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Wavelet Footprints and Sparse Bayesian Learning for DNA Copy Number Change Analysis

Roger Pique-Regi, En-Shuo Tsau, Antonio Ortega, Robert Seeger, Shahab Asgharzadeh
2007 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07  
First, wavelet footprints are used to obtain a basis for representing the DNA copy number that is maximally sparse in the number of copy number change points.  ...  Second, Sparse Bayesian Learning is applied to infer the copy number changes from noisy array probe intensities.  ...  sparse Bayesian learning (SBL) [10] .  ... 
doi:10.1109/icassp.2007.366689 dblp:conf/icassp/Pique-RegiTOSA07 fatcat:ll3uwr4ufbcjldiwtm3w6fefoe

Comprehensive Study Of Dna Copy Number Analysis Using Sigma Filter

Abdullah Alqallaf, A.H. Tewfik
2007 Zenodo  
More recent study [14] used wavelet footprints to obtain a basis for representing the DCN data that is maximally sparse then Sparse Bayesian Learning is applied to infer the copy number changes from  ...  It provides a high-resolution method to map and measure relative changes in DNA copy number simultaneously at thousands of genomic loci.  ... 
doi:10.5281/zenodo.40523 fatcat:e3xnumtphvfr7nx5hatwvlumyi

Maximum likelihood principle for DNA copy number analysis

Abdullah K. Alqallaf, Ahmed H. Tewfik
2009 2009 IEEE International Conference on Acoustics, Speech and Signal Processing  
In this paper, we present a robust procedure for the analysis of DNA copy number data based on maximum likelihood principle using global information of the entire data record.  ...  Microarray technologies had been used to measure DNA copy number data. The copy number represents the relative fluorescent intensity level between control and test DNA samples.  ...  It is used to obtain a basis for representing the DCN data that is maximally sparse and then sparse Bayesian learning is applied to infer the copy number changes from the noisy data.  ... 
doi:10.1109/icassp.2009.4959630 dblp:conf/icassp/AlqallafT09 fatcat:pgalnq4snnbq7ac527fgsij3wq

Joint Alignment of Multiple Protein–Protein Interaction Networks via Convex Optimization

Somaye Hashemifar, Qixing Huang, Jinbo Xu
2016 Journal of Computational Biology  
HSA models chromatin as a Markov chain under a generalized linear model framework, and uses simulated annealing to globally search for the latent structure underlying the cleavage footprints of different  ...  HSA is robust, accurate, and outperforms or rivals existing computational tools when evaluated on simulated and real datasets in diverse cell types.  ...  Fast Bayesian Inference of Copy Number Variants Using Hidden Markov Models with Wavelet Compression This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we  ... 
doi:10.1089/cmb.2016.0025 pmid:27428933 fatcat:tr4e3u3hqjaoziavhhsklxkkmi

AI in Health: State of the Art, Challenges, and Future Directions

Fei Wang, Anita Preininger
2019 IMIA Yearbook of Medical Informatics  
This review highlights recent developments over the past five years and directions for the future.  ...  This progress provides new opportunities and challenges, as well as directions for the future of AI in health.  ...  [8] proposed the iCluster framework for subtyping glioblastoma with three omics data types: copy number, mRNA expression, and DNA methylation data.  ... 
doi:10.1055/s-0039-1677908 pmid:31419814 pmcid:PMC6697503 fatcat:mhxvvtvvbzdffmha73cfxy3nua

A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data

Kshitij Khare, Sang-Yun Oh, Syed Rahman, Bala Rajaratnam
2019 Machine Learning  
Acknowledgment We have been fortunate to have our colleagues and collaborators give us their impressions and contributions toward the contents of this book. We would like to  ...  for genotyping, quantitative DNA analysis, gene expression analysis, analysis of indels and DNA methylation, and DNA/RNA sequencing.  ...  Learning Bayesian Networks With the modeling of data using Bayesian networks, the next challenge is learning from the modeled data.  ... 
doi:10.1007/s10994-019-05810-5 fatcat:nulmjvxvwjgojfoe2ywv3pjrpu

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Bayesian Learning DAY 3 -Jan 14, 2021 Chang, Xinyuan; Tao, Xiaoyu; Hong, Xiaopeng; WEI, Xing; Ke, Wei; Gong, Yihong 2681 Class-Incremental Learning with Topological Schemas of Memory Spaces  ...  Semi-Parametric Bayesian Survival Rule Lists from Heterogeneous Patient Data DAY 2 -Jan 13, 2021 Track 1: AI, Learning for Classification, and Clustering PS T1.8 Poster Gather Town 4:00 PM 5:  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Cost-Efficient Global Robot Navigation in Rugged Off-Road Terrain

