the previous models fail to integrate nucleotide position information and semantic information without noisy responses. Thus, there is still room for improvement. Moreover, both the inner mechanism and prediction results of these models are challenging to interpret. To this end, the Deep Attentive Encoder-Decoder Neural Network (D-AEDNet) is developed to identify the location of TFs-DNA binding sites in DNA sequences. In particular, our model adopts Skip Architecture to leverage the nucleotide position information in the encoder and removes noisy responses in the information fusion process by Attention Gate. Simultaneously, the Transcription Factor Motif Discovery based on Sliding Window (TF-MoDSW), an approach to discover TFs-DNA binding motifs by utilizing the output of neural networks, is proposed to understand the biological meaning of the predicted result. On ChIP-exo datasets, experimental results show that D-AEDNet has better performance than competing methods. Besides, we authenticate that Attention Gate can improve the interpretability of our model by ways of visualization analysis. Furthermore, we confirm that ability of D-AEDNet to learn TFs-DNA binding motifs outperform the state-of-the-art methods and availability of TF-MoDSW to discover biological sequence motifs in TFs-DNA interaction by conducting experiment on ChIP-seq datasets.
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Affiliation(s)
- Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, 610225, Chengdu, China
| | - Zixuan Wang
- School of Computer Science, Chengdu University of Information Technology, 610225, Chengdu, China
| | - Yuanqi Zeng
- School of Computer Science, Chengdu University of Information Technology, 610225, Chengdu, China
| | - Jiliu Zhou
- School of Computer Science, Chengdu University of Information Technology, 610225, Chengdu, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, 610054, Chengdu, China
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Kong L, Chen Y, Xu F, Xu M, Li Z, Fang J, Zhang L, Pian C. Mining influential genes based on deep learning. BMC Bioinformatics 2021; 22:27. [PMID: 33482718 PMCID: PMC7821411 DOI: 10.1186/s12859-021-03972-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 01/15/2021] [Indexed: 11/17/2022] Open
Abstract
Background Currently, large-scale gene expression profiling has been successfully applied to the discovery of functional connections among diseases, genetic perturbation, and drug action. To address the cost of an ever-expanding gene expression profile, a new, low-cost, high-throughput reduced representation expression profiling method called L1000 was proposed, with which one million profiles were produced. Although a set of ~ 1000 carefully chosen landmark genes that can capture ~ 80% of information from the whole genome has been identified for use in L1000, the robustness of using these landmark genes to infer target genes is not satisfactory. Therefore, more efficient computational methods are still needed to deep mine the influential genes in the genome. Results Here, we propose a computational framework based on deep learning to mine a subset of genes that can cover more genomic information. Specifically, an AutoEncoder framework is first constructed to learn the non-linear relationship between genes, and then DeepLIFT is applied to calculate gene importance scores. Using this data-driven approach, we have re-obtained a landmark gene set. The result shows that our landmark genes can predict target genes more accurately and robustly than that of L1000 based on two metrics [mean absolute error (MAE) and Pearson correlation coefficient (PCC)]. This reveals that the landmark genes detected by our method contain more genomic information. Conclusions We believe that our proposed framework is very suitable for the analysis of biological big data to reveal the mysteries of life. Furthermore, the landmark genes inferred from this study can be used for the explosive amplification of gene expression profiles to facilitate research into functional connections.
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Affiliation(s)
- Lingpeng Kong
- College of Agriculture, Nanjing Agricultural University, Jiangsu, 210095, Nanjing, China
| | - Yuanyuan Chen
- Department of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, 210095, China
| | - Fengjiao Xu
- Department of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, 210095, China
| | - Mingmin Xu
- College of Agriculture, Nanjing Agricultural University, Jiangsu, 210095, Nanjing, China
| | - Zutan Li
- College of Agriculture, Nanjing Agricultural University, Jiangsu, 210095, Nanjing, China
| | - Jingya Fang
- College of Agriculture, Nanjing Agricultural University, Jiangsu, 210095, Nanjing, China
| | - Liangyun Zhang
- Department of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, 210095, China.
| | - Cong Pian
- Department of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, 210095, China.
