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Journal articles on the topic 'Disease model'

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Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Rifki Taufik, Muhammad, Dwi Lestari, and Tri Wijayanti Septiarini. "Mathematical Model for Vaccinated Tuberculosis Disease with VEIT Model." International Journal of Modeling and Optimization 5, no. 3 (June 2015): 192–97. http://dx.doi.org/10.7763/ijmo.2015.v5.460.

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Krishnaprasath, V. T., and J. Preethi. "Finite automata model for leaf disease classification." Agricultural Economics (Zemědělská ekonomika) 67, No. 6 (June 25, 2021): 220–26. http://dx.doi.org/10.17221/70/2020-agricecon.

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In this modern era, the detection of plant disease plays a vital role in the sustainability of agricultural ecosystem. Today, India being second in farming, well-timed information related to crop is still questioning. Indian Government's farmer portal is available for pesticides, fertilisers, and farm machinery. To alleviate this problem, the paper describes a model to validate the leaf image, predicting leaf disease and notifying the farmer in an effective way on the harvest failure to stabilise farming income. For specific consideration on the validation, a data set library with predefined, uniformly scaled, regular image patterns of leaf disease, is maintained. The research suggests that farmers utilising the model can predict the breakout of leaf disease predominantly acquiring 100% yield.

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Rani, K. Sandhya, M. Sai Manoj, and G. Suguna Mani. "A Heart Disease Prediction Model using Logistic Regression." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1463–66. http://dx.doi.org/10.31142/ijtsrd11401.

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Shimohama, Shun, Hideyuki Sawada, Yoshihisa Kitamura, and Takashi Taniguchi. "Disease model: Parkinson's disease." Trends in Molecular Medicine 9, no. 8 (August 2003): 360–65. http://dx.doi.org/10.1016/s1471-4914(03)00117-5.

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Ahmad-Sabry, Mohammad H. I. "Disease Model." Medicine 94, no. 15 (April 2015): e711. http://dx.doi.org/10.1097/md.0000000000000711.

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DISHMEMA, Elfrida, and Lulezim HANELLI. "A SIR Model for Measles Disease Case for Albania." International Journal of Innovative Research in Engineering & Management 6, no. 4 (July 2019): 38–43. http://dx.doi.org/10.21276/ijirem.2019.6.4.3.

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M., Inbavalli. "Fuzzy Inference Model for Computation and Prediction of Disease Pattern." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 672–79. http://dx.doi.org/10.5373/jardcs/v12sp4/20201533.

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Subramanian, Suresh, and Y. Angeline Christobel. "A Hybrid Machine Learning Model to Predict Heart Disease Accurately." Indian Journal of Science and Technology 15, no. 12 (March 27, 2022): 527–34. http://dx.doi.org/10.17485/ijst/v15i12.104.

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Wang, Xixin, Daniëlle Copmans, and Peter A. M. de Witte. "Using Zebrafish as a Disease Model to Study Fibrotic Disease." International Journal of Molecular Sciences 22, no. 12 (June 15, 2021): 6404. http://dx.doi.org/10.3390/ijms22126404.

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In drug discovery, often animal models are used that mimic human diseases as closely as possible. These animal models can be used to address various scientific questions, such as testing and evaluation of new drugs, as well as understanding the pathogenesis of diseases. Currently, the most commonly used animal models in the field of fibrosis are rodents. Unfortunately, rodent models of fibrotic disease are costly and time-consuming to generate. In addition, present models are not very suitable for screening large compounds libraries. To overcome these limitations, there is a need for new in vivo models. Zebrafish has become an attractive animal model for preclinical studies. An expanding number of zebrafish models of human disease have been documented, for both acute and chronic diseases. A deeper understanding of the occurrence of fibrosis in zebrafish will contribute to the development of new and potentially improved animal models for drug discovery. These zebrafish models of fibrotic disease include, among others, cardiovascular disease models, liver disease models (categorized into Alcoholic Liver Diseases (ALD) and Non-Alcoholic Liver Disease (NALD)), and chronic pancreatitis models. In this review, we give a comprehensive overview of the usage of zebrafish models in fibrotic disease studies, highlighting their potential for high-throughput drug discovery and current technical challenges.

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Rani, K. Sandhya, M. Sai Chaitanya, and G. Sai Kiran. "A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBase." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1467–70. http://dx.doi.org/10.31142/ijtsrd11402.

