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AI Deep Learning Approach to Study Biological Questions

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A special issue of Biology (ISSN 2079-7737).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 32116

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Prof. Dr. Chung-Der Hsiao

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Epidermal Stem Cell Lab, Department of Bioscience Technology, Chung Yuan Christian University, Chung-Li 320314, Taiwan
Interests: deep learning; image analysis; aquatic animal physiology and toxicology; new tool invention
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Dr. Tzong-Rong Ger

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Department of Biomedical Engineering, Chung Yuan Christian University, Chung-Li 320314, Taiwan
Interests: artificial intelligent; medical image analysis; bio-signal analysis; biosensor; smart medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the help of computer calculation power, we are witnessing a transition from the manual to the fully automated and systematic dissection of biological questions. Some research fields such as developmental biology, molecular biology, physiology, ecology, taxonomy, etc., have experienced considerable advancements with the aid of AI deep learning. For example, Daphnia and Zebrafish are two important aquatic animals used in developmental and toxicological studies. Using the U-Net/Mask RCNN deep learning and machine vision OpenCV approaches, we were able to address cardiac physiology alterations following exposure to environmental pollutants [1,2]. In tetrahymena, we were able to conduct precise cell quantification using the deep-learning-based StarDist tool [3]. This Special Issue, entitled “AI Deep Learning Approach to Study Biological Questions”, in Biology will particularly welcome researchers who use deep learning or machine vision to address diverse biological questions relating from fundamental to biomedical relevant fields. Image segmentation, classification, locomotion trajectory analysis, and volumetric prediction applied to plants, animals, or protozoa are especially welcome. Research into novel algorithms or new applications that can to help wet-lab biological researchers ask better biological questions will be appreciated. This Special Issue of Biology invites researchers and clinicians worldwide to submit their results or reviews within the scope of the title, “AI deep learning approach to study biological questions”.

[1] Saputra, F.; Farhan, A.; Suryanto, M.E.; Kurnia, K.A.; Chen, K.H.-C.; Vasquez, R.D.; Roldan, M.J.M.; Huang, J.-C.; Lin, Y.-K.; Hsiao, C.-D. Automated Cardiac Chamber Size and Cardiac Physiology Measurement in Water Fleas by U-Net and Mask RCNN Convolutional Networks. Animals 2022, 12, 1670. https://doi.org/10.3390/ani12131670

[2] Farhan, A.; Kurnia, K.A.; Saputra, F.; Chen, K.H.-C.; Huang, J.-C.; Roldan, M.J.M.; Lai, Y.-H.; Hsiao, C.-D. An OpenCV-Based Approach for Automated Cardiac Rhythm Measurement in Zebrafish from Video Datasets. Biomolecules 2021, 11, 1476. https://doi.org/10.3390/biom11101476

[3] Kurnia, K.A.; Sampurna, B.P.; Audira, G.; Juniardi, S.; Vasquez, R.D.; Roldan, M.J.M.; Tsao, C.-C.; Hsiao, C.-D. Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension. Int. J. Mol. Sci. 2022, 23, 6009. https://doi.org/10.3390/ijms23116009

Prof. Dr. Chung-Der Hsiao
Dr. Tzong-Rong Ger
Guest Editors

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Keywords

OpenCV
Mask RCNN
YOLO
U-Net
StarDist
ImageJ
MATLAB
image segmentation
image classification
locomotion trajectory analysis
volumetric prediction
plants
animals
protozoa
invertebrates
animal behavior
developmental biology
toxicology
zebrafish
medaka
daphnia

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AI Deep Learning Approach to Study Biological Questions (2nd Edition) in Biology

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Research

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19 pages, 16054 KiB

Open AccessArticle

Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images

by Lin-Na Zhao, Jian-Qiang Li, Wen-Xiu Cheng, Su-Qin Liu, Zheng-Kai Gao, Xi Xu, Cai-Hua Ye and Huan-Ling You

Biology 2022, 11(12), 1841; https://doi.org/10.3390/biology11121841 - 16 Dec 2022

Cited by 6 | Viewed by 1985

Abstract

Existing API approaches usually independently leverage detection or classification models to distinguish allergic pollens from Whole Slide Images (WSIs). However, palynologists tend to identify pollen grains in a progressive learning manner instead of the above one-stage straightforward way. They generally focus on two pivotal problems during pollen identification. (1) Localization: where are the pollen grains located? (2) Classification: which categories do these pollen grains belong to? To perfectly mimic the manual observation process of the palynologists, we propose a progressive method integrating pollen localization and classification to achieve allergic pollen identification from WSIs. Specifically, data preprocessing is first used to cut WSIs into specific patches and filter out blank background patches. Subsequently, we present the multi-scale detection model to locate coarse-grained pollen regions (targeting at “pollen localization problem”) and the multi-classifiers combination to determine the fine-grained category of allergic pollens (targeting at “pollen classification problem”). Extensive experimental results have demonstrated the feasibility and effectiveness of our proposed method. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions)

