Corpus overview


MeSH Disease

Human Phenotype

Asthma (2)

Fever (1)

Cough (1)

Hypertension (1)

Pneumonia (1)


gender (1)


There are no seroprevalence terms in the subcorpus

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    Artificial Intelligence Based Study on Analyzing of Habits and with History of Diseases MESHD of Patients for Prediction of Recurrence of Disease Due to COVID-19

    Authors: Samir Kumar Bandyopadhyay; Shawni Dutta

    id:10.20944/preprints202008.0542.v1 Date: 2020-08-25 Source:

    A patient will visit physicians when he/she feels ill. This illness is not for COVID-19 but it is a general tendency of human being to visit doctor probably it can not be controlled by general drug. When a patient comes to a doctor, the doctor examines him/her after knowing his/her problem. The physician always asks him/her about some questions related to him/her daily life. For example, if a young male TRANS patient comes to a doctor with a symptom of fever HP fever MESHD and cough HP cough MESHD, the first question doctor asked him that he has a habit of smoking. Then doctor asks him whether this type of symptom appeared often to him previously or not. If the answers of both questions are yes, then the first one is habit and the second one is that he may suffering from some serious disease MESHD or a disease due to the weather. The aim of this paper is to consider habit of the patient as well as he/she has been affected by a critical disease. This information is used to build a model that will predict whether there is any possibility of his/her being affected by COVID-19. This research work contributes to tackle the pandemic situation occurred due to Corona Virus Infectious Disease MESHD, 2019 (Covid-19). Outbreak of this disease happens based on numerous factors such as past health records and habits of patients. Health records include diabetes tendency MESHD, cardiovascular disease MESHD existence, pregnancy, asthma HP asthma MESHD, hypertension HP hypertension MESHD, pneumonia HP pneumonia MESHD; chronic renal disease MESHD may contribute to this disease occurrence. Past lifestyles such as tobacco, alcohol consumption may be analyzed. A deep learning based framework is investigated to verify the relationship between past health records, habits of patients and covid-19 occurrence. A stacked Gated Recurrent Unit (GRU) based model is proposed in this paper that identifies whether a patient can be infected by this disease or not. The proposed predictive system is compared against existing benchmark Machine Learning classifiers such as Support Vector Machine (SVM) and Decision Tree (DT).

    Finding disease modules for cancer MESHD and COVID-19 in gene co-expression networks with the Core&Peel method

    Authors: Marta Lucchetta; Marco Pellegrini

    doi:10.1101/2020.05.27.118414 Date: 2020-05-27 Source: bioRxiv

    Diseases imply dysregulation of cells functions at several levels. The study of differentially expressed genes in case-control cohorts of patients is often the first step in understanding the details of the cells dysregulation. A further level of analysis is introduced by noticing that genes are organized in functional modules (often called pathways), thus their action and their dysregulation may be better understood by the identification of the modules most affected by the disease ( aka disease MESHD modules, or active subnetworks). We describe how an algorithm based on the Core&Peel method developed originally for detecting protein complexes in PPI networks, can be adapted to detect disease modules in co-expression networks of genes. We first validate Core&Peel for the easier general task of functional module detection by comparison with 42 methods participating in the Disease Module Identification DREAM challenge of 2019. Next, we use four specific disease test cases ( colorectal cancer MESHD cancer, prostate HP prostate cancer MESHD, asthma HP asthma MESHD and rheumatoid arthritis HP rheumatoid arthritis MESHD), four state-of-the-art algorithms (ModuleDiscoverer, Degas, KeyPathwayMiner and ClustEx), and several pathway databases to validate the proposed algorithm. Core&Peel is the only method able to find significant associations of the predicted disease module with known validated relevant pathways for all four diseases. Moreover for the two cancer MESHD data sets, Core&Peel detects further nine relevant pathways enriched in the predicted disease module, not discovered by the other methods used in the comparative analysis. Finally we apply Core&Peel, along with other methods, to explore the transcriptional response of human cells to SARS-CoV-2 infection MESHD, at a modular level, aiming at finding supporting evidence for drug repositioning efforts.

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MeSH Disease
Human Phenotype

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