Sharif Digital Repository

Evolutionary processes are the process of change in one or more physical and heritable characteristics that result from the occurrence of genetic changes (beneficial, harmful, or neutral) over time, and ultimately from generation to generation, depending on natural selection. Cancer is a genetic disease that occurs as a result of an evolutionary process by the somatic cells and examining the spatial characteristics of cancer can help understanding it. It is also important to examine the spatial configuration of cells considering their access to limiting factors such as nutrients and adequate space. In addition, paying attention to the gene expression, individually and collectively, will help... 

The aim of this thesis is to review the theory of deep learning with an experimental approach. In this thesis, we review researches that examine the impact of input selection on outputs in deep learning systems; Inputs we can control (samples, architecture, model size, optimizer, etc.) and outputs we can observe (the performance of the neural network, its test error, its parameters, etc.). Among the reviewed cases are the generalizability of deep learning systems, the effect of model components on its accuracy, interpolation and hyperparameters, as well as new phenomena in this field for which new frameworks have been defined  

As machine learning continues to be used extensively in all aspects of human life, especially social and legal decision making; Concerns have been raised about data-driven software and services biasing against certain demographic groups. Machine learning fairness, which refers to methods for correcting algorithmic bias in automated decision-making systems, is not only a social concern but also an industry need for developing human-centered tools.The study reviews studies on bias, fairness definitions, and attempts to reduce bias in machine learning models. Eventually, we suggest a method for reducing bias in imbalanced datasets  

One of the most available genomics data in Iran is non-invasive parental testing (NIPT) data obtained from the blood of pregnant mothers after the tenth week of pregnancy using the new generation sequencing technology. Sequencer output is a combination of maternal and fetal read data, most of which (about 90%) is from maternal DNA. These data have very low coverage of the genome, but their advantage is that they read the entire human genome. Low coverage data has led to the loss of large parts of the genome, but having a large number of samples helps to compensate for this problem. The purpose of this project is to use this data with the help of imputation methods to build a reference for... 

Colorectal cancer (CRC) is one of the most common cause of cancer death worldwide. Identification of pivot genes in colorectal cancer can play an important role as biomarkers in predicting and early diagnosis and reducing the number of deaths caused by this disease. In this study, the aim of which is to discover pivot genes in colorectal cancer, six microarray datasets selected from the GEO database including 277 tumor tissue samples and 325 normal colon tissue samples. After data processing, differentially expressed genes and CRC-related genes were screened and 285 shared genes between them were identified for subsequent analysis. Based on 285 shared genes, the protein-protein interaction... 

The ”Secretary Problem” is an easy model of online decision making unedr uncertainty, in which a small company intends to hire a new employee. It interviews with the applicants and after each interview, it should make a decision based on the information of the interviewees seen so for and without any knowledge of further applicants. The goal is to design a strategy of decision making with which the probability of choosing the best one is maximized.The secretary problem is not restricted to the issue of hiring an employee. It is advantageous in various areas such as economy,management, marriage and etc. The span of its utility makes it an intriguing problem that attracts the attention of many... 

One of the most important problems in online learning is a prediction with expert advice. In each step we make our prediction not only based on previous observation but also use expert information. In this thesis, we study the different well-known algorithms of expert advice and generalize problems when data arrival is in the two-dimensional grid. regret is a well-studied concept to evaluate online learning algorithm. online algorithm when data arrive consecutively in T time step has regret O (√(T)) . regret in two-dimensional grid with T row and P column is O(T√(P)).
2010 MSC: 68Q32 ; 68T05 ; 90C27  

In minimum cut problem we aim at finding a set of edges with minimum overall cost possible, such that removing them would separate two specific vertices, source and sink. This problem has been studied thoroughly, however the available algorithms were not proven to be time-wise optimal. Nowadays, planar graphs have attracted more attention because of their applications in cities and countries map. Moreover,their characteristics and properties, make some problems easier to solve in planar case. In this thesis, we will have a survey on algorithms for minimum cut on planar graphs. Our motivation to choose these algorithms is the balance between how easily they could be implemented and how fast... 

The Steiner forest problem is one of the most fundamental issues in network design that emerges in various contexts, such as the design of communication networks,transportation networks, VLSI circuits and phylogenetic network reconstruction. In this problem, we are given a connected weighted undirected graph and a special subset of vertices that are partitioned in pairs. The goal is to fnd a minimum-cost sub-graph in which each special vertex is connected to its mate. In this thesis we review a set of selected solutions for this problem and a few other related problems.For example, we study modeling and solving the s-t shortest path problem in order to gain insight into current Steiner... 

Multiplicative weight update method, is an statistical method to produce approximation algorithms. These algorithms have applications in various fields, including: Graph Algorithms, Machine learning, Optimization, Computational geometry, Game theory, etc.Recently, with this method, fast approximation algorithms have been created for many problems, including, solving linear programming. Here, we try to study this method and it’s applications, in detail  

Online problems were first introduced in 1980s due to process the data arise from the internet. An Online algorithm is an algorithm which takes a part of input at each step and should give an irrevocable answer before entrance of the rest of data. Online problems are considered when the size of the input is substantial or there are time limitations to wait for the whole input to arrive. One of the first online algorithms discussed was the online bipartite matching. In this problem there exists a bipartite graph with one offline part which is present from the beginning and nodes of the other part entering one by one with all of their corresponding edges. At the entrance of each node the... 

One of the most important targets in bio-informatics is the analysis of biological networks. These networks are modeled by graphs. Comparing networks with mapping is a useful tool for analyzing. The mapping between the nodes of a network that preserves some topological and functional structures, is called network alignment. Network alignment has various applications in different fields; such as pattern recognition, social networks, biological networks, and etc. The alignment of the protein-protein interaction network is one of the substantial problems. There are many static algorithms for the alignment of PPI networks. Because of the developments of computer science in recent years,... 

The field of Genome-Wide Asocciation Studies (GWAS) plays a vital role in understanding the genetic basis of complex traits and diseases. In this thesis, the focus is on investigating the effectiveness of two approaches combining Differential Evolution (DE) with Random Forest (RF) and support vector machine (SVM) for feature selection in the context of GWAS. Arabidopsois Thaliana dataset is used as experimental dataset for comparative analysis. The main goal is to achieve more efficient feature selection while maintaining competitive accuracy compared to RF and SVM without using DE. This research includes conducting experiments using DE with RF and DE with SVM followed by a comprehensive...