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Stastny, P., V. A. Stembridge, T. L. Vischer, and M. Ziff. "HOMOLOGOUS DISEASE, A MODEL FOR AUTOIMMUNE DISEASE*." Annals of the New York Academy of Sciences 124, no. 1 (December 16, 2006): 158–61. http://dx.doi.org/10.1111/j.1749-6632.1965.tb18953.x.

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Saftig, Paul, Kurt von Figura, Yshitaka Tanaka, and Renate Lüllmann-Rauch. "Disease model: LAMP-2 enlightens Danon disease." Trends in Molecular Medicine 7, no. 1 (January 2001): 37–39. http://dx.doi.org/10.1016/s1471-4914(00)01868-2.

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Memon, Muhammad Suleman, Pardeep Kumar, and Rizwan Iqbal. "Meta Deep Learn Leaf Disease Identification Model for Cotton Crop." Computers 11, no. 7 (June 22, 2022): 102. http://dx.doi.org/10.3390/computers11070102.

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Agriculture is essential to the growth of every country. Cotton and other major crops fall into the cash crops. Cotton is affected by most of the diseases that cause significant crop damage. Many diseases affect yield through the leaf. Detecting disease early saves crop from further damage. Cotton is susceptible to several diseases, including leaf spot, target spot, bacterial blight, nutrient deficiency, powdery mildew, leaf curl, etc. Accurate disease identification is important for taking effective measures. Deep learning in the identification of plant disease plays an important role. The proposed model based on meta Deep Learning is used to identify several cotton leaf diseases accurately. We gathered cotton leaf images from the field for this study. The dataset contains 2385 images of healthy and diseased leaves. The size of the dataset was increased with the help of the data augmentation approach. The dataset was trained on Custom CNN, VGG16 Transfer Learning, ResNet50, and our proposed model: the meta deep learn leaf disease identification model. A meta learning technique has been proposed and implemented to provide a good accuracy and generalization. The proposed model has outperformed the Cotton Dataset with an accuracy of 98.53%.

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Stern, Hal S., and Noel Cressie. "Posterior predictive model checks for disease mapping models." Statistics in Medicine 19, no. 17-18 (2000): 2377–97. http://dx.doi.org/10.1002/1097-0258(20000915/30)19:17/18<2377::aid-sim576>3.0.co;2-1.

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Lakmeche, Abdelkader, Mohamed Helal, Imane Mammar, and Abdelghani Ouahab. "Impulsive prion disease model." ITM Web of Conferences 4 (2015): 01009. http://dx.doi.org/10.1051/itmconf/20150401009.

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Vrbová, Gerta, Linda Greensmith, and Katarzyna Sieradzan. "Motor neuron disease model." Nature 360, no. 6401 (November 1992): 216. http://dx.doi.org/10.1038/360216b0.

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Matsui, William. "Perspective: A model disease." Nature 480, no. 7377 (December 2011): S58. http://dx.doi.org/10.1038/480s58a.

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McMurray, David N. "Disease model: pulmonary tuberculosis." Trends in Molecular Medicine 7, no. 3 (March 2001): 135–37. http://dx.doi.org/10.1016/s1471-4914(00)01901-8.

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Li, Tiansen. "Disease model: photoreceptor degenerations." Trends in Molecular Medicine 7, no. 3 (March 2001): 133–35. http://dx.doi.org/10.1016/s1471-4914(00)01902-x.

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Kuro-o, Makoto. "Disease model: human aging." Trends in Molecular Medicine 7, no. 4 (April 2001): 179–81. http://dx.doi.org/10.1016/s1471-4914(01)01921-9.

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Wong, Jasmine C. Y., and Manuel Buchwald. "Disease model: Fanconi anemia." Trends in Molecular Medicine 8, no. 3 (March 2002): 139–42. http://dx.doi.org/10.1016/s1471-4914(01)02262-6.

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22

Davies, S., and P. Roberts. "Model of Huntington's disease." Science 241, no. 4864 (July 22, 1988): 474–75. http://dx.doi.org/10.1126/science.2969147.

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M.R. Tamjis, Surendar Aravindhan,. "Leaf Disease Detection by Using Convolutional Pretrained Model." International Journal of Communication Networks and Information Security (IJCNIS) 14, no. 1s (December 31, 2022): 114–20. http://dx.doi.org/10.17762/ijcnis.v14i1s.5619.