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iPTT(2 L)-CNN: A Two-Layer Predictor for Identifying Promoters and Their Types in Plant Genomes by Convolutional Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6636350. [PMID: 33488763 PMCID: PMC7803414 DOI: 10.1155/2021/6636350] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 12/13/2020] [Accepted: 12/16/2020] [Indexed: 11/18/2022]
Abstract
A promoter is a short DNA sequence near to the start codon, responsible for initiating transcription of a specific gene in genome. The accurate recognition of promoters has great significance for a better understanding of the transcriptional regulation. Because of their importance in the process of biological transcriptional regulation, there is an urgent need to develop in silico tools to identify promoters and their types timely and accurately. A number of prediction methods had been developed in this regard; however, almost all of them were merely used for identifying promoters and their strength or sigma types. Owing to that TATA box region in TATA promoter that influences posttranscriptional processes, in the current study, we developed a two-layer predictor called iPTT(2L)-CNN by using the convolutional neural network (CNN) for identifying TATA and TATA-less promoters. The first layer can be used to identify a given DNA sequence as a promoter or nonpromoter. The second layer is used to identify whether the recognized promoter is TATA promoter or not. The 5-fold crossvalidation and independent testing results demonstrate that the constructed predictor is promising for identifying promoter and classifying TATA and TATA-less promoter. Furthermore, to make it easier for most experimental scientists get the results they need, a user-friendly web server has been established at http://www.jci-bioinfo.cn/iPPT(2L)-CNN.
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Del Sol A, Jung S. The Importance of Computational Modeling in Stem Cell Research. Trends Biotechnol 2020; 39:126-136. [PMID: 32800604 DOI: 10.1016/j.tibtech.2020.07.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 07/13/2020] [Accepted: 07/15/2020] [Indexed: 12/30/2022]
Abstract
The generation of large amounts of omics data is increasingly enabling not only the processing and analysis of large data sets but also the development of computational models in the field of stem cell research. Although computational models have been proposed in recent decades, we believe that the stem cell community is not fully aware of the potentiality of computational modeling in guiding their experimental research. In this regard, we discuss how single-cell technologies provide the right framework for computational modeling at different scales of biological organization in order to address challenges in the stem cell field and to guide experimentalists in the design of new strategies for stem cell therapies and treatment of congenital disorders.
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Affiliation(s)
- Antonio Del Sol
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, Esch-sur-Alzette, L-4367 Belvaux, Luxembourg; CIC bioGUNE-BRTA (Basque Research and Technology Alliance), Bizkaia Technology Park, 801 Building, 48160 Derio, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao 48013, Spain.
| | - Sascha Jung
- CIC bioGUNE-BRTA (Basque Research and Technology Alliance), Bizkaia Technology Park, 801 Building, 48160 Derio, Spain
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Xie L, He S, Zhang Z, Lin K, Bo X, Yang S, Feng B, Wan K, Yang K, Yang J, Ding Y. Domain-adversarial multi-task framework for novel therapeutic property prediction of compounds. Bioinformatics 2020; 36:2848-2855. [PMID: 31999334 DOI: 10.1093/bioinformatics/btaa063] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 12/23/2019] [Accepted: 01/23/2020] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION With the rapid development of high-throughput technologies, parallel acquisition of large-scale drug-informatics data provides significant opportunities to improve pharmaceutical research and development. One important application is the purpose prediction of small-molecule compounds with the objective of specifying the therapeutic properties of extensive purpose-unknown compounds and repurposing the novel therapeutic properties of FDA-approved drugs. Such a problem is extremely challenging because compound attributes include heterogeneous data with various feature patterns, such as drug fingerprints, drug physicochemical properties and drug perturbation gene expressions. Moreover, there is a complex non-linear dependency among heterogeneous data. In this study, we propose a novel domain-adversarial multi-task framework for integrating shared knowledge from multiple domains. The framework first uses an adversarial strategy to learn target representations and then models non-linear dependency among several domains. RESULTS Experiments on two real-world datasets illustrate that our approach achieves an obvious improvement over competitive baselines. The novel therapeutic properties of purpose-unknown compounds that we predicted have been widely reported or brought to clinics. Furthermore, our framework can integrate various attributes beyond the three domains examined herein and can be applied in industry for screening significant numbers of small-molecule drug candidates. AVAILABILITY AND IMPLEMENTATION The source code and datasets are available at https://github.com/JohnnyY8/DAMT-Model. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lingwei Xie
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Song He
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Zhongnan Zhang
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Kunhui Lin
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Shu Yang
- Department of Computer Science, UC Santa Barbara, Santa Barbara, CA 93106, USA
| | - Boyuan Feng
- Department of Computer Science, UC Santa Barbara, Santa Barbara, CA 93106, USA
| | - Kun Wan
- Department of Computer Science, UC Santa Barbara, Santa Barbara, CA 93106, USA
| | - Kang Yang
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Jie Yang
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Yufei Ding
- Department of Computer Science, UC Santa Barbara, Santa Barbara, CA 93106, USA
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