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Xu F, Liu S, Zhao A, Shang M, Wang Q, Jiang S, Cheng Q, Chen X, Zhai X, Zhang J, Wang X, Yan J. iFLAS: positive-unlabeled learning facilitates full-length transcriptome-based identification and functional exploration of alternatively spliced isoforms in maize. New Phytol 2024; 241:2606-2620. [PMID: 38291701 DOI: 10.1111/nph.19554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/06/2024] [Indexed: 02/01/2024]
Abstract
The advent of full-length transcriptome sequencing technologies has accelerated the discovery of novel splicing isoforms. However, existing alternative splicing (AS) tools are either tailored for short-read RNA-Seq data or designed for human and animal studies. The disparities in AS patterns between plants and animals still pose a challenge to the reliable identification and functional exploration of novel isoforms in plants. Here, we developed integrated full-length alternative splicing analysis (iFLAS), a plant-optimized AS toolkit that introduced a semi-supervised machine learning method known as positive-unlabeled (PU) learning to accurately identify novel isoforms. iFLAS also enables the investigation of AS functions from various perspectives, such as differential AS, poly(A) tail length, and allele-specific AS (ASAS) analyses. By applying iFLAS to three full-length transcriptome sequencing datasets, we systematically identified and functionally characterized maize (Zea mays) AS patterns. We found intron retention not only introduces premature termination codons, resulting in lower expression levels of isoforms, but may also regulate the length of 3'UTR and poly(A) tail, thereby affecting the functional differentiation of isoforms. Moreover, we observed distinct ASAS patterns in two genes within heterosis offspring, highlighting their potential value in breeding. These results underscore the broad applicability of iFLAS in plant full-length transcriptome-based AS research.
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Affiliation(s)
- Feng Xu
- State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China
| | - Songyu Liu
- State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China
| | - Anwen Zhao
- State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China
| | - Meiqi Shang
- State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China
| | - Qian Wang
- State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China
| | - Shuqin Jiang
- State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China
| | - Qian Cheng
- State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China
| | - Xingming Chen
- Molbreeding Biotechnology Co., Ltd, Shijiazhuang, Hebei Province, 051430, China
| | - Xiaoguang Zhai
- Molbreeding Biotechnology Co., Ltd, Shijiazhuang, Hebei Province, 051430, China
| | - Jianan Zhang
- Molbreeding Biotechnology Co., Ltd, Shijiazhuang, Hebei Province, 051430, China
| | - Xiangfeng Wang
- State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China
| | - Jun Yan
- State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China
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Peng L, Huang L, Tian G, Wu Y, Li G, Cao J, Wang P, Li Z, Duan L. Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network. Front Microbiol 2023; 14:1244527. [PMID: 37789848 PMCID: PMC10543759 DOI: 10.3389/fmicb.2023.1244527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 08/16/2023] [Indexed: 10/05/2023] Open
Abstract
Background Microbes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe-disease association (MDA) prediction are expensive, time-consuming, and labor-intensive. Methods We developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe-disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs. Results GPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation. Conclusion The proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
- College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, China
| | - Liangliang Huang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Geng Tian
- Geneis (Beijing) Co. Ltd., Beijing, China
| | - Yan Wu
- Geneis (Beijing) Co. Ltd., Beijing, China
| | - Guang Li
- Faculty of Pediatrics, The Chinese PLA General Hospital, Beijing, China
- Department of Pediatric Surgery, The Seventh Medical Center of PLA General Hospital, Beijing, China
- National Engineering Laboratory for Birth Defects Prevention and Control of Key Technology, Beijing, China
- Beijing Key Laboratory of Pediatric Organ Failure, Beijing, China
| | - Jianying Cao
- Faculty of Pediatrics, The Chinese PLA General Hospital, Beijing, China
- Department of Pediatric Surgery, The Seventh Medical Center of PLA General Hospital, Beijing, China
- National Engineering Laboratory for Birth Defects Prevention and Control of Key Technology, Beijing, China
- Beijing Key Laboratory of Pediatric Organ Failure, Beijing, China
| | - Peng Wang
- School of Computer Science, Hunan Institute of Technology, Hengyang, China
