#### Filter Results:

- Full text PDF available (11)

#### Publication Year

2009

2016

- This year (0)
- Last 5 years (10)
- Last 10 years (12)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Layla Oesper, Ahmad Mahmoody, Benjamin J. Raphael
- Genome Biology
- 2013

Tumor samples are typically heterogeneous, containing admixture by normal, non-cancerous cells and one or more subpopulations of cancerous cells. Whole-genome sequencing of a tumor sample yields reads from this mixture, but does not directly reveal the cell of origin for each read. We introduce THetA (Tumor Heterogeneity Analysis), an algorithm that infers… (More)

- Iman Hajirasouliha, Ahmad Mahmoody, Benjamin J. Raphael
- Bioinformatics
- 2014

MOTIVATION
High-throughput sequencing of tumor samples has shown that most tumors exhibit extensive intra-tumor heterogeneity, with multiple subpopulations of tumor cells containing different somatic mutations. Recent studies have quantified this intra-tumor heterogeneity by clustering mutations into subpopulations according to the observed counts of DNA… (More)

The multinomial model that we use in our likelihood function does not assume that the observed read depths in different intervals are independent. Even though we assume that reads are distributed uniformly on the cancer genome, large copy number aberrations (e.g. gain and loss of whole chromosomes) will cause the observed number of aligned reads in an… (More)

Article history: Received 20 October 2008 Accepted 21 January 2009 Available online 6 March 2009 Submitted by R.A. Brualdi AMS classification: 05C20 05C50 05C78

Betweenness centrality (BWC) is a fundamental centrality measure in social network analysis. Given a large-scale network, how can we find the most central nodes? This question is of great importance to many key applications that rely on BWC, including community detection and understanding graph vulnerability. Despite the large amount of work on scalable… (More)

- Ahmad Mahmoody, Crystal L. Kahn, Benjamin J. Raphael
- BMC Bioinformatics
- 2012

Many cancer genome sequencing efforts are underway with the goal of identifying the somatic mutations that drive cancer progression. A major difficulty in these studies is that tumors are typically heterogeneous, with individual cells in a tumor having different complements of somatic mutations. However, nearly all DNA sequencing technologies sequence DNA… (More)

- Ahmad Mahmoody, Matteo Riondato, Eli Upfal
- WSDM
- 2016

Detecting new information and events in a dynamic network by probing individual nodes has many practical applications: discovering new webpages, analyzing influence properties in network, and detecting failure propagation in electronic circuits or infections in public drinkable water systems. In practice, it is infeasible for anyone but the owner of the… (More)

- Saieed Akbari, M. Jamaali, Ahmad Mahmoody, Seyed Amin Seyed Fakhari
- Australasian J. Combinatorics
- 2009

Let G be a simple graph of order n and size m which is not a tree. If 3 is a natural number and the length of every cycle of G is divisible by , then m −2(n − 2), and the equality holds if and only if the following hold: (i) is odd and G is a cycle of order or (ii) is even and G is a generalized θ-graph with paths of length 2 . It is shown that for a (0 mod… (More)

- Ahmad Mahmoody, Eli Upfal
- ArXiv
- 2015

We formulate and study the Probabilistic Hitting Set Paradigm (PHSP), a general framework for design and analysis of search and detection algorithms in large scale dynamic networks. The PHSP captures applications ranging from monitoring new contents on the web, blogosphere, and Twitterverse, to analyzing influence properties in social networks, and… (More)

- Ahmad Mahmoody, Evgenios M. Kornaropoulos, Eli Upfal
- COCOA
- 2015

We formulate and study a fundamental search and detection problem, Schedule Optimization, motivated by a variety of real-world applications, ranging from monitoring content changes on the web, social networks, and user activities to detecting failure on large systems with many individual machines. We consider a large system consists of many nodes, where… (More)