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Although agriculture plays an important role in developing countries such as India, food security remains a major concern. Due to a shortage of storage space, transportation, and plant diseases, the majority of crops are squandered. In India, infections cause more than 15% of crops to be wasted, making it one of the most pressing issues to be addressed. There is a need for an autonomous system that can detect these illnesses and assist farmers in taking the necessary procedures to avoid crop loss. Farmers have used the traditional approach of recognizing plant illnesses with their naked eyes. However, not all farmers can recognize these diseases in the same way. With the advancement of Artificial Intelligence, there is a need to apply computer vision capabilities to the agricultural area. Deep Learning's comprehensive libraries, as well as the user and developer-friendly environment in which to work, all combine to make Deep Learning the best way to get started with this topic. Taking leaves from diseased crops and identifying them according to the disease pattern is part of the process. Images of diseased leaves are processed using pixel-based procedures to improve the informational content of the images. The next step is feature extraction, image segmentation, and finally, classification of crop diseases based on patterns recovered from diseased leaves. Convolutional Neural Networks (CNNs) are used to classify diseases. Some of the deep learning pre-trained models have got more accuracy here. The comparison of two pre-trained models was shown.

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Save, Apurva, Aksham Gupta, Sarthak Pruthi, Divyanjana Nikam, and Prof Dr Shilpa Paygude. "Plant Disease Detection and Fertilizer Suggestion." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 351–56. http://dx.doi.org/10.22214/ijraset.2022.40275.

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Abstract: Plant disease diagnosis is the foundation for efficient and precise plant disease prevention in today's complicated environment. Plant disease identification has become digitised and data-driven as smart farming has grown, allowing for advanced decision support, smart analysis, and planning. This work provides a deep learning-based mathematical model for detecting and recognising plant diseases, which improves accuracy, generality, and training efficiency. The prevention and control of plant disease have consistently been broadly talked about in light of the fact that plants are presented to the external climate and are profoundly inclined to diseases. Typically, the precise and quick diagnosis of disease assumes a significant part in controlling plant disease, since helpful protection measures are frequently carried out after right diagnosis Identification of the plant diseases is the way to prevent the misfortunes in the yield and amount of the rural item. Early Detection of Plant Leaf Disease is a significant need in a developing horticultural economy like India. Without legitimate recognizable proof of the disease, disease control measures can be an exercise in futility and cash and can prompt further plant misfortunes. Our task proposes a profound learning-based model which will be trained utilizing a dataset containing pictures of healthy and diseased crop leaves. The model will serve its target by ordering pictures of leaves into diseased classes dependent on the example of imperfection. The framework effectively recognizes various sorts of disease found in Tomato Crop. Index Terms: Convolutional Neural Networks (CNN), Deep Learning, Pretrained models, Inceptionv3, Xceptionv3, MobilenetV2.

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Lee, Chanjung, Brian Jo, Hyunki Woo, Yoori Im, Rae Woong Park, and ChulHyoung Park. "Chronic Disease Prediction Using the Common Data Model: Development Study." JMIR AI 1, no. 1 (December 22, 2022): e41030. http://dx.doi.org/10.2196/41030.

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Background Chronic disease management is a major health issue worldwide. With the paradigm shift to preventive medicine, disease prediction modeling using machine learning is gaining importance for precise and accurate medical judgement. Objective This study aimed to develop high-performance prediction models for 4 chronic diseases using the common data model (CDM) and machine learning and to confirm the possibility for the extension of the proposed models. Methods In this study, 4 major chronic diseases—namely, diabetes, hypertension, hyperlipidemia, and cardiovascular disease—were selected, and a model for predicting their occurrence within 10 years was developed. For model development, the Atlas analysis tool was used to define the chronic disease to be predicted, and data were extracted from the CDM according to the defined conditions. A model for predicting each disease was built with 4 algorithms verified in previous studies, and the performance was compared after applying a grid search. Results For the prediction of each disease, we applied 4 algorithms (logistic regression, gradient boosting, random forest, and extreme gradient boosting), and all models show greater than 80% accuracy. As compared to the optimized model’s performance, extreme gradient boosting presented the highest predictive performance for the 4 diseases (diabetes, hypertension, hyperlipidemia, and cardiovascular disease) with 80% or greater and from 0.84 to 0.93 in area under the curve standards. Conclusions This study demonstrates the possibility for the preemptive management of chronic diseases by predicting the occurrence of chronic diseases using the CDM and machine learning. With these models, the risk of developing major chronic diseases within 10 years can be demonstrated by identifying health risk factors using our chronic disease prediction machine learning model developed with the real-world data–based CDM and National Health Insurance Corporation examination data that individuals can easily obtain.

