1
|
Rahaman MM, Ahsan MA, Chen M. Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification. Sci Rep 2019; 9:19526. [PMID: 31862925 PMCID: PMC6925301 DOI: 10.1038/s41598-019-55609-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 11/21/2019] [Indexed: 11/09/2022] Open
Abstract
Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of local crop cultivars. Therefore, integrated or a new analytical approach is needed to deal with these phenomics data. We proposed a statistical framework for the analysis of phenomics data by integrating DM and ML methods. The most popular supervised ML methods; Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine with linear (SVM-l) and radial basis (SVM-r) kernel are used for classification/prediction plant status (stress/non-stress) to validate our proposed approach. Several simulated and real plant phenotype datasets were analyzed. The results described the significant contribution of the features (selected by our proposed approach) throughout the analysis. In this study, we showed that the proposed approach removed phenotype data analysis complexity, reduced computational time of ML algorithms, and increased prediction accuracy.
Collapse
Affiliation(s)
- Md Matiur Rahaman
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.,Department of Statistics, Faculty of Science, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, 8100, Bangladesh
| | - Md Asif Ahsan
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
| |
Collapse
|
2
|
Godinez WJ, Chan H, Hossain I, Li C, Ranjitkar S, Rasper D, Simmons RL, Zhang X, Feng BY. Morphological Deconvolution of Beta-Lactam Polyspecificity in E. coli. ACS Chem Biol 2019; 14:1217-1226. [PMID: 31184469 DOI: 10.1021/acschembio.9b00141] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Beta-lactams comprise one of the earliest classes of antibiotic therapies. These molecules covalently inhibit enzymes from the family of penicillin-binding proteins (PBPs), which are essential in construction of the bacterial cell wall. As a result, beta-lactams cause striking changes to cellular morphology, the nature of which varies by the range of PBPs simultaneously engaged in the cell. The traditional method of exploring beta-lactam polyspecificity is a gel-based binding assay which is low-throughput and typically is run ex situ in cell extracts. Here, we describe a medium-throughput, image-based assay combined with machine learning methods to automatically profile the activity of beta-lactams in E. coli cells. By testing for morphological change across a panel of strains with perturbations to individual PBP enzymes, our approach automatically and quantifiably relates different beta-lactam antibiotics according to their preferences for individual PBPs in cells. We show the potential of our approach for guiding the design of novel inhibitors toward different PBP-binding profiles by predicting the mechanisms of two recently reported PBP inhibitors.
Collapse
Affiliation(s)
- William J. Godinez
- Chemical Biology and Therapeutics, Novartis Institutes for BioMedical Research, Basel, Switzerland
- Infectious Diseases, Novartis Institutes for BioMedical Research, Emeryville, California, United States
| | - Helen Chan
- Infectious Diseases, Novartis Institutes for BioMedical Research, Emeryville, California, United States
| | - Imtiaz Hossain
- Chemical Biology and Therapeutics, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Cindy Li
- Infectious Diseases, Novartis Institutes for BioMedical Research, Emeryville, California, United States
| | - Srijan Ranjitkar
- Infectious Diseases, Novartis Institutes for BioMedical Research, Emeryville, California, United States
| | - Dita Rasper
- Infectious Diseases, Novartis Institutes for BioMedical Research, Emeryville, California, United States
| | - Robert L. Simmons
- Infectious Diseases, Novartis Institutes for BioMedical Research, Emeryville, California, United States
| | - Xian Zhang
- Chemical Biology and Therapeutics, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Brian Y. Feng
- Infectious Diseases, Novartis Institutes for BioMedical Research, Emeryville, California, United States
| |
Collapse
|
3
|
Nanni L, Brahnam S, Ghidoni S, Lumini A. Bioimage Classification with Handcrafted and Learned Features. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 16:874-885. [PMID: 29994096 DOI: 10.1109/tcbb.2018.2821127] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Bioimage classification is increasingly becoming more important in many biological studies including those that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new General Purpose (GenP) bioimage classification method that can be applied to a large range of classification problems. The GenP system we propose is an ensemble that combines multiple texture features (both handcrafted and learned descriptors) for superior and generalizable discriminative power. Our ensemble obtains a boosting of performance by combining local features, dense sampling features, and deep learning features. Each descriptor is used to train a different Support Vector Machine that is then combined by sum rule. We evaluate our method on a diverse set of bioimage classification tasks each represented by a benchmark database, including some of those available in the IICBU 2008 database. Each bioimage classification task represents a typical subcellular, cellular, and tissue level classification problem. Our evaluation on these datasets demonstrates that the proposed GenP bioimage ensemble obtains state-of-the-art performance without any ad-hoc dataset tuning of the parameters (thereby avoiding any risk of overfitting/overtraining). To reproduce the experiments reported in this paper, the MATLAB code of all the descriptors is available at https://github.com/LorisNanni and https://www.dropbox.com/s/bguw035yrqz0pwp/ElencoCode.docx?dl=0.
