Ali Masoudi-Nejad

TL;DR: A new algorithm, Kavosh, for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms, based on counting all k- size sub-graphs of a given graph (directed or undirected).

TL;DR: This framework represents the logics of complex cancer biology as a myriad of phenotypic complexities governed by a limited set of underlying organizing principles and adds to the understanding of tumor evolution and tumorigenesis, and the potential usefulness of predicting tumors' evolutionary paths and clinical phenotypes.

TL;DR: EGassembler is a web server, which provides an automated as well as a user-customized analysis tool for cleaning, repeat masking, vector trimming, organelle masksing, clustering and assembling of ESTs and genomic fragments and provides an excellent bioinformatics tool to research groups in wet-lab as an all-in-one-tool for sequence handling to bioinformics researchers.

TL;DR: In this article, a cancer hallmark network framework for modelling genome-sequencing data to predict cancer clonal evolution and associated clinical phenotypes is discussed. And the authors discuss strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for a cancer patient, as well as cancer risks for a healthy individual.

TL;DR: This paper presents a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently and is able to identifylarger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms.