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

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A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 23234

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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
Special Issues, Collections and Topics in MDPI journals

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 undergone 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 of Biology particularly welcomes researchers who use deep learning or machine vision to address diverse biological questions relating to fundamental, biomedical and other 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 aid wet-lab biological researchers in asking 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.

[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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Related Special Issues

AI Deep Learning Approach to Study Biological Questions in Biology (8 articles)
AI Deep Learning Approach to Study Biological Questions (3rd Edition) in Biology

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Research

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31 pages, 4275 KB

Open AccessArticle

WCEDSAM: A Lightweight Multi-Scale Colonoscopy Polyp-Segmentation Network Combining Frequency-Domain Decomposition and Adaptive Feature Enhancement

by Lei Wang, Tongyu Wang, Sitong Liu, Zheng Chen, Jie Zhang, Cong Jin and Dexing Kong

Biology 2026, 15(9), 707; https://doi.org/10.3390/biology15090707 - 30 Apr 2026

Viewed by 378

Abstract

Colorectal cancer screening is challenged by variations in polyp morphology, indistinct polyp boundaries, and the high computational costs associated with current models. To address these issues, a lightweight medical image segmentation model, WCEDSAM, has been developed. WCEDSAM is based on a modified, compact version of MedSAM, which incorporates a Wavelet Transform-based component to extract and separate overlapping features at the pixel level. Additionally, a DSConv-ECA module is positioned before the ViT encoder to capture local features efficiently while reducing parameter count and enhancing inter-channel communication. Experimental results demonstrate that WCEDSAM achieves top performance on five public datasets, including Kvasir-SEG and CVC-ClinicDB, with 15.38 million parameters, achieving mean Dice (mDice) scores of 0.9383 on Kvasir-SEG and 0.9376 on CVC-ClinicDB. Cross-domain evaluations yield mDice scores of 0.9189 on CVC-ColonDB, 0.8961 on CVC-300, and 0.7765 on ETIS datasets, respectively, substantially outperforming other methods such as UNet++ and TransUNet. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))

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27 pages, 5256 KB

Open AccessArticle

AntID_APP: Empowering Citizen Scientists with YOLO Models for Ant Identification in Taiwan

by Nan-Yuan Hsiung, Jen-Shin Hong, Shiu-Wu Chau and Chung-Der Hsiao

Biology 2026, 15(6), 470; https://doi.org/10.3390/biology15060470 - 14 Mar 2026

Viewed by 907

Abstract

Ants are vital bioindicators that contribute to soil health and food webs, making accurate identification essential for biodiversity monitoring and conservation. However, traditional taxonomic methods are time-consuming and require specialized expertise, limiting large-scale data collection and public participation. This paper presents AntID_APP, a web-based application designed to support citizen scientists in Taiwan by enabling real-time, image-based detection and the identification of native ant genera. Fine-tuned YOLO models first detect ants in user-uploaded images and then classify them at the genus level. The models were trained on a curated dataset of 60,429 open-access images from iNaturalist, covering 54 native ant species. To ensure robustness in real-world conditions, we applied targeted data augmentation and evaluated multiple YOLO versions (v9–v12). The best-performing model achieved a mean Average Precision (mAP50: 0.935–0.948, mAP50-95: 0.777–0.807) for the detection task, followed by accurate genus-level identification. The application features an intuitive interface and a lightweight asynchronous server architecture, allowing users to upload images and receive both visual detection results (bounding boxes) and genus predictions efficiently. By combining high accuracy with accessibility, AntID_APP offers a scalable solution for biodiversity monitoring and public engagement in ecological research. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))

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15 pages, 1079 KB

Open AccessArticle

Preclinical HistoBench: A Pilot Benchmark Dataset for Evaluating Large Language Models on Preclinical Histopathological Classification

by Avan Kader, Marie-Luise H. H. Ranner-Hafferl, Felix Reuter, Miriam L. Fichtner, Marcus R. Makowski, Keno K. Bressem and Lisa C. Adams

Biology 2026, 15(5), 395; https://doi.org/10.3390/biology15050395 - 27 Feb 2026

Viewed by 525

Abstract

Background and Purpose: We present a pilot benchmark dataset of 378 preclinical histological samples for evaluating large language model (LLM) performance on multi-dimensional classification tasks. This dataset addresses the lack of standardized benchmarks for assessing LLMs in preclinical histopathology, encompassing species identification (mouse, rabbit, rat), organ recognition, staining methods, and preparation techniques. Methods: We evaluated the LLMs GPT-4.1, GPT-4o-mini, and Llama 3.2 on 378 histological samples across four classification dimensions: species identification (mouse, rabbit, rat), organ recognition (kidney, liver, prostate, spleen), staining method classification (H&E, Elastica van Gieson, collagen, iron, IHC-elastin, MOVAT’s pentachrome), and preparation type determination (frozen vs. paraffin-embedded). Performance was assessed using sensitivity and specificity metrics with confusion matrix analysis. Results: Model performance varied substantially across tasks and exhibited strong sensitivity to class imbalance. For preparation type classification, GPT-4.1 achieved the most balanced performance (50% frozen sensitivity, 85.7% paraffin sensitivity), while Llama 3.2 failed to recognize paraffin samples (0% sensitivity). In species classification, Llama 3.2 was the only model capable of identifying all three species (rabbit: 75% sensitivity, rat: 85.7% sensitivity) despite poor mouse recognition (0.3% sensitivity). GPT-4.1 achieved higher mouse sensitivity within this dataset (70.4% sensitivity) but failed with minority species. For staining classification, Llama 3.2 demonstrated highest overall performance, achieving >88% sensitivity for most staining types, while GPT-4o-mini showed perfect H&E recognition (100% sensitivity). Conclusions: Current LLMs demonstrate variable performance for histological classification with substantial sensitivity to class imbalance. While not suitable for standalone diagnostic use, they may serve as useful screening tools in research settings with appropriate human oversight. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))

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24 pages, 2326 KB

Open AccessArticle

Explainable Deep Learning Framework for Reliable Species-Level Classification Within the Genera Desmodesmus and Tetradesmus

by İlknur Meriç Turgut, Dilara Gerdan Koc and Özden Fakıoğlu

Biology 2026, 15(1), 99; https://doi.org/10.3390/biology15010099 - 3 Jan 2026

Viewed by 731

Abstract

Microalgae are an evolutionarily ancient and morphologically diverse group of photosynthetic eukaryotes, with taxonomic resolution complicated by environmentally driven phenotypic plasticity. This study merges deep learning and explainable artificial intelligence (XAI) to establish a transparent, reliable, and biologically meaningful framework for green microalgae (Chlorophyta) classification. Microscope images from three morphologically distinct algal species—Desmodesmus flavescens, Desmodesmus subspicatus, and Tetradesmus dimorphus representing the genera Desmodesmus and Tetradesmus within Chlorophyta—were analyzed using twelve convolutional neural networks, including EfficientNet-B0–B7, DenseNet201, NASNetLarge, Xception, and ResNet152V2. A curated dataset comprising 3624 microscopic images from three Chlorophyta species was used, split into training, validation, and test subsets. All models were trained using standardized preprocessing and data augmentation procedures, including grayscale conversion, CLAHE-based contrast enhancement, rotation, flipping, and brightness normalization. The model’s performance was assessed using accuracy and loss metrics on independent test datasets, while interpretability was evaluated through saliency maps and Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations. ResNet152V2 achieved the highest overall performance among all evaluated architectures, outperforming EfficientNet variants, NASNetLarge, and Xception in terms of macro F1-score. Visualization analysis showed that both Grad-CAM and saliency mapping consistently highlighted biologically relevant regions—including cell walls, surface ornamentation, and colony structures—confirming that the models relied on taxonomically meaningful features rather than background artifacts. The findings indicate that the integration of deep learning and XAI can attain consistently high test accuracy for microalgal species, even with constrained datasets. This approach enables automated taxonomy and supports biodiversity monitoring, ecological assessment, biomass optimization, and biodiesel production by integrating interpretability with high predictive accuracy. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))

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16 pages, 1173 KB

Open AccessEditor’s ChoiceArticle

Transformer-Based Classification of Transposable Element Consensus Sequences with TEclass2

by Lucas Bickmann, Matias Rodriguez, Xiaoyi Jiang and Wojciech Makałowski

Biology 2026, 15(1), 59; https://doi.org/10.3390/biology15010059 - 29 Dec 2025

Cited by 1 | Viewed by 1085

Abstract

Transposable elements (TEs) constitute a significant portion of eukaryotic genomes and play crucial roles in genome evolution, yet their diverse and complex sequences pose challenges for accurate classification. Existing tools often lack reliability in TE classification, limiting genomic analyses. Here, we present TEclass2, a software employing a deep learning approach based on a linear transformer architecture with k-mer tokenization and sequence-specific adaptations to classify TE consensus sequences into sixteen superfamilies. TEclass2 demonstrates improved classification performance and offers flexible model training on custom datasets. Accessible via a web interface with pre-trained models, TEclass2 facilitates rapid and reliable TE classification. These advancements provide a foundation for enhanced genomic annotation and support further bioinformatics research involving transposable elements. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))

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27 pages, 6127 KB

Open AccessArticle

High-Fidelity Transcriptome Reconstruction of Degraded RNA-Seq Samples Using Denoising Diffusion Models

by Ke Xiao, Jinlei Sun, Yunqing Liu, Chen Li, Hengchuan Guo, Yiying Wang, Yajun Pang and Zhiyu Liu

Biology 2025, 14(12), 1652; https://doi.org/10.3390/biology14121652 - 23 Nov 2025

Viewed by 1009

Abstract

Background: RNA degradation in clinically archived samples introduces systematic biases into RNA-seq data, limiting the accuracy of downstream analyses. Developing computational methods for high-fidelity transcriptome restoration is therefore of critical importance. Methods: We introduce DiffRepairer, a deep learning model that combines a Transformer architecture with a conditional diffusion model framework to reverse the effects of RNA degradation. The model is trained on “degraded-original” paired data, generated via a comprehensive simulation pipeline, to learn a direct, one-step repair mapping. Results: Across five diverse pseudo-degraded datasets, DiffRepairer demonstrated stable and superior performance, outperforming traditional statistical methods (e.g., CQN) and standard deep learning models (e.g., VAE) on key technical and biological metrics. Conclusion: DiffRepairer is a validated, high-precision tool for transcriptome repair that effectively restores biologically meaningful signals from degraded RNA-seq data, highlighting the potential of advanced generative models in bioinformatics. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))

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39 pages, 13819 KB

Open AccessArticle

Combination Ensemble and Explainable Deep Learning Framework for High-Accuracy Classification of Wild Edible Macrofungi

by Aras Fahrettin Korkmaz, Fatih Ekinci, Eda Kumru, Şehmus Altaş, Seyit Kaan Güneş, Ahmet Tunahan Yalçın, Mehmet Serdar Güzel and Ilgaz Akata

Biology 2025, 14(12), 1644; https://doi.org/10.3390/biology14121644 - 22 Nov 2025

Cited by 4 | Viewed by 1112

Abstract

Accurate identification of wild edible macrofungi is essential for biodiversity conservation, food safety, and ecological sustainability, yet remains challenging due to the morphological similarity between edible and toxic species. In this study, a curated dataset of 24 wild edible macrofungi species was analyzed using six state-of-the-art convolutional neural networks (CNNs) and four ensemble configurations, benchmarked across eight evaluation metrics. Among individual models, EfficientNetB0 achieved the highest performance (95.55% accuracy), whereas MobileNetV3-L underperformed (90.55%). Pairwise ensembles yielded inconsistent improvements, highlighting the importance of architectural complementarity. Notably, the proposed Combination Model, integrating EfficientNetB0, ResNet50, and RegNetY through a hierarchical voting strategy, achieved the best results with 97.36% accuracy, 0.9996 AUC, and 0.9725 MCC, surpassing all other models. To enhance interpretability, explainable AI (XAI) methods Grad-CAM, Eigen-CAM, and LIME were employed, consistently revealing biologically meaningful regions and transforming the framework into a transparent decision-support tool. These findings establish a robust and scalable paradigm for fine-grained fungal classification, demonstrating that carefully engineered ensemble learning combined with XAI not only advances mycological research but also paves the way for broader applications in plant recognition, spore analysis, and large-scale vegetation monitoring from satellite imagery. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))