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Hengyang, China
| | - Lian Duan
- Faculty of Pediatrics, The Chinese PLA General Hospital, Beijing, China
- Department of Pediatric Surgery, The Seventh Medical Center of PLA General Hospital, Beijing, China
- National Engineering Laboratory for Birth Defects Prevention and Control of Key Technology, Beijing, China
- Beijing Key Laboratory of Pediatric Organ Failure, Beijing, China
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Yan F, Zhao Z, Simon LM. EmptyNN: A neural network based on positive and unlabeled learning to remove cell-free droplets and recover lost cells in scRNA-seq data. Patterns (N Y) 2021; 2:100311. [PMID: 34430929 PMCID: PMC8369248 DOI: 10.1016/j.patter.2021.100311] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 06/04/2021] [Accepted: 06/18/2021] [Indexed: 11/26/2022]
Abstract
Droplet-based single-cell RNA sequencing (scRNA-seq) has significantly increased the number of cells profiled per experiment and revolutionized the study of individual transcriptomes. However, to maximize the biological signal, robust computational methods are needed to distinguish cell-free from cell-containing droplets. Here, we introduce a novel cell-calling algorithm called EmptyNN, which trains a neural network based on positive-unlabeled learning for improved filtering of barcodes. For benchmarking purposes, we leveraged cell hashing and genetic variation to provide ground truth. EmptyNN accurately removed cell-free droplets while recovering lost cell clusters, and achieved an area under the receiver operating characteristics of 94.73% and 96.30%, respectively. Comparisons to current state-of-the-art cell-calling algorithms demonstrated the superior performance of EmptyNN. EmptyNN was further applied to a single-nucleus RNA sequencing (snRNA-seq) dataset and showed good performance. Therefore, EmptyNN represents a powerful tool to enhance both scRNA-seq and snRNA-seq quality control analyses. The novel cell-calling algorithm EmptyNN improves the quality of scRNA-seq datasets EmptyNN accurately removes cell-free droplets and recovers genuine cells Benchmarking analyses leverage cell hashing information and genetic variation
Advances in measuring gene expression at the cellular level at high throughput have been fueled by the advent of droplet-based single-cell RNA sequencing (scRNA-seq) platforms. Droplet-based scRNA-seq platforms profile a large number of cells per experiment and accelerate our understanding of biology. Accurate classification of cell-free and cell-containing droplets will maximize biological signal and facilitate downstream analysis. Here, we present a novel cell-calling algorithm called EmptyNN, which trains a neural network based on positive-unlabeled learning for improved filtering of barcodes. Our results indicate that EmptyNN outperforms existing cell-calling methods and, thus, represents a powerful tool to enhance both scRNA-seq and single-nucleus RNA sequencing quality control analyses.
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Affiliation(s)
- Fangfang Yan
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Lukas M Simon
- Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX 77030, USA
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Abstract
Understanding the substrate specificity of HIV-1 protease plays an essential role in the prevention of HIV infection. A variety of computational models have thus been developed to predict substrate sites that are cleaved by HIV-1 protease, but most of them normally follow a supervised learning scheme to build classifiers by considering experimentally verified cleavable sites as positive samples and unknown sites as negative samples. However, certain noisy can be contained in the negative set, as false negative samples are possibly existed. Hence, the performance of the classifiers is not as accurate as they could be due to the biased prediction results. In this work, unknown substrate sites are regarded as unlabeled samples instead of negative ones. We propose a novel positive-unlabeled learning algorithm, namely PU-HIV, for an effective prediction of HIV-1 protease cleavage sites. Features used by PU-HIV are encoded from different perspectives of substrate sequences, including amino acid identities, coevolutionary patterns and chemical properties. By adjusting the weights of errors generated by positive and unlabeled samples, a biased support vector machine classifier can be built to complete the prediction task. In comparison with state-of-the-art prediction models, benchmarking experiments using cross-validation and independent tests demonstrated the superior performance of PU-HIV in terms of AUC, PR-AUC, and F-measure. Thus, with PU-HIV, it is possible to identify previously unknown, but physiologically existed substrate sites that are able to be cleaved by HIV-1 protease, thus providing valuable insights into designing novel HIV-1 protease inhibitors for HIV treatment.
