Learning oncogenetic networks by reducing to mixed integer linear programming. 2013

Hossein Shahrabi Farahani, and Jens Lagergren
KTH Royal Institute of Technology, Science for Life Laboratory (SciLifeLab), Center for Industrial and Applied Mathematics, School of Computer Science and Communication, Stockholm, Sweden.

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

UI MeSH Term Description Entries
D007680 Kidney Neoplasms Tumors or cancers of the KIDNEY. Cancer of Kidney,Kidney Cancer,Renal Cancer,Cancer of the Kidney,Neoplasms, Kidney,Renal Neoplasms,Cancer, Kidney,Cancer, Renal,Cancers, Kidney,Cancers, Renal,Kidney Cancers,Kidney Neoplasm,Neoplasm, Kidney,Neoplasm, Renal,Neoplasms, Renal,Renal Cancers,Renal Neoplasm
D008954 Models, Biological Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment. Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic
D009857 Oncogenes Genes whose gain-of-function alterations lead to NEOPLASTIC CELL TRANSFORMATION. They include, for example, genes for activators or stimulators of CELL PROLIFERATION such as growth factors, growth factor receptors, protein kinases, signal transducers, nuclear phosphoproteins, and transcription factors. A prefix of "v-" before oncogene symbols indicates oncogenes captured and transmitted by RETROVIRUSES; the prefix "c-" before the gene symbol of an oncogene indicates it is the cellular homolog (PROTO-ONCOGENES) of a v-oncogene. Transforming Genes,Oncogene,Transforming Gene,Gene, Transforming,Genes, Transforming
D011382 Programming, Linear A technique of operations research for solving certain kinds of problems involving many variables where a best value or set of best values is to be found. It is most likely to be feasible when the quantity to be optimized, sometimes called the objective function, can be stated as a mathematical expression in terms of the various activities within the system, and when this expression is simply proportional to the measure of the activities, i.e., is linear, and when all the restrictions are also linear. It is different from computer programming, although problems using linear programming techniques may be programmed on a computer. Linear Programming
D002292 Carcinoma, Renal Cell A heterogeneous group of sporadic or hereditary carcinoma derived from cells of the KIDNEYS. There are several subtypes including the clear cells, the papillary, the chromophobe, the collecting duct, the spindle cells (sarcomatoid), or mixed cell-type carcinoma. Adenocarcinoma, Renal Cell,Carcinoma, Hypernephroid,Grawitz Tumor,Hypernephroma,Renal Carcinoma,Adenocarcinoma Of Kidney,Adenocarcinoma, Renal,Chromophil Renal Cell Carcinoma,Chromophobe Renal Cell Carcinoma,Clear Cell Renal Carcinoma,Clear Cell Renal Cell Carcinoma,Collecting Duct Carcinoma,Collecting Duct Carcinoma (Kidney),Collecting Duct Carcinoma of the Kidney,Nephroid Carcinoma,Papillary Renal Cell Carcinoma,Renal Cell Cancer,Renal Cell Carcinoma,Renal Cell Carcinoma, Papillary,Renal Collecting Duct Carcinoma,Sarcomatoid Renal Cell Carcinoma,Adenocarcinoma Of Kidneys,Adenocarcinomas, Renal Cell,Cancer, Renal Cell,Carcinoma, Collecting Duct,Carcinoma, Collecting Duct (Kidney),Carcinoma, Nephroid,Carcinoma, Renal,Carcinomas, Collecting Duct,Carcinomas, Collecting Duct (Kidney),Carcinomas, Renal Cell,Collecting Duct Carcinomas,Collecting Duct Carcinomas (Kidney),Hypernephroid Carcinoma,Hypernephroid Carcinomas,Hypernephromas,Kidney, Adenocarcinoma Of,Nephroid Carcinomas,Renal Adenocarcinoma,Renal Adenocarcinomas,Renal Carcinomas,Renal Cell Adenocarcinoma,Renal Cell Adenocarcinomas,Renal Cell Cancers,Renal Cell Carcinomas,Tumor, Grawitz
D002869 Chromosome Aberrations Abnormal number or structure of chromosomes. Chromosome aberrations may result in CHROMOSOME DISORDERS. Autosome Abnormalities,Cytogenetic Aberrations,Abnormalities, Autosome,Abnormalities, Chromosomal,Abnormalities, Chromosome,Chromosomal Aberrations,Chromosome Abnormalities,Cytogenetic Abnormalities,Aberration, Chromosomal,Aberration, Chromosome,Aberration, Cytogenetic,Aberrations, Chromosomal,Aberrations, Chromosome,Aberrations, Cytogenetic,Abnormalities, Cytogenetic,Abnormality, Autosome,Abnormality, Chromosomal,Abnormality, Chromosome,Abnormality, Cytogenetic,Autosome Abnormality,Chromosomal Aberration,Chromosomal Abnormalities,Chromosomal Abnormality,Chromosome Aberration,Chromosome Abnormality,Cytogenetic Aberration,Cytogenetic Abnormality
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D001499 Bayes Theorem A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result. Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes
D012984 Software Sequential operating programs and data which instruct the functioning of a digital computer. Computer Programs,Computer Software,Open Source Software,Software Engineering,Software Tools,Computer Applications Software,Computer Programs and Programming,Computer Software Applications,Application, Computer Software,Applications Software, Computer,Applications Softwares, Computer,Applications, Computer Software,Computer Applications Softwares,Computer Program,Computer Software Application,Engineering, Software,Open Source Softwares,Program, Computer,Programs, Computer,Software Application, Computer,Software Applications, Computer,Software Tool,Software, Computer,Software, Computer Applications,Software, Open Source,Softwares, Computer Applications,Softwares, Open Source,Source Software, Open,Source Softwares, Open,Tool, Software,Tools, Software
D055028 Comparative Genomic Hybridization A method for comparing two sets of chromosomal DNA by analyzing differences in the copy number and location of specific sequences. It is used to look for large sequence changes such as deletions, duplications, amplifications, or translocations. Array Comparative Genomic Hybridization,Array-Based Comparative Genomic Hybridization,Comparative Genome Hybridization,Array Based Comparative Genomic Hybridization,Comparative Genome Hybridizations,Comparative Genomic Hybridizations,Genome Hybridization, Comparative,Genome Hybridizations, Comparative,Genomic Hybridization, Comparative,Genomic Hybridizations, Comparative,Hybridization, Comparative Genome,Hybridization, Comparative Genomic,Hybridizations, Comparative Genome,Hybridizations, Comparative Genomic

Related Publications

Hossein Shahrabi Farahani, and Jens Lagergren
January 2017, BMC bioinformatics,
Hossein Shahrabi Farahani, and Jens Lagergren
May 2021, Bioinformatics (Oxford, England),
Hossein Shahrabi Farahani, and Jens Lagergren
May 2014, Bioinformatics (Oxford, England),
Hossein Shahrabi Farahani, and Jens Lagergren
January 1995, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society,
Hossein Shahrabi Farahani, and Jens Lagergren
January 2023, PloS one,
Hossein Shahrabi Farahani, and Jens Lagergren
January 2008, IEEE/ACM transactions on computational biology and bioinformatics,
Hossein Shahrabi Farahani, and Jens Lagergren
July 1993, The British journal of nutrition,
Hossein Shahrabi Farahani, and Jens Lagergren
February 2018, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society,
Hossein Shahrabi Farahani, and Jens Lagergren
June 2016, Nucleic acids research,
Hossein Shahrabi Farahani, and Jens Lagergren
January 2004, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing,
Copied contents to your clipboard!