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Singh, Ganesh Bahadur, Rajneesh Rani, Nonita Sharma, and Deepti Kakkar. "Identification of Tomato Leaf Diseases Using Deep Convolutional Neural Networks." International Journal of Agricultural and Environmental Information Systems 12, no. 4 (October 2021): 1–22. http://dx.doi.org/10.4018/ijaeis.20211001.oa3.

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Crop disease is a major issue now days; as it drastically reduces food production rate. Tomato is cultivated in major part of the world. The most common diseases that affect tomato crops are bacterial spot, early blight, septoria leaf spot, late blight, leaf mold, target spot, etc. In order to increase the production rate of tomato, early identification of diseases is highly required. The existing work contains very less accurate system for identification of tomato crop diseases. The goal of our work is to propose cost effective and efficient deep learning model inspired from Alexnet for identification of tomato crop diseases. To validate the performance of proposed model, experiments have also been done on standard pretrained models. The plantVillage dataset is used for the same, which contains 18,160 images of diseased and non-diseased tomato leaf. The disease identification accuracy of proposed model is compared with standard pretrained models and found that proposed model gave more promising results for tomato crop diseases identification.

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Ahmad, Bamanga Mahmud, Ahmadu Asabe Sandra, Musa Yusuf Malgwi, and Dahiru I. Sajoh. "Ensemble model for Heart Disease Prediction." Science Progress and Research 1, no. 4 (October 5, 2021): 268–80. http://dx.doi.org/10.52152/spr/2021.145.

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For the identification and prediction of different diseases, machine learning techniques are commonly used in clinical decision support systems. Since heart disease is the leading cause of death for both men and women around the world. Heart is one of the essential parts of human body, therefore, it is one of the most critical concerns in the medical domain, and several researchers have developed intelligent medical devices to support the systems and further to enhance the ability to diagnose and predict heart diseases. However, there are few studies that look at the capabilities of ensemble methods in developing a heart disease detection and prediction model. In this study, the researchers assessed that how to use ensemble model, which proposes a more stable performance than the use of base learning algorithm and these leads to better results than other heart disease prediction models. The University of California, Irvine (UCI) Machine Learning Repository archive was used to extract patient heart disease data records. To achieve the aim of this study, the researcher developed the meta-algorithm. The ensemble model is a superior solution in terms of high predictive accuracy and diagnostics output reliability, as per the results of the experiments. An ensemble heart disease prediction model is also presented in this work as a valuable, cost-effective, and timely predictive option with a user-friendly graphical user interface that is scalable and expandable. From the finding, the researcher suggests that Bagging is the best ensemble classifier to be adopted as the extended algorithm that has the high prediction probability score in the implementation of heart disease prediction.

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Aryal, Binod, and Youngseok Lee. "Disease model organism for Parkinson disease: Drosophila melanogaster." BMB Reports 52, no. 4 (April 30, 2019): 250–58. http://dx.doi.org/10.5483/bmbrep.2019.52.4.204.

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Pegoraro, Francesco, Matthias Papo, Valerio Maniscalco, Frédéric Charlotte, Julien Haroche, and Augusto Vaglio. "Erdheim–Chester disease: a rapidly evolving disease model." Leukemia 34, no. 11 (June 26, 2020): 2840–57. http://dx.doi.org/10.1038/s41375-020-0944-4.

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Kagnoff, Martin F. "Celiac disease: pathogenesis of a model immunogenetic disease." Journal of Clinical Investigation 117, no. 1 (January 2, 2007): 41–49. http://dx.doi.org/10.1172/jci30253.

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Ito, Kaori, Sima Ahadieh, Brian Corrigan, Jonathan French, Terence Fullerton, and Thomas Tensfeldt. "Disease progression meta-analysis model in Alzheimer's disease." Alzheimer's & Dementia 6, no. 1 (January 2010): 39–53. http://dx.doi.org/10.1016/j.jalz.2009.05.665.

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Ahmad, Aanis, Dharmendra Saraswat, Aly Gamal, and Gurmukh S. Johal. "Comparison of Deep Learning Models for Corn Disease Region Location, Identification of Disease Type, and Severity Estimation Using Images Acquired From UAS-Mounted and Handheld Sensors." Journal of the ASABE 65, no. 6 (2022): 1433–42. http://dx.doi.org/10.13031/ja.14895.