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Affiliation(s)
- Zhenfeng Li
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Zehai Tang
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Cheng Zhao
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
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Song H, Bremer BJ, Hinds EC, Raskutti G, Romero PA. Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning. Cell Syst 2020; 12:92-101.e8. [PMID: 33212013 DOI: 10.1016/j.cels.2020.10.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 08/13/2020] [Accepted: 10/22/2020] [Indexed: 10/22/2022]
Abstract
Machine learning can infer how protein sequence maps to function without requiring a detailed understanding of the underlying physical or biological mechanisms. It is challenging to apply existing supervised learning frameworks to large-scale experimental data generated by deep mutational scanning (DMS) and related methods. DMS data often contain high-dimensional and correlated sequence variables, experimental sampling error and bias, and the presence of missing data. Notably, most DMS data do not contain examples of negative sequences, making it challenging to directly estimate how sequence affects function. Here, we develop a positive-unlabeled (PU) learning framework to infer sequence-function relationships from large-scale DMS data. Our PU learning method displays excellent predictive performance across ten large-scale sequence-function datasets, representing proteins of different folds, functions, and library types. The estimated parameters pinpoint key residues that dictate protein structure and function. Finally, we apply our statistical sequence-function model to design highly stabilized enzymes.
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Affiliation(s)
- Hyebin Song
- Department of Statistics, The Pennsylvania State University, State College, PA 16802, USA; Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Bennett J Bremer
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Emily C Hinds
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Garvesh Raskutti
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Philip A Romero
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
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Peng L, Shen L, Liao L, Liu G, Zhou L. RNMFMDA: A Microbe-Disease Association Identification Method Based on Reliable Negative Sample Selection and Logistic Matrix Factorization With Neighborhood Regularization. Front Microbiol 2020; 11:592430. [PMID: 33193260 PMCID: PMC7652725 DOI: 10.3389/fmicb.2020.592430] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 09/17/2020] [Indexed: 12/22/2022] Open
Abstract
Microbes with abnormal levels have important impacts on the formation and development of various complex diseases. Identifying possible Microbe-Disease Associations (MDAs) helps to understand the mechanisms of complex diseases. However, experimental methods for MDA identification are costly and time-consuming. In this study, a new computational model, RNMFMDA, was developed to find possible MDAs. RNMFMDA contains two main processes. First, Reliable Negative MDA samples were selected based on Positive-Unlabeled (PU) learning and random walk with restart on the heterogeneous microbe-disease network. Second, Logistic Matrix Factorization with Neighborhood Regularization (LMFNR) was developed to compute the association probabilities for all microbe-disease pairs. To evaluate the performance of the proposed RNMFMDA method, we compared RNMFMDA with five state-of-the-art MDA prediction methods based on five-fold cross-validations on microbes, diseases, and MDAs. As a result, RNMFMDA obtained the best AUCs of 0.6332, 0.8669, and 0.9081, respectively for the three five-fold cross validations, significantly outperforming other models. The promising prediction performance may be attributed to the following three features: highly quality negative MDA sample selection, LMFNR-based MDA prediction model, and various biological information integration. In addition, a few predicted microbe-disease pairs with high association scores are worthy of further experimental validation.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Ling Shen
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Longjie Liao
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Guangyi Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
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Ju Z, Wang SY. Computational Identification of Lysine Glutarylation Sites Using Positive-Unlabeled Learning. Curr Genomics 2020; 21:204-211. [PMID: 33071614 PMCID: PMC7521029 DOI: 10.2174/1389202921666200511072327] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 04/12/2020] [Accepted: 04/13/2020] [Indexed: 12/27/2022] Open
Abstract
Background
As a new type of protein acylation modification, lysine glutarylation has been found to play a crucial role in metabolic processes and mitochondrial functions. To further explore the biological mechanisms and functions of glutarylation, it is significant to predict the potential glutarylation sites. In the existing glutarylation site predictors, experimentally verified glutarylation sites are treated as positive samples and non-verified lysine sites as the negative samples to train predictors. However, the non-verified lysine sites may contain some glutarylation sites which have not been experimentally identified yet. Methods
In this study, experimentally verified glutarylation sites are treated as the positive samples, whereas the remaining non-verified lysine sites are treated as unlabeled samples. A bioinformatics tool named PUL-GLU was developed to identify glutarylation sites using a positive-unlabeled learning algorithm. Results
Experimental results show that PUL-GLU significantly outperforms the current glutarylation site predictors. Therefore, PUL-GLU can be a powerful tool for accurate identification of protein glutarylation sites. Conclusion
A user-friendly web-server for PUL-GLU is available at http://bioinform.cn/pul_glu/.