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19 pages, 4888 KiB

Open AccessArticle

A Systems Biology and LASSO-Based Approach to Decipher the Transcriptome–Interactome Signature for Predicting Non-Small Cell Lung Cancer

by Firoz Ahmed, Abdul Arif Khan, Hifzur Rahman Ansari and Absarul Haque

Biology 2022, 11(12), 1752; https://doi.org/10.3390/biology11121752 - 30 Nov 2022

Cited by 5 | Viewed by 2932

Abstract

The lack of precise molecular signatures limits the early diagnosis of non-small cell lung cancer (NSCLC). The present study used gene expression data and interaction networks to develop a highly accurate model with the least absolute shrinkage and selection operator (LASSO) for predicting NSCLC. The differentially expressed genes (DEGs) were identified in NSCLC compared with normal tissues using TCGA and GTEx data. A biological network was constructed using DEGs, and the top 20 upregulated and 20 downregulated hub genes were identified. These hub genes were used to identify signature genes with penalized logistic regression using the LASSO to predict NSCLC. Our model’s development involved the following steps: (i) the dataset was divided into 80% for training (TR) and 20% for testing (TD1); (ii) a LASSO logistic regression analysis was performed on the TR with 10-fold cross-validation and identified a combination of 17 genes as NSCLC predictors, which were used further for development of the LASSO model. The model’s performance was assessed on the TD1 dataset and achieved an accuracy and an area under the curve of the receiver operating characteristics (AUC-ROC) of 0.986 and 0.998, respectively. Furthermore, the performance of the LASSO model was evaluated using three independent NSCLC test datasets (GSE18842, GSE27262, GSE19804) and achieved high accuracy, with an AUC-ROC of >0.99, >0.99, and 0.95, respectively. Based on this study, a web application called NSCLCpred was developed to predict NSCLC. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions)

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16 pages, 4546 KiB

Open AccessArticle

KRS-Net: A Classification Approach Based on Deep Learning for Koi with High Similarity

by Youliang Zheng, Limiao Deng, Qi Lin, Wenkai Xu, Feng Wang and Juan Li

Biology 2022, 11(12), 1727; https://doi.org/10.3390/biology11121727 - 29 Nov 2022

Cited by 2 | Viewed by 3713

Abstract

As the traditional manual classification method has some shortcomings, including high subjectivity, low efficiency, and high misclassification rate, we studied an approach for classifying koi varieties. The main contributions of this study are twofold: (1) a dataset was established for thirteen kinds of koi; (2) a classification problem with high similarity was designed for underwater animals, and a KRS-Net classification network was constructed based on deep learning, which could solve the problem of low accuracy for some varieties that are highly similar. The test experiment of KRS-Net was carried out on the established dataset, and the results were compared with those of five mainstream classification networks (AlexNet, VGG16, GoogLeNet, ResNet101, and DenseNet201). The experimental results showed that the classification test accuracy of KRS-Net reached 97.90% for koi, which is better than those of the comparison networks. The main advantages of the proposed approach include reduced number of parameters and improved accuracy. This study provides an effective approach for the intelligent classification of koi, and it has guiding significance for the classification of other organisms with high similarity among classes. The proposed approach can be applied to some other tasks, such as screening, breeding, and grade sorting. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions)

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22 pages, 3570 KiB

Open AccessArticle

OpenBloodFlow: A User-Friendly OpenCV-Based Software Package for Blood Flow Velocity and Blood Cell Count Measurement for Fish Embryos

by Ali Farhan, Ferry Saputra, Michael Edbert Suryanto, Fahad Humayun, Roi Martin B. Pajimna, Ross D. Vasquez, Marri Jmelou M. Roldan, Gilbert Audira, Hong-Thih Lai, Yu-Heng Lai and Chung-Der Hsiao

Biology 2022, 11(10), 1471; https://doi.org/10.3390/biology11101471 - 8 Oct 2022

Cited by 4 | Viewed by 4880

Abstract

The transparent appearance of fish embryos provides an excellent assessment feature for observing cardiovascular function in vivo. Previously, methods to conduct vascular function assessment were based on measuring blood-flow velocity using third-party software. In this study, we reported a simple software, free of costs and skills, called OpenBloodFlow, which can measure blood flow velocity and count blood cells in fish embryos for the first time. First, videos captured by high-speed CCD were processed for better image stabilization and contrast. Next, the optical flow of moving objects was extracted from the non-moving background in a frame-by-frame manner. Finally, blood flow velocity was calculated by the Gunner Farneback algorithm in Python. Data validation with zebrafish and medaka embryos in OpenBloodFlow was consistent with our previously published ImageJ-based method. We demonstrated consistent blood flow alterations by either OpenBloodFlow or ImageJ in the dorsal aorta of zebrafish embryos when exposed to either phenylhydrazine or ractopamine. In addition, we validated that OpenBloodFlow was able to conduct precise blood cell counting. In this study, we provide an easy and fully automatic programming for blood flow velocity calculation and blood cell counting that is useful for toxicology and pharmacology studies in fish. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions)