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Highlights An approach using deep learning was proposed for identifying diseased regions in UAS imagery of corn fields with 97.23% testing accuracy using the VGG16 model. Disease types were identified within the diseased regions with a testing accuracy of 98.85% using the VGG16 model. On the diseased leaves, severity was estimated with a testing accuracy of 94.20% using the VGG16 model. Deep Learning models have the potential to bring efficiency and accuracy to field scouting. Abstract. Accurately locating diseased regions, identifying disease types, and estimating disease severity in corn fields are all connected steps for developing an effective disease management system. Traditional disease management that relied on a manual scouting approach was inefficient. Therefore, the research community is working on developing advanced disease management systems using deep learning. However, most of the past studies used public datasets consisting of images with uniform backgrounds acquired under lab conditions to train deep learning models, thus, limiting their use under field conditions. In addition, limited studies have been conducted for in-field corn disease analysis using Unmanned Aerial System (UAS) imagery. Therefore, UAS and handheld imagery sensors were used in this study to acquire corn disease images from fields located at Purdue University’s Agronomy Center for Research and Education (ACRE) in the summer of 2020. A total of 55 UAS flights were conducted over three different corn fields from June 20 through September 29, resulting in a collection of approximately 59,000 images. A novel three-stage approach was proposed by independently training a total of nine image classification models using three neural network architectures, namely: VGG16, ResNet50, and InceptionV3, for locating diseased regions, identifying disease types, and estimating disease severity under field conditions. Diseased regions were first identified accurately in UAS-acquired corn field imagery by a sliding window and deep learning-based image classification, with testing accuracies of up to 97.23%. Diseased region identification was followed by accurately identifying three common corn diseases, namely Northern Leaf Blight (NLB), Gray Leaf Spot (GLS), and Northern Leaf Spot (NLS), within the diseased regions with testing accuracies of up to 98.85%. Finally, the severity of the NLS disease on leaves was estimated with a testing accuracy of up to 94.20%. The VGG16 model achieved the highest testing accuracies for identifying diseased regions in corn fields, identifying corn disease types, and estimating NLS's severity. This study presents promising results for three main elements of a disease management system and could advance traditional scouting by integrating deep learning with UAS imagery. Keywords: Corn Diseases, Datasets, Deep Learning, Disease Identification, Disease Region Location, Image Classification, Severity Estimation, UAS Imagery.

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Younis, Haseeb, Muhammad Asad Arshed, Fawad ul Hassan, Maryam Khurshid, and Hadia Ghassan. "Tomato Disease Classification using Fine-Tuned Convolutional Neural Network." Vol 4 Issue 1 4, no. 1 (February 13, 2022): 123–34. http://dx.doi.org/10.33411/ijist/2022040109.

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Tomatoes have enhanced vitamins that are necessary for mental and physical health. We use tomatoes in our daily life. The global agricultural industry is dominated by vegetables. Farmers typically suffer a significant loss when tomato plants are affected by multiple diseases. Diagnosis of tomato diseases at an early stage can help address this deficit. It is difficult to classify the attacking disease due to its range of manifestations. We can use deep learning models to identify diseased plants at an initial stage and take appropriate measures to minimize loss through early detection. For the initial diagnosis and classification of diseased plants, an effective deep learning model has been proposed in this paper. Our deep learning-based pre-trained model has been tuned twofold using a specific dataset. The dataset includes tomato plant images that show diseased and healthy tomato plants. In our classification, we intend to label each plant with the name of the disease or healthy that is afflicting it. With 98.93% accuracy, we were able to achieve astounding results using the transfer learning method on this dataset of tomato plants. Based on our understanding, this model appears to be lighter than other advanced models with such considerable results and which employ ten classes of tomatoes. This deep learning application is usable in reality to detect plant diseases.

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Lamb, Bruce T., Adrian L. Oblak, Harriet M. Williams, David Baglietto-Vargas, Marcelo A. Wood, Ali Mortazavi, Kim N. Green, et al. "P1-131: MODEL-AD: LATE-ONSET ALZHEIMER'S DISEASE MODELS." Alzheimer's & Dementia 14, no. 7S_Part_5 (July 1, 2006): P321. http://dx.doi.org/10.1016/j.jalz.2018.06.134.

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Phutthichayanon, Thanyada, and Surapol Naowarat. "Effects of Hand Washing Campaign on Dynamical Model of Hand Foot Mouth Disease." International Journal of Modeling and Optimization 5, no. 2 (April 2015): 104–8. http://dx.doi.org/10.7763/ijmo.2015.v5.444.

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Ilnytskyi, H., and J. Ilnytskyi. "Simple epidemiology model for a non-immune disease with ordinary and resistant carriers." Mathematical Modeling and Computing 4, no. 1 (July 1, 2017): 37–42. http://dx.doi.org/10.23939/mmc2017.01.037.

