مشاريع طلاب خريف 2022- F22
In silico detection for Beta Thalassemia via bioinformatics and expert systems
Aim of the study: Providing a guide to choose the most efficient way to design a new specific-primer by applying web services on SNPs from the HbVar database to understand the relationship between phenotype and genotype in the clinical setting and investigating the effects of SNP mutations in the HBB exons and give a guideline for functional studies and prenatal diagnosis to be developed as basis for future studies , Finding alternative therapeutic molecules made from natural inducers that had fewer side effects than traditional medications for treating beta thalassemia by recognizing the particular ligands that bind to specific receptor binding sites and recognize the foremost favorable ligand with the assistance of molecular docking , and creating a fuzzy inference system to predict the severity involve in Thalassemia disease.
Conclusion: Single nucleotide polymorphisms (SNPs) have been proposed as the next generation of markers to identify loci associated with complex diseases and their therapeutic treatment . Low-cost genotyping tools are absolutely necessary for effective personalized medicine ,so the in silico analysis like AS-PCR methods are quick, excellent and inexpensive strategies and require minimal instruments that are found in most laboratories to be developed for massive implementation into clinical laboratories . we hope that identify the mechanisms responsible for fetal hemoglobin control, since reactivation of fetal hemoglobin can provide major therapeutic benefits to people affected by β-hemoglobinopathies .
إعداد: الطالبة رند محمد حمزه خياطه
إشراف: الدكتور ياسر خضرا
In silico detection for Beta Thalassemia via bioinformatics and expert systems
محاكاة مرض جنف المراهقين مجهول السبب باستخدام أداة Synthea
Simulation of adolescent idiopathic scoliosis using the Synthea tool
The aim of this research is to review scientific publications related to the history of the disease, diagnostic markers, and treatment options. Additionally, the research seeks to create a model that simulates the disease using the Synthea tool, generating realistic but synthetic patient data. Furthermore, a field visit was conducted to the specialized unit for conservative treatment of scoliosis at Ibn Al-Nafees Hospital in Damascus, and demographic data of patients in the Syrian Arab Republic were obtained based on United Nations statistics for 2023.
The results demonstrate that using the Synthea tool to simulate adolescent idiopathic scoliosis can provide a bioinformatics model that supports clinical and therapeutic decision-making for scoliosis specialists. This can potentially be used in the future to build dedicated databases for adolescent idiopathic scoliosis and for educational and informational purposes for specialists at various academic levels. Moreover, it can be used to develop software applications that support diagnostic and therapeutic decision-making, thus enhancing and improving healthcare outcomes and positively impacting patient health.
إعداد : الطالبة ريم موسى قبه
إشراف: الدكتور ينال أحمد القدسي
إشراف مشارك: الدكتور داوود رزق الله قره كولله
التنبؤ بأمراض القلب باستخدام الذكاء الاصطناعي
Prediction of Heart Disease Using Artificial Intelligence
In this study, we proposed an efficient and accurate model for early prediction of cardiovascular disease, based on 13 features that are important for physicians to diagnose like age, gender, chest pain type, blood pressure, cholesterol, blood glucose, also on ECG reading, and other investigations. The model is based on machine learning techniques and artificial neural networks by using three dataset related to California University (Cleveland, Statlog) and from kaggle heart predicted data. The model performed by 3 platforms (SPSS, WEKA, Python), then developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree, Random Forest, XGBoost.
إعداد: الطالبة رهام علي نوفل
إشراف: الدكتور عبد القادر عبَادي
Investigating the role of aberrant alternative splicing in Cholangiocarcinoma via Integrated Bioinformatic
Aim of the study: This study was conducted to examine the impact of alternative splicing on cholangiocarcinoma. It aimed to analyze the differences in splicing patterns between tumoral and normal samples using bioinformatics-based alternative splicing detection tools. Furthermore, the study aimed to discover new biomarkers and investigate genetic modification techniques.
The results improve our understanding of the association between AS events and CHOL and might be a starting point for further research to confirm the importance of the splicing events studied in this research in CHOL and to identify new prognosis biomarkers.
إعداد: الطالبة ديما حسين سويد
إشراف: الدكتور ياسر خضرا
Identification of Key Genes and Pathways Associated with Post-Traumatic Stress Disorder
Post-Traumatic Stress Disorder (PTSD) is a complex multifactorial mental health condition characterized by a range of symptoms, including intrusive thoughts, nightmares, hypervigilance, and emotional distress, significantly affecting an individual's quality of life. While PTSD is relatively common, with a prevalence ranging from 6% to 10%, it varies depending on the population and specific traumatic events experienced. However, the exact pathogenesis of PTSD remains unclear, and accurate diagnosis can be challenging due to the possibility of inaccuracies in reporting symptoms. Furthermore, preventive therapies for PTSD development are limited. This study aimed to bridge these gaps by conducting an integrative bioinformatics analysis that explores the molecular mechanisms, identifies diagnostic markers, and discovers therapeutic targets for PTSD.
إعداد: الطالبة لين سمير خوري
إشراف: الدكتور مجد الجمالي
Identification of Key Genes and Pathways Associated with Post-Traumatic Stress Disorder
Predicting Breast Cancer Prognosis Using Machine Learning
The aim of the research is to find algorithms with high accuracy and sensitivity capable of predicting breast cancer prognosis and the cause of death in the study sample based on many variables in order to be able to intervene quickly in the patient's treatment protocol to reduce mortality as much as possible.
Materials and Methods: This study utilized the METABRIC database, containing targeted sequencing data of 1904 primary breast cancer samples, to predict breast cancer outcomes. Clinical and genetic attributes, such as age at diagnosis, type of surgery, chemotherapy, genetic expression levels, mutation data among others were analyzed using SPSS Statistics 25.0 and Python libraries. The dataset was split into training and test sets for model development and evaluation. Data preprocessing techniques were applied, and Python libraries facilitated data manipulation and analysis.
Conclusions: The study underscores the importance of selecting appropriate classification algorithms for predicting breast cancer patient outcomes. The Decision Tree and Random Forest algorithms offer promising results, while Logistic Regression may not be the most effective choice. These findings contribute to the field of breast cancer prognosis and provide insights for improving personalized treatment strategies. Future research can focus on exploring additional algorithms and incorporating more comprehensive datasets to further enhance predictive accuracy.
إعداد: الطالبة أريانا يونس يونس
إشراف: الدكتور مجد الجمالي
In-Silico Identification of Single Nucleotide Polymorphisms (SNPs) Associated with Alzheimer’s Disease
Aim of the study: in this project, we aim to identify genes contributing to neuroinflammation present in Alzheimer’s disease, identify SNPs located in these genes, and test if they are in linkage equilibrium thus, they could be inherited as a haplotype. Also identifying a haplotype that could possibly be frequent in populations and associated with AD.
Methods: We used in silico approaches to identify SNPs related to TNFα and other immune factors affecting Alzheimer’s disease whether directly or indirectly. We used various databases to search for genes that regulate inflammation through cytokines, interleukins, and immune cells.
Results: We identified 5 genes on chromosome 6 that are linked to inflammation, TNF-α, and Treg cells. We tested various SNP pairs to check if they are linked, and found 49 pairs to be in LD out of 84 pairs tested using LDpair. SNPs in high LD were selected and tested with LDhap to generate possible haplotypes. One haplotype with a frequency of 1.72% containing 4 significant SNPs was selected, which could be close to AD frequency in the elder (above 65 yrs) population (10.7%).
إعداد: الطالبة علياء عمار صالح
إشراف: الدكتور مجد الجمالي