مشاريع طلاب ربيع 2022 _ S22
التحقق وتبيان وجود الجينات المتضمنة لطفرات نقطية ذات قيمة تنبؤية وتشخيصية في سرطان المبيض باستخدام أدوات المعلومات الحيوية
Identification of Hub Genes and Key Pathways Associated with Human Papillomavirus Status in Cervical Squamous Cell Carcinoma Based on Gene Expression Profiling via Integrated Bioinformatics
Using integrated bioinformatics to screen differentially expressed genes (DEGs) associated with two HPV status (HPV positive and HPV negative) in Cervical Squamous Cell Carcinoma could reveal valuable information about the pathogenic mechanism underlying the tumor progression. Moreover, the identification of significant differentially expressed genes, enrichment of their biological functions and key pathways, and visualization of the network of DEGs and hub genes will provide more accurate and reliable biomarkers and therapeutic targets for early diagnosis, individualized prevention measures, and improvement of therapeutic efficacy.
In this study, a series of analyses was conducted using R2 software of HPV status in squamous cervical carcinoma-related data in TCGA database to screen and identify prognostic biomarkers related to differentially expressed genes. Then, the up- and downregulated DEGs were classified into three groups (biological processes, molecular functions, and cellular components) according to Gene Ontology(GO) terms, and KEGG pathway enrichment analysis was conducted using DAVID websit.
إعداد الطالبة: مرح عماد مسعود
إشراف: الدكتورة لمى يوسف
الدكتور مجد الجمالي
تصميم لقاح افتراضي متعدد الحواتم ضد ضمة الكوليرا باستخدام أدوات المعلوماتية المناعية
Designing a virtual multi-epitopes vaccine against Vibrio cholerae using immunoinformatics
إعداد الطالبة: رفا شكيب صالح
إشراف: الدكتور عبد القادر عبّادي
تصميم لقاح افتراضي متعدد الحواتم ضد ضمة الكوليرا باستخدام أدوات المعلوماتية المناعية
التنبؤ بوفيات كوفيد-19 باستخدام التعلم الآلي
Predicting COVID-19 deaths using machine learning
Coronavirus disease 2019 (COVID-19) is a highly contagious viral disease that causes the severe acute respiratory syndrome (SARS), and has had a disastrous impact on demographics around the world. Studies have shown that using machine learning (ML) considered one of the most important lines of research to understand and fight COVID-19.
The aim of this study is to develop models for prediction the causes of mortality for patients with COVID-19 infection in order to make timely and effective clinical decision for COVID-19 treatments.
إعداد الطالبة: روزاليا ايليا معماري
إشراف: الدكتورة رشا مسعود
الدكتور زكريا الزلق
التنبؤ بوفيات كوفيد-19 باستخدام التعلم الآلي
تحليل المستضدات السرطانية لتطبيق العلاج المناعي المعتمد على الخلايا التغصنية
In silico analysis of cancer antigens of non-small cell lung cancer (NSCLC) for dendritic cell-based immune-gene therapy application
In this project, we aim to design a new structural model containing putative antigenic epitopes. Since immune stimulation is considered one of the most important mechanisms in tumor treatment, tumor cells can escape from the immune system. It may be advantageous to use T cell epitopes of different tumor Antigens simultaneously, the goal of using multiple antigenic epitopes instead of a single antigen is to avoid the specific antigen being lost or mutated. The use of multiple antigenic epitopes in a single structural model would cover a wide range of histocompatibility complex polymorphisms.
إعداد الطالبة : هند زهير اللو
إشراف: الدكتور مجد الجمالي
الدكتورة لمى يوسف
تحليل المستضدات السرطانية لتطبيق العلاج المناعي المعتمد على الخلايا التغصنية