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Chakraborty, Subhadeep. "Multi-Disease Detection using Hybrid Machine Learning." Scholars Journal of Engineering and Technology 10, no. 10 (October 12, 2022): 271–78. http://dx.doi.org/10.36347/sjet.2022.v10i10.002.

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Machine Learning has a significant application in the detection of disease because of the automated process. Using machine learning models, the detection of disease can be done with higher effectiveness and with less error which may be seen in the context of computations made by humans. In this research, the detection of multiple diseases has been done with the application of machine learning. In this research context, three data have been selected namely Heart Disease Data (from UCI Repository), Liver Disease Data (from Kaggle Repository) and Diabetes Data (from Kaggle Repository). To detect disease, four state-of-the-art classifiers have been applied along with the proposed hybrid model. By applying those classifiers or machine learning models, the detection of three diseases has been done along with the comparison of performances. In that comparison, it has been observed that the proposed hybrid model has performed the best to detect all three types of disease. In the detection of heart disease, the proposed model has achieved 96.7% accuracy, for liver disease, the accuracy has reached 97.42% and for diabetes disease detection, the proposed model has acquired 97.39% accuracy. These performances of the proposed hybrid model have also been seen to be higher compared to the existing approaches for the detection of similar diseases.

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Whiteley, Andrew E., Trevor T. Price, Gaia Cantelli, and Dorothy A. Sipkins. "Leukaemia: a model metastatic disease." Nature Reviews Cancer 21, no. 7 (May 5, 2021): 461–75. http://dx.doi.org/10.1038/s41568-021-00355-z.

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Williams, Neil E. "The Factory Model of Disease." Monist 90, no. 4 (2007): 555–84. http://dx.doi.org/10.5840/monist200790437.

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Matsui, Hideaki, Roberto Gavinio, and Ryosuke Takahashi. "Medaka Fish Parkinson's Disease Model." Experimental Neurobiology 21, no. 3 (September 30, 2012): 94–100. http://dx.doi.org/10.5607/en.2012.21.3.94.

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Panov, Alexander, Sergey Dikalov, Natalia Shalbuyeva, Georgia Taylor, Todd Sherer, and J. Timothy Greenamyre. "Rotenone Model of Parkinson Disease." Journal of Biological Chemistry 280, no. 51 (October 21, 2005): 42026–35. http://dx.doi.org/10.1074/jbc.m508628200.

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Beal, M. Flint. "Parkinson's disease: a model dilemma." Nature 466, no. 7310 (August 2010): S8—S10. http://dx.doi.org/10.1038/466s8a.

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Chan Kwong, Anna, Catherine Cassé-Perrot, Marie-Claude Costes-Salon, Elisabeth Jouve, Laura Lanteaume, Christine Audebert, Franck Rouby, et al. "An Alzheimer Disease Challenge Model." Journal of Clinical Psychopharmacology 40, no. 3 (2020): 222–30. http://dx.doi.org/10.1097/jcp.0000000000001199.

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Tukhtamishev, M., and F. Akhmedova. "Tremorin model of Parkinson's disease." Parkinsonism & Related Disorders 79 (October 2020): e95-e96. http://dx.doi.org/10.1016/j.parkreldis.2020.06.347.

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Critchley, J. "Why model coronary heart disease?" European Heart Journal 23, no. 2 (January 15, 2002): 110–16. http://dx.doi.org/10.1053/euhj.2001.2681.

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Double, Duncan. "Giving up the disease model." Lancet Psychiatry 2, no. 8 (August 2015): 682. http://dx.doi.org/10.1016/s2215-0366(15)00273-4.

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Fodde, Riccardo, and Ron Smits. "Disease model: familial adenomatous polyposis." Trends in Molecular Medicine 7, no. 8 (August 2001): 369–73. http://dx.doi.org/10.1016/s1471-4914(01)02050-0.

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Arin, Meral J., and Dennis R. Roop. "Disease model: heritable skin blistering." Trends in Molecular Medicine 7, no. 9 (September 2001): 422–24. http://dx.doi.org/10.1016/s1471-4914(01)02095-0.

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Graham, Dustin M. "Scaling up disease model discovery." Lab Animal 46, no. 9 (September 2017): 334. http://dx.doi.org/10.1038/laban.1342.

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Wynshaw-Boris, Anthony. "Model mice and human disease." Nature Genetics 13, no. 3 (July 1996): 259–60. http://dx.doi.org/10.1038/ng0796-259.

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