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15 pages, 2595 KiB

Open AccessArticle

Estimating the Prognosis of Low-Grade Glioma with Gene Attention Using Multi-Omics and Multi-Modal Schemes

by Sanghyuk Roy Choi and Minhyeok Lee

Biology 2022, 11(10), 1462; https://doi.org/10.3390/biology11101462 - 5 Oct 2022

Cited by 11 | Viewed by 2260

Abstract

The prognosis estimation of low-grade glioma (LGG) patients with deep learning models using gene expression data has been extensively studied in recent years. However, the deep learning models used in these studies do not utilize the latest deep learning techniques, such as residual learning and ensemble learning. To address this limitation, in this study, a deep learning model using multi-omics and multi-modal schemes, namely the Multi-Prognosis Estimation Network (Multi-PEN), is proposed. When using Multi-PEN, gene attention layers are employed for each datatype, including mRNA and miRNA, thereby allowing us to identify prognostic genes. Additionally, recent developments in deep learning, such as residual learning and layer normalization, are utilized. As a result, Multi-PEN demonstrates competitive performance compared to conventional models for prognosis estimation. Furthermore, the most significant prognostic mRNA and miRNA were identified using the attention layers in Multi-PEN. For instance, MYBL1 was identified as the most significant prognostic mRNA. Such a result accords with the findings in existing studies that have demonstrated that MYBL1 regulates cell survival, proliferation, and differentiation. Additionally, hsa-mir-421 was identified as the most significant prognostic miRNA, and it has been extensively reported that hsa-mir-421 is highly associated with various cancers. These results indicate that the estimations of Multi-PEN are valid and reliable and showcase Multi-PEN’s capacity to present hypotheses regarding prognostic mRNAs and miRNAs. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions)

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18 pages, 5736 KiB

Open AccessArticle

Using DeepLabCut as a Real-Time and Markerless Tool for Cardiac Physiology Assessment in Zebrafish

by Michael Edbert Suryanto, Ferry Saputra, Kevin Adi Kurnia, Ross D. Vasquez, Marri Jmelou M. Roldan, Kelvin H.-C. Chen, Jong-Chin Huang and Chung-Der Hsiao

Biology 2022, 11(8), 1243; https://doi.org/10.3390/biology11081243 - 21 Aug 2022

Cited by 8 | Viewed by 5386

Abstract

DeepLabCut (DLC) is a deep learning-based tool initially invented for markerless pose estimation in mammals. In this study, we explored the possibility of adopting this tool for conducting markerless cardiac physiology assessment in an important aquatic toxicology model of zebrafish (Danio rerio). Initially, high-definition videography was applied to capture heartbeat information at a frame rate of 30 frames per second (fps). Next, 20 videos from different individuals were used to perform convolutional neural network training by labeling the heart chamber (ventricle) with eight landmarks. Using Residual Network (ResNet) 152, a neural network with 152 convolutional neural network layers with 500,000 iterations, we successfully obtained a trained model that can track the heart chamber in a real-time manner. Later, we validated DLC performance with the previously published ImageJ Time Series Analysis (TSA) and Kymograph (KYM) methods. We also evaluated DLC performance by challenging experimental animals with ethanol and ponatinib to induce cardiac abnormality and heartbeat irregularity. The results showed that DLC is more accurate than the TSA method in several parameters tested. The DLC-trained model also detected the ventricle of zebrafish embryos even in the occurrence of heart abnormalities, such as pericardial edema. We believe that this tool is beneficial for research studies, especially for cardiac physiology assessment in zebrafish embryos. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions)

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Review

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26 pages, 3067 KiB

Open AccessReview

Deep Learning Methods for Omics Data Imputation

by Lei Huang, Meng Song, Hui Shen, Huixiao Hong, Ping Gong, Hong-Wen Deng and Chaoyang Zhang

Biology 2023, 12(10), 1313; https://doi.org/10.3390/biology12101313 - 7 Oct 2023

Cited by 6 | Viewed by 4024

Abstract

One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or non-monotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions)

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15 pages, 1439 KiB

Open AccessReview

Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review

by Julien Issa, Mazen Abou Chaar, Bartosz Kempisty, Lukasz Gasiorowski, Raphael Olszewski, Paul Mozdziak and Marta Dyszkiewicz-Konwińska

Biology 2022, 11(10), 1412; https://doi.org/10.3390/biology11101412 - 28 Sep 2022

Cited by 6 | Viewed by 4825

Abstract

This systematic scoping review aims to map and identify the available artificial-intelligence-based techniques for imaging analysis, the characterization of stem cell differentiation, and trans-differentiation pathways. On the ninth of March 2022, data were collected from five electronic databases (PubMed, Medline, Web of Science, Cochrane, and Scopus) and manual citation searching; all data were gathered in Zotero 5.0. A total of 4422 articles were collected after deduplication; only twenty-seven studies were included in this systematic scoping review after a two-phase screening against inclusion criteria by two independent reviewers. The amount of research in this field is significantly increasing over the years. While the current state of artificial intelligence (AI) can tackle a multitude of medical problems, the consensus amongst researchers remains that AI still falls short in multiple ways that investigators should examine, ranging from the quality of images used in training sets and appropriate sample size, as well as the unexpected events that may occur which the algorithm cannot predict. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions)

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