Collapse
|
4
|
Song Y, Li Q, Huang H, Feng D, Chen M, Cai W. Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1636-1649. [PMID: 28358678 DOI: 10.1109/tmi.2017.2687466] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Microscopy image classification is important in various biomedical applications, such as cancer subtype identification, and protein localization for high content screening. To achieve automated and effective microscopy image classification, the representative and discriminative capability of image feature descriptors is essential. To this end, in this paper, we propose a new feature representation algorithm to facilitate automated microscopy image classification. In particular, we incorporate Fisher vector (FV) encoding with multiple types of local features that are handcrafted or learned, and we design a separation-guided dimension reduction method to reduce the descriptor dimension while increasing its discriminative capability. Our method is evaluated on four publicly available microscopy image data sets of different imaging types and applications, including the UCSB breast cancer data set, MICCAI 2015 CBTC challenge data set, and IICBU malignant lymphoma, and RNAi data sets. Our experimental results demonstrate the advantage of the proposed low-dimensional FV representation, showing consistent performance improvement over the existing state of the art and the commonly used dimension reduction techniques.
Collapse
|
5
|
An Overview of data science uses in bioimage informatics. Methods 2017; 115:110-118. [DOI: 10.1016/j.ymeth.2016.12.014] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 12/09/2016] [Accepted: 12/30/2016] [Indexed: 01/17/2023] Open
|
6
|
Song Y, Cai W, Huang H, Feng D, Wang Y, Chen M. Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors. BMC Bioinformatics 2016; 17:465. [PMID: 27852213 PMCID: PMC5112644 DOI: 10.1186/s12859-016-1318-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Accepted: 11/01/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Bioimage classification is a fundamental problem for many important biological studies that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new bioimage classification method that can be generally applicable to a wide variety of classification problems. We propose to use a high-dimensional multi-modal descriptor that combines multiple texture features. We also design a novel subcategory discriminant transform (SDT) algorithm to further enhance the discriminative power of descriptors by learning convolution kernels to reduce the within-class variation and increase the between-class difference. RESULTS We evaluate our method on eight different bioimage classification tasks using the publicly available IICBU 2008 database. Each task comprises a separate dataset, and the collection represents typical subcellular, cellular, and tissue level classification problems. Our method demonstrates improved classification accuracy (0.9 to 9%) on six tasks when compared to state-of-the-art approaches. We also find that SDT outperforms the well-known dimension reduction techniques, with for example 0.2 to 13% improvement over linear discriminant analysis. CONCLUSIONS We present a general bioimage classification method, which comprises a highly descriptive visual feature representation and a learning-based discriminative feature transformation algorithm. Our evaluation on the IICBU 2008 database demonstrates improved performance over the state-of-the-art for six different classification tasks.
Collapse
Affiliation(s)
- Yang Song
- School of Information Technologies, The University of Sydney, Sydney, Australia
| | - Weidong Cai
- School of Information Technologies, The University of Sydney, Sydney, Australia
| | - Heng Huang
- Department of Computer Science and Engineering, University of Texas, Arlington, USA
| | - Dagan Feng
- School of Information Technologies, The University of Sydney, Sydney, Australia
- Med-X Research Institute, Shanghai Jiaotong University, Shanghai, China
| | - Yue Wang
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, USA
| | - Mei Chen
- Computer Engineering Department, University of Albany State University of New York, Albany, USA
- Robotics Institute, Carnegie Mellon University, Pittsburgh, USA
| |
Collapse
|
7
|
Dürr O, Sick B. Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks. ACTA ACUST UNITED AC 2016; 21:998-1003. [PMID: 26950929 DOI: 10.1177/1087057116631284] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 01/16/2016] [Indexed: 11/16/2022]
Abstract
Deep learning methods are currently outperforming traditional state-of-the-art computer vision algorithms in diverse applications and recently even surpassed human performance in object recognition. Here we demonstrate the potential of deep learning methods to high-content screening-based phenotype classification. We trained a deep learning classifier in the form of convolutional neural networks with approximately 40,000 publicly available single-cell images from samples treated with compounds from four classes known to lead to different phenotypes. The input data consisted of multichannel images. The construction of appropriate feature definitions was part of the training and carried out by the convolutional network, without the need for expert knowledge or handcrafted features. We compare our results against the recent state-of-the-art pipeline in which predefined features are extracted from each cell using specialized software and then fed into various machine learning algorithms (support vector machine, Fisher linear discriminant, random forest) for classification. The performance of all classification approaches is evaluated on an untouched test image set with known phenotype classes. Compared to the best reference machine learning algorithm, the misclassification rate is reduced from 8.9% to 6.6%.
Collapse
Affiliation(s)
- Oliver Dürr
- Zurich University of Applied Sciences, School of Engineering, Winterthur, Switzerland
| | - Beate Sick
- Zurich University of Applied Sciences, School of Engineering, Winterthur, Switzerland
| |
Collapse
|