Pinned Repositories
TumorDecon
The input of TumorDecon software is the gene expression profile of the tumor, and the output is the relative number of each cell type.
Mathematical-Model-of-Breast-Tumors-Progression-Based-on-Their-Immune-Infiltration
surgical-procedures-for-breast-cancer
Breast cancer has one of the most heterogeneous tumors. Since the outcome of breast cancer treatments strongly depends on the tumor subtypes, several studies investigated the outcome of surgical procedures for each of these subtypes. On the other hand, it has been shown that the outcome of breast cancer treatments is significantly different between black and white patients. In this study, to determine the optimal surgical procedure for each racial group of breast cancer patients with a given tumor subtype, we analyze clinical and gene expression data sets of 1082 patients with breast invasive carcinoma.
-A-review-of-digital-cytometry-methods
A-Bio-Mechanical-PDE-model-of-breast-tumor-progression-in-MMTV-PyMT-mice
Bio-Mechanical-model-of-osteosarcoma-tumor-microenvironment-A-porous-media-approach
Data-driven-mathematical-model-for-colon-cancer
Data-driven-mathematical-model-of-FOLFIRI-treatment-for-colon-cancer
Data-driven-mathematical-model-of-osteosarcom
Data driven mathematical model of osteosarcom
Data-Driven-QSP-Software-for-Personalized-Colon-Cancer-Treatment
Colon cancer is the third leading cause of cancer-related deaths in the United States in both men and women. A major clinical challenge is to obtain an effective treatment strategy for each patient or at least identify a subset of patients who could benefit from a particular treatment. Since each colon cancer has its own unique features, it is very important to obtain personalized cancer treatments and find a way to tailor treatment strategies for each patient based on each individual's characteristics, including race, gender, genetic factors, immune response variations. Recently, Quantitative and Systems Pharmacology (QSP) has been commonly used to discover, validate, and test drugs. QSP models are a system of differential equations that model the dynamic interactions between drug(s) and a biological system. These mathematical models provide an integrated “systems level” approach to determining mechanisms of action of drugs and finding new ways to alter complex cellular networks with mono or combination therapy to obtain effective treatments. Since QSP models are a complex system of nonlinear equations with many unknown parameters, estimating the values of the model's parameters is extremely difficult. Existing parameter estimation methods for QSP models often use assembled data from various sources rather than a single curated dataset. These datasets are usually obtained through various biological experiments, in vitro and in vivo animal studies, thus rendering QSP models hard to be practicable for personalized treatments. To the best of our knowledge, no QSP model has been developed for personalized colon cancer treatments. In this project, we propose a unique approach to develop a data-driven QSP software to suggest effective treatment for each patient based on gene expression data from the primary tumor samples. Since signatures of main characteristics of tumors, such as immune response variations, can be found in gene expression profiling of primary tumors, we use gene expression data as input. We develop an innovative framework to systematically employ a combination of data science, mathematical, and statistical methods to obtain personalized colon cancer treatment. We will use these techniques to propose an optimal treatment strategy for each patient and predict the efficacy of the proposed treatment. The model might also suggest alternative therapies in case of low efficacy for some patients.
ShahriyariLab's Repositories
ShahriyariLab/TumorDecon
The input of TumorDecon software is the gene expression profile of the tumor, and the output is the relative number of each cell type.
ShahriyariLab/Optimal-Fusion-of-Genotype-and-Drug-Embeddings-in-Predicting-Cancer-Drug-Response
ShahriyariLab/Bio-Mechanical-model-of-osteosarcoma-tumor-microenvironment-A-porous-media-approach
ShahriyariLab/Investigating-the-spatial-interaction-of-immune-cells-in-colon-cancer
ShahriyariLab/Patient-Specific-Mathematical-Model-of-Clear-Cell-Renal-Cell-Carcinoma-Microenvironme
ShahriyariLab/Data-Driven-QSP-Software-for-Personalized-Colon-Cancer-Treatment
Colon cancer is the third leading cause of cancer-related deaths in the United States in both men and women. A major clinical challenge is to obtain an effective treatment strategy for each patient or at least identify a subset of patients who could benefit from a particular treatment. Since each colon cancer has its own unique features, it is very important to obtain personalized cancer treatments and find a way to tailor treatment strategies for each patient based on each individual's characteristics, including race, gender, genetic factors, immune response variations. Recently, Quantitative and Systems Pharmacology (QSP) has been commonly used to discover, validate, and test drugs. QSP models are a system of differential equations that model the dynamic interactions between drug(s) and a biological system. These mathematical models provide an integrated “systems level” approach to determining mechanisms of action of drugs and finding new ways to alter complex cellular networks with mono or combination therapy to obtain effective treatments. Since QSP models are a complex system of nonlinear equations with many unknown parameters, estimating the values of the model's parameters is extremely difficult. Existing parameter estimation methods for QSP models often use assembled data from various sources rather than a single curated dataset. These datasets are usually obtained through various biological experiments, in vitro and in vivo animal studies, thus rendering QSP models hard to be practicable for personalized treatments. To the best of our knowledge, no QSP model has been developed for personalized colon cancer treatments. In this project, we propose a unique approach to develop a data-driven QSP software to suggest effective treatment for each patient based on gene expression data from the primary tumor samples. Since signatures of main characteristics of tumors, such as immune response variations, can be found in gene expression profiling of primary tumors, we use gene expression data as input. We develop an innovative framework to systematically employ a combination of data science, mathematical, and statistical methods to obtain personalized colon cancer treatment. We will use these techniques to propose an optimal treatment strategy for each patient and predict the efficacy of the proposed treatment. The model might also suggest alternative therapies in case of low efficacy for some patients.
ShahriyariLab/Mathematical-Model-of-Breast-Tumors-Progression-Based-on-Their-Immune-Infiltration
ShahriyariLab/A-Bio-Mechanical-PDE-model-of-breast-tumor-progression-in-MMTV-PyMT-mice
ShahriyariLab/Investigating-key-cell-types-and-molecules-dynamics-in-PyMT-mice-model-of-breast-cancer
ShahriyariLab/Data-driven-mathematical-model-of-osteosarcom
Data driven mathematical model of osteosarcom
ShahriyariLab/-A-review-of-digital-cytometry-methods
ShahriyariLab/Immune-Classification-of-Osteosarcoma
ShahriyariLab/Investigating-optimal-chemotherapy-options-for-osteosarcoma-patients-through-a-data-driven-mathemati
ShahriyariLab/Data-driven-mathematical-model-of-FOLFIRI-treatment-for-colon-cancer
ShahriyariLab/Data-driven-mathematical-model-for-colon-cancer
ShahriyariLab/RGS5
This is the code for our paper entitled "RGS5 Plays a Significant Role in Renal Cell Carcinoma".
ShahriyariLab/surgical-procedures-for-breast-cancer
Breast cancer has one of the most heterogeneous tumors. Since the outcome of breast cancer treatments strongly depends on the tumor subtypes, several studies investigated the outcome of surgical procedures for each of these subtypes. On the other hand, it has been shown that the outcome of breast cancer treatments is significantly different between black and white patients. In this study, to determine the optimal surgical procedure for each racial group of breast cancer patients with a given tumor subtype, we analyze clinical and gene expression data sets of 1082 patients with breast invasive carcinoma.