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21 pages, 2727 KB

Open AccessArticle

Explainable Artificial Intelligence for Ovarian Cancer: Biomarker Contributions in Ensemble Models

by Hasan Ucuzal and Mehmet Kıvrak

Biology 2025, 14(11), 1487; https://doi.org/10.3390/biology14111487 - 24 Oct 2025

Cited by 2 | Viewed by 1451

Abstract

Ovarian cancer’s high mortality is primarily due to late-stage diagnosis, underscoring the critical need for improved early detection tools. This study develops and validates explainable artificial intelligence (XAI) models to discriminate malignant from benign ovarian masses using readily available demographic and laboratory data. A dataset of 309 patients (140 malignant, 169 benign) with 47 clinical parameters was analyzed. The Boruta algorithm selected 19 significant features, including tumor markers (CA125, HE4, CEA, CA19-9, AFP), hematological indices, liver function tests, and electrolytes. Five ensemble machine learning algorithms were optimized and evaluated using repeated stratified 5-fold cross-validation. The Gradient Boosting model achieved the highest performance with 88.99% (±3.2%) accuracy, 0.934 AUC-ROC, and 0.782 Matthews correlation coefficient. SHAP analysis identified HE4, CEA, globulin, CA125, and age as the most globally important features. Unlike black-box approaches, our XAI framework provides clinically interpretable decision pathways through LIME and SHAP visualizations, revealing how feature values push predictions toward malignancy or benignity. Partial dependence plots illustrated non-linear risk relationships, such as a sharp increase in malignancy probability with CA125 > 35 U/mL. This explainable approach demonstrates that ensemble models can achieve high diagnostic accuracy using routine lab data alone, performing comparably to established clinical indices while ensuring transparency and clinical plausibility. The integration of state-of-the-art XAI techniques highlights established biomarkers and reveals potential novel contributors like inflammatory and hepatic indices, offering a pragmatic, scalable triage tool to augment existing diagnostic pathways, particularly in resource-constrained settings. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))

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44 pages, 65610 KB

Open AccessEditor’s ChoiceArticle

scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data

by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang and Shilei Chen

Biology 2025, 14(6), 679; https://doi.org/10.3390/biology14060679 - 11 Jun 2025

Cited by 2 | Viewed by 3727

Abstract

Single-cell RNA sequencing now profiles whole transcriptomes for hundreds of thousands of cells, yet existing trajectory-inference tools rarely pinpoint where and when fate decisions are made. We present single-cell reinforcement learning (scRL), an actor–critic framework that recasts differentiation as a sequential decision process on an interpretable latent manifold derived with Latent Dirichlet Allocation. The critic learns state-value functions that quantify fate intensity for each cell, while the actor traces optimal developmental routes across the manifold. Benchmarks on hematopoiesis, mouse endocrinogenesis, acute myeloid leukemia, and gene-knockout and irradiation datasets show that scRL surpasses fifteen state-of-the-art methods in five independent evaluation dimensions, recovering early decision states that precede overt lineage commitment and revealing regulators such as Dapp1. Beyond fate decisions, the same framework produces competitive measures of lineage-contribution intensity without requiring ground-truth probabilities, providing a unified and extensible approach for decoding developmental logic from single-cell data. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))

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19 pages, 3993 KB

Open AccessArticle

Application of a ImageJ-Based Method to Measure Blood Flow in Adult Zebrafish and Its Applications for Toxicological and Pharmacological Assessments

by Ferry Saputra, Tzu-Ming Tseng, Franelyne P. Casuga, Yu-Heng Lai, Chih-Hsin Hung and Chung-Der Hsiao