T. Braun
2011 Künstliche Intelligenz  
Thanks to Norbert Schmitz for maintaining and fixing many things in our software framework, Daniel Schmidt for putting up with the chores of teaching and undergraduate education, Jochen Hirth for his work  ...  And finally, I am very thankful for the unwavering support received from my girlfriend Christiane, who tolerated the many late nights at the lab and constantly provided comfort during difficult times when  ...  Similarity of different fingerprints is estimated by applying string matching algorithms originally developed for DNA sequence analysis to the place fingerprints.  ... 
doi:10.1007/s13218-011-0088-9 fatcat:u4ssainmcvc3jmqdon6gpjg674

Knowledge Extracted from Copernicus Satellite Data

Dumitru Octavian, Schwarz Gottfried, Eltoft Torbjørn, Kræmer Thomas, Wagner Penelope, Hughes Nick, Arthus David, Fleming Andrew, Koubarakis Manolis, Datcu Mihai
2019 Zenodo  
By applying an already established active learning approach based on a Support Vector Machine with relevance feedback [2], we can limit ourselves to a limited number of typical satellite images to extract  ...  The proposed methodology uses new paradigms from Recurrent Neural Networks and Generative Adversarial Networks, supported by Bayesian and Information Bottleneck concepts. References 1.  ...  new solutions for a better and safer world [2].  ... 
doi:10.5281/zenodo.3941573 fatcat:zzifwgljifck5bpjnboetsftfu

Speaker comfort and increase of voice level in lecture rooms

Jonas Brunskog, Anders C. Gade, Gaspar Payà Bellester, Lilian Reig Calbo
2008 Journal of the Acoustical Society of America  
The current work identifies acoustic characteristics of reduced 'flaps' and presents phonetic identification data for continua that manipulate these characteristics.  ...  indicate that all three of these characteristics do affect listeners' percept of a consonant, but not sufficiently to completely account for the percept.  ...  Inference and learning in gamma chains for Bayesian audio processing.  ... 
doi:10.1121/1.2934367 fatcat:xr6gp4ldo5bylnxytx2iumrdmi

Fine‐structure processing, frequency selectivity and speech perception in hearing‐impaired listeners

Olaf Strelcyk, Torsten Dau
2008 Journal of the Acoustical Society of America  
The current work identifies acoustic characteristics of reduced 'flaps' and presents phonetic identification data for continua that manipulate these characteristics.  ...  indicate that all three of these characteristics do affect listeners' percept of a consonant, but not sufficiently to completely account for the percept.  ...  Inference and learning in gamma chains for Bayesian audio processing.  ... 
doi:10.1121/1.2935148 fatcat:nqyyia5pubamnhqgonegghrudm

The neural bases of normalising for accented speech: A repetition suppression functional magnetic resonance imaging study

Patti Adank, Peter Hagoort
2008 Journal of the Acoustical Society of America  
The current work identifies acoustic characteristics of reduced 'flaps' and presents phonetic identification data for continua that manipulate these characteristics.  ...  indicate that all three of these characteristics do affect listeners' percept of a consonant, but not sufficiently to completely account for the percept.  ...  Inference and learning in gamma chains for Bayesian audio processing.  ... 
doi:10.1121/1.2934685 fatcat:qqmjcl5gjzcj7kssv2pi6efwti

Patterns and algorithms in high-throughput sequencing count data [article]

Alessandro Mammana, Universitätsbibliothek Der FU Berlin, Universitätsbibliothek Der FU Berlin
2016
This method can integrate measurements for different histone marks and uses a wavelet to detect the count pattern corresponding to positioned nucleosomes.  ...  Our algorithm learns the genomic sequences that attract the transcription factor (the motif) and the count pattern observable at binding sites (the footprint) at once.  ...  I am grateful to Mike Love for his invaluable help with statistics and data analysis, for the many stimulating discussions about bioinformatics, and for convincing me to use the R programming  ... 
doi:10.17169/refubium-16832 fatcat:s4pmzpsofrhslgyj5gqshw2o6q

Chasing the AIDS virus

Thomas Lengauer, André Altmann, Alexander Thielen, Rolf Kaiser
2010 Communications of the ACM  
I thank the reviewers for very detailed and helpful feedback.  ...  All errors and omissions are my own (though of course I faced constraints on length and number of citations).  ...  But the virus changes its genome with practically every copy. The reason for such flexibility is that RT lacks a proofreading mechanism and does not repair copy errors.  ... 
doi:10.1145/1666420.1666440 fatcat:o2qllqh4tzhh5dzgvnjewl52vq

ACNP 58th Annual Meeting: Poster Session III

2019 Neuropsychopharmacology  
excluded and genotype was adjusted for in statistical analysis.  ...  Methods: To assess the effectiveness and safety of MDMAassisted psychotherapy for reducing symptoms of PTSD, a systematic review and meta-analysis was undertaken.  ...  In addition, lithium sarcosine group exhibited delayed neurological deficit and the first changes in stride length occurred at 18 weeks of age, instead of 15 weeks in controls, in the footprint analysis  ... 
doi:10.1038/s41386-019-0547-9 pmid:31801974 pmcid:PMC6957926 fatcat:dd7d43ysfvc5bbbstfl73szya4
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