Robust classification of cell cycle phase and biological feature extraction by image-based deep learning

By applying convolutional neural network-based classifiers, we demonstrate that cell images can be robustly classified according to cell cycle phases. Combined with Grad-CAM analysis, our approach enables us to extract biological features underlying cellular phenomena of interest in an unbiased and data-driven manner.

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An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽

10.3390/info12060249 ◽

2021 ◽

Vol 12 (6) ◽

pp. 249

Author(s):

Xin Jin ◽

Yuanwen Zou ◽

Zhongbing Huang

Keyword(s):

Cell Cycle ◽

Deep Learning ◽

Image Classification ◽

Classification Accuracy ◽

Data Augmentation ◽

Cycle Phase ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

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Deep learning model for metagenome fragment classification using spaced k-mers feature extraction

Jurnal Teknologi dan Sistem Komputer ◽

10.14710/jtsiskom.2020.13407 ◽

2020 ◽

Vol 8 (3) ◽

pp. 234-238

Author(s):

Nur Choiriyati ◽

Yandra Arkeman ◽

Wisnu Ananta Kusuma

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Language Processing ◽

Computational Time ◽

Genus Level ◽

Computational Resources ◽

Learning Architectures ◽

Deep Learning Model

An open challenge in bioinformatics is the analysis of the sequenced metagenomes from the various environments. Several studies demonstrated bacteria classification at the genus level using k-mers as feature extraction where the highest value of k gives better accuracy but it is costly in terms of computational resources and computational time. Spaced k-mers method was used to extract the feature of the sequence using 111 1111 10001 where 1 was a match and 0 was the condition that could be a match or did not match. Currently, deep learning provides the best solutions to many problems in image recognition, speech recognition, and natural language processing. In this research, two different deep learning architectures, namely Deep Neural Network (DNN) and Convolutional Neural Network (CNN), trained to approach the taxonomic classification of metagenome data and spaced k-mers method for feature extraction. The result showed the DNN classifier reached 90.89 % and the CNN classifier reached 88.89 % accuracy at the genus level taxonomy.

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Performance Measures of Respiratory Disease Classification using Deep Learning

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35109 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 884-892

Author(s):

G. Rama Janani

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Performance Measures ◽

Respiratory Infections ◽

Respiratory Illness ◽

Extraction Methods ◽

Disease Classification ◽

Deep Cnn

The paper is based on classification of respiratory illness like covid 19 and pneumonia by using deep learning. The symptoms of COVID-19 and pneumonia are similar. Due to this, it is often difficult to identify what is causing your condition without being tested for COVID-19 or other respiratory infections. To find out how COVID-19 and pneumonia differs from one another, this paper presents that a novel Convolutional Neural Network in Tensor Flow and Keras based Covid-19 pneumonia classification. The proposed system supported implements CNN using Pneumonia images to classify the Covid-19, normal, pneumonia. The knowledge from these studies can potentially help in diagnosis of the concerned disease. It is predicted that the success of the anticipated results will increase if the CNN method is supported by adding extra feature extraction methods for classifying covid-19 and pneumonia successfully thereby improving the efficacy and potential of using deep CNN to pictures.

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Data Driven Cell Cycle Model to Quantify the Efficacy of Cancer Therapeutics Targeting Specific Cell-Cycle Phases From Flow Cytometry Results

Frontiers in Bioinformatics ◽

10.3389/fbinf.2021.662210 ◽

2021 ◽

Vol 1 ◽

Author(s):

David W. James ◽

Andrew Filby ◽

M. Rowan Brown ◽

Huw D. Summers ◽

Lewis W. Francis ◽

...

Keyword(s):

Cell Cycle ◽

Flow Cytometry ◽

Phase Transitions ◽

Mechanism Of Action ◽

Cell Cycle Phase ◽

Data Driven ◽

Cycle Phase ◽

Cell Populations ◽

Specific Cell ◽

Cell Cycle Phases

Many chemotherapeutic drugs target cell processes in specific cell cycle phases. Determining the specific phases targeted is key to understanding drug mechanism of action and efficacy against specific cancer types. Flow cytometry experiments, combined with cell cycle phase and division round specific staining, can be used to quantify the current cell cycle phase and number of mitotic events of each cell within a population. However, quantification of cell interphase times and the efficacy of cytotoxic drugs targeting specific cell cycle phases cannot be determined directly. We present a data driven computational cell population model for interpreting experimental results, where in-silico populations are initialized to match observable results from experimental populations. A two-stage approach is used to determine the efficacy of cytotoxic drugs in blocking cell-cycle phase transitions. In the first stage, our model is fitted to experimental multi-parameter flow cytometry results from untreated cell populations to identify parameters defining probability density functions for phase transitions. In the second stage, we introduce a blocking routine to the model which blocks a percentage of attempted transitions between cell-cycle phases due to therapeutic treatment. The resulting model closely matches the percentage of cells from experiment in each cell-cycle phase and division round. From untreated cell populations, interphase and intermitotic times can be inferred. We then identify the specific cell-cycle phases that cytotoxic compounds target and quantify the percentages of cell transitions that are blocked compared with the untreated population, which will lead to improved understanding of drug efficacy and mechanism of action.

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Growth and cell cycle phase composition of fibroblasts in the reconstituted dermis model.

Nishi Nihon Hifuka ◽

10.2336/nishinihonhifu.52.986 ◽

1990 ◽

Vol 52 (5) ◽

pp. 986-992

Author(s):

Takeshi KONO ◽

Tsukasa TANII ◽

Masayoshi FURUKAWA ◽

Nobuyuki MIZUNO ◽

Shoji TANIGUCHI ◽

...

Keyword(s):

Cell Cycle ◽

Phase Composition ◽

Cell Cycle Phase ◽

Cycle Phase

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Faculty Opinions recommendation of Degradation of the SCF component Skp2 in cell-cycle phase G1 by the anaphase-promoting complex.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1018701.240456 ◽

2004 ◽

Author(s):

Laura Itzhaki

Keyword(s):

Cell Cycle ◽

Cell Cycle Phase ◽

Cycle Phase ◽

Anaphase Promoting Complex

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Classification of papillary thyroid carcinoma histological images based on deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210100 ◽

2021 ◽

pp. 1-11

Author(s):

Yaning Liu ◽

Lin Han ◽

Hexiang Wang ◽

Bo Yin

Keyword(s):

Neural Network ◽

Differential Diagnosis ◽

Deep Learning ◽

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Image Features ◽

Papillary Thyroid ◽

Histological Image ◽

Histological Images

Papillary thyroid carcinoma (PTC) is a common carcinoma in thyroid. As many benign thyroid nodules have the papillary structure which could easily be confused with PTC in morphology. Thus, pathologists have to take a lot of time on differential diagnosis of PTC besides personal diagnostic experience and there is no doubt that it is subjective and difficult to obtain consistency among observers. To address this issue, we applied deep learning to the differential diagnosis of PTC and proposed a histological image classification method for PTC based on the Inception Residual convolutional neural network (IRCNN) and support vector machine (SVM). First, in order to expand the dataset and solve the problem of histological image color inconsistency, a pre-processing module was constructed that included color transfer and mirror transform. Then, to alleviate overfitting of the deep learning model, we optimized the convolution neural network by combining Inception Network and Residual Network to extract image features. Finally, the SVM was trained via image features extracted by IRCNN to perform the classification task. Experimental results show effectiveness of the proposed method in the classification of PTC histological images.

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