Biology 2025, 14(1), 51; https://doi.org/10.3390/biology14010051 - 10 Jan 2025

Cited by 6 | Viewed by 3885

Abstract

Blood flow is an important physiological endpoint to measure cardiovascular performance in animals. Because of their innate transparent bodies, zebrafish is an excellent animal model for assessing in vivo cardiovascular performance. Previously, various helpful methods for measuring blood flow in zebrafish larvae were discovered and developed. However, an optimized method to measure blood flow in adult zebrafish has not been reported. In this paper, the tail fin region was selected as target for blood flow measurements using the Trackmate method, provided by ImageJ platform. Based on power statistic calculations, the aortic vessel at the tail base was selected, and other parameters, such as ambient temperature, were investigated for method standardization, in order to minimize experimental variation. The method was also validated using fenpropathrin and ponatinib, which showed some cardiac alterations in a previous zebrafish study. We also checked the versatility of this method by following the same setup in black tetra and medaka and found that this method performed well. However, our results show that heavy pigmentation, like that found in tiger barb, and overlapping vessels, like those in parrot fish, make it hard for this method to perform well. Overall, an optimized protocol was used for the first time to measure blood flow velocity in adult wild-type zebrafish without the aid of transgenic lines or fluorescent dye. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))

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Review

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27 pages, 1246 KB

Open AccessReview

Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery

by Yufei Ren, Haotian Bai, Jihan Wang, Yanning Yang and Yangyang Wang

Biology 2026, 15(5), 410; https://doi.org/10.3390/biology15050410 - 2 Mar 2026

Cited by 3 | Viewed by 1657

Abstract

Precise drug target discovery is pivotal to mitigating the escalating costs and high attrition rates that characterize pharmaceutical research and development. Given that traditional single-omics methods often fail to elucidate the systemic complexity of human diseases, deep learning (DL)-enabled multi-omics integration has emerged as a transformative frontier. This review systematically summarizes the advancements in DL-driven multi-omics integration for drug target discovery. First, the multi-omics data foundation and integration strategies are delineated, followed by an exploration of the DL architectures utilized for processing such data. Subsequently, the efficacy of DL-driven multi-omics integration is examined regarding the identification of novel disease drivers, prediction of synthetic lethality interactions, and prioritization of therapeutic targets. Finally, addressing persistent challenges related to data sparsity, model interpretability, and target druggability and validation hurdles, emerging opportunities driven by Generative AI, Large Multimodal Models (LMMs), Explainable AI (XAI), and multidimensional feasibility assessment frameworks are discussed in the context of advancing precision medicine. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))

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11 pages, 1402 KB

Open AccessBrief Report

A Deep Learning Approach to Measure Visual Function in Zebrafish

by Manjiri Patil, Annabel Birchall, Hammad Syed, Vanessa Rodwell, Ha-Jun Yoon, William H. J. Norton and Mervyn G. Thomas

Biology 2025, 14(6), 663; https://doi.org/10.3390/biology14060663 - 9 Jun 2025

Cited by 3 | Viewed by 4714

Abstract

Visual behaviour in zebrafish, often measured by the optokinetic reflex (OKR), serves as a valuable model for studying aspects of human neurological and ocular diseases and for conducting therapeutic or toxicology assays. Traditional methods for OKR analysis often rely on binarization techniques (threshold-based conversion of images to black and white) or costly software, which limits their utility in low-contrast settings or hypopigmented disease models. Here, we present a novel deep learning pipeline for OKR analysis, using ResNet-50 within the DeepLabCut framework in a Python Version 3.10 environment. Our approach employs object tracking to enable robust eye movement quantification, regardless of variations in contrast or pigmentation. OKR responses were elicited in both wild-type and slc45a2 (albino) mutant zebrafish larvae at 5 days post-fertilisation, using a mini-LED arena with a rotating visual stimulus. Eye movements were recorded and analysed using both conventional software and our deep learning approach. We demonstrate that the deep learning model achieves comparable accuracy to traditional methods, with the added benefits of applicability in diverse lighting conditions and in hypopigmented larvae. Statistical analyses, including Bland–Altman tests, confirmed the reliability of the deep learning model. While this study focuses on 5-day-old zebrafish larvae under controlled conditions, the pipeline is adaptable across developmental stages, pigmentation types, and behavioural assays. With appropriate adjustments to experimental parameters, it could be applied to broader behavioural studies, including social interactions and predator–prey dynamics in ocular and neurological disease models. Full article

(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))

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