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Affiliation(s)
- Zhe Ju
- College of Science, Shenyang Aerospace University, Shenyang110136, P.R. China
| | - Shi-Yun Wang
- College of Science, Shenyang Aerospace University, Shenyang110136, P.R. China
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Vitsios D, Petrovski S. Mantis-ml: Disease-Agnostic Gene Prioritization from High-Throughput Genomic Screens by Stochastic Semi-supervised Learning. Am J Hum Genet 2020; 106:659-678. [PMID: 32386536 PMCID: PMC7212270 DOI: 10.1016/j.ajhg.2020.03.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 03/26/2020] [Indexed: 12/22/2022] Open
Abstract
Access to large-scale genomics datasets has increased the utility of hypothesis-free genome-wide analyses. However, gene signals are often insufficiently powered to reach experiment-wide significance, triggering a process of laborious triaging of genomic-association-study results. We introduce mantis-ml, a multi-dimensional, multi-step machine-learning framework that allows objective assessment of the biological relevance of genes to disease studies. Mantis-ml is an automated machine-learning framework that follows a multi-model approach of stochastic semi-supervised learning to rank disease-associated genes through iterative learning sessions on random balanced datasets across the protein-coding exome. When applied to a range of human diseases, including chronic kidney disease (CKD), epilepsy, and amyotrophic lateral sclerosis (ALS), mantis-ml achieved an average area under curve (AUC) prediction performance of 0.81-0.89. Critically, to prove its value as a tool that can be used to interpret exome-wide association studies, we overlapped mantis-ml predictions with data from published cohort-level association studies. We found a statistically significant enrichment of high mantis-ml predictions among the highest-ranked genes from hypothesis-free cohort-level statistics, indicating a substantial improvement over the performance of current state-of-the-art methods and pointing to the capture of true prioritization signals for disease-associated genes. Finally, we introduce a generic mantis-ml score (GMS) trained with over 1,200 features as a generic-disease-likelihood estimator, outperforming published gene-level scores. In addition to our tool, we provide a gene prioritization atlas that includes mantis-ml's predictions across ten disease areas and empowers researchers to interactively navigate through the gene-triaging framework. Mantis-ml is an intuitive tool that supports the objective triaging of large-scale genomic discovery studies and enhances our understanding of complex genotype-phenotype associations.
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Affiliation(s)
- Dimitrios Vitsios
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, 1 Francis Crick Avenue, CB2 0RE Cambridge, UK.
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, 1 Francis Crick Avenue, CB2 0RE Cambridge, UK.
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Xu Y, Gong M, Chen J, Chen Z, Batmanghelich K. 3D-BoxSup: Positive-Unlabeled Learning of Brain Tumor Segmentation Networks From 3D Bounding Boxes. Front Neurosci 2020; 14:350. [PMID: 32410939 PMCID: PMC7199456 DOI: 10.3389/fnins.2020.00350] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Accepted: 03/23/2020] [Indexed: 11/13/2022] Open
Abstract
Accurate segmentation is an essential task when working with medical images. Recently, deep convolutional neural networks achieved a state-of-the-art performance for many segmentation benchmarks. Regardless of the network architecture, the deep learning-based segmentation methods view the segmentation problem as a supervised task that requires a relatively large number of annotated images. Acquiring a large number of annotated medical images is time consuming, and high-quality segmented images (i.e., strong labels) crafted by human experts are expensive. In this paper, we have proposed a method that achieves competitive accuracy from a "weakly annotated" image where the weak annotation is obtained via a 3D bounding box denoting an object of interest. Our method, called "3D-BoxSup," employs a positive-unlabeled learning framework to learn segmentation masks from 3D bounding boxes. Specially, we consider the pixels outside of the bounding box as positively labeled data and the pixels inside the bounding box as unlabeled data. Our method can suppress the negative effects of pixels residing between the true segmentation mask and the 3D bounding box and produce accurate segmentation masks. We applied our method to segment a brain tumor. The experimental results on the BraTS 2017 dataset (Menze et al., 2015; Bakas et al., 2017a,b,c) have demonstrated the effectiveness of our method.
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Affiliation(s)
- Yanwu Xu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Mingming Gong
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, Australia
| | - Junxiang Chen
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Ziye Chen
- School of Compuer Science, Wuhan University, Wuhan, China
| | - Kayhan Batmanghelich
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
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Wang CC, Chen X, Yin J, Qu J. An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy. RNA Biol 2019; 16:257-269. [PMID: 30646823 DOI: 10.1080/15476286.2019.1568820] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
MicroRNAs (miRNAs) play an important role in prevention, diagnosis and treatment of human complex diseases. Predicting potential miRNA-disease associations could provide important prior information for medical researchers. Therefore, reliable computational models are expected to be an effective supplement for inferring associations between miRNAs and diseases. In this study, we developed a novel calculative model named Negative Samples Extraction based MiRNA-Disease Association prediction (NSEMDA). NSEMDA filtered reliable negative samples by two positive-unlabeled learning models, namely, the Spy and Rocchio techniques and calculated similarity weights for ambiguous samples. The positive samples, reliable negative samples and ambiguous samples with similarity weights were used to construct a Support Vector Machine-Similarity Weight model to predict miRNA-disease associations. NSEMDA improved the credibility of negative samples and reduced the impact of noise samples by introducing ambiguous samples with similarity weights to train prediction model. As a result, NSEMDA achieved the AUC of 0.8899 in global leave-one-out cross validation (LOOCV) and AUC of 0.8353 under local LOOCV. In 100 times 5-fold cross validation, NSEMDA obtained an average AUC of 0.8878 and standard deviation of 0.0014. These AUCs are higher than many classical models. Besides, we also carried out three kinds of case studies to evaluate the performance of NSEMDA. Among the top 50 potential related miRNAs of esophageal neoplasms, lung neoplasms and carcinoma hepatocellular predicted by NSEMDA, 46, 50 and 45 miRNAs were verified to be associated with the investigated disease by experimental evidences, respectively. Therefore, NSEMDA would be a reliable calculative model for inferring miRNA-disease associations.
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Affiliation(s)
- Chun-Chun Wang
- a School of Information and Control Engineering , China University of Mining and Technology , Xuzhou , China
| | - Xing Chen
- a School of Information and Control Engineering , China University of Mining and Technology , Xuzhou , China
| | - Jun Yin
- a School of Information and Control Engineering , China University of Mining and Technology , Xuzhou , China
| | - Jia Qu
- a School of Information and Control Engineering , China University of Mining and Technology , Xuzhou , China
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Ramola R, Jain S, Radivojac P. Estimating classification accuracy in positive-unlabeled learning: characterization and correction strategies. Pac Symp Biocomput 2019; 24:124-135. [PMID: 30864316 PMCID: PMC6417800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Accurately estimating performance accuracy of machine learning classifiers is of fundamental importance in biomedical research with potentially societal consequences upon the deployment of bestperforming tools in everyday life. Although classification has been extensively studied over the past decades, there remain understudied problems when the training data violate the main statistical assumptions relied upon for accurate learning and model characterization. This particularly holds true in the open world setting where observations of a phenomenon generally guarantee its presence but the absence of such evidence cannot be interpreted as the evidence of its absence. Learning from such data is often referred to as positive-unlabeled learning, a form of semi-supervised learning where all labeled data belong to one (say, positive) class. To improve the best practices in the field, we here study the quality of estimated performance in positive-unlabeled learning in the biomedical domain. We provide evidence that such estimates can be wildly inaccurate, depending on the fraction of positive examples in the unlabeled data and the fraction of negative examples mislabeled as positives in the labeled data. We then present correction methods for four such measures and demonstrate that the knowledge or accurate estimates of class priors in the unlabeled data and noise in the labeled data are sufficient for the recovery of true classification performance. We provide theoretical support as well as empirical evidence for the efficacy of the new performance estimation methods.
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