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Displaying 1-17 out of 17 total
Classification and clustering in metagenomics with unified data management and computational framework
Found in: 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)
By Zeehasham Rasheed,Huzefa Rangwala,Patrick Gillevet
Issue Date:October 2012
pp. 965-967
The new generation of genomie technologies have allowed researchers to determine the collective DNA of organisms co-existing as communities across different environments. There is a need for the computational approaches to analyze and annotate the large vo...
 
Guest Editorial for Special Section on BIOKDD2013
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Gaurav Pandey,Huzefa Rangwala
Issue Date:September 2014
pp. 773-774
The four articles in this special section were presented at the 2013 International Workshop on Data Mining in Bioinformatics (BIOKDD) which was held in conjunction with the ACM Conference on Knowledge Discovery and Data Mining (KDD) in Chicago, IL.
 
A Map-Reduce Framework for Clustering Metagenomes
Found in: 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW)
By Zeehasham Rasheed,Huzefa Rangwala
Issue Date:May 2013
pp. 549-558
The past few years has seen an explosion in the use of sequence technologies for met genomics i.e., determination of the collective genome of microorganisms co-existing within several environments. In parallel, there has been rapid development of computati...
 
Erratum to “Protein Function Prediction Using Multilabel Ensemble Classification”
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Guoxian Yu,Huzefa Rangwala,Carlotta Domeniconi,Guoji Zhang,Zhiwen Yu
Issue Date:January 2014
pp. 265
No summary available.
 
Classifying Documents within Multiple Hierarchical Datasets Using Multi-task Learning
Found in: 2013 IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI)
By Azad Naik,Anveshi Charuvaka,Huzefa Rangwala
Issue Date:November 2013
pp. 390-397
Multi-task learning (MTL) is a supervised learning paradigm in which the prediction models for several related tasks are learned jointly to achieve better generalization performance. When there are only a few training examples per task, MTL considerably ou...
 
Protein Function Prediction Using Multilabel Ensemble Classification
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Guoxian Yu,Huzefa Rangwala,Carlotta Domeniconi, Guoji Zhang, Zhiwen Yu
Issue Date:July 2013
pp. 1045-1057
High-throughput experimental techniques produce several kinds of heterogeneous proteomic and genomic data sets. To computationally annotate proteins, it is necessary and promising to integrate these heterogeneous data sources. Some methods transform these ...
 
Multi-task Learning for Classifying Proteins Using Dual Hierarchies
Found in: 2012 IEEE 12th International Conference on Data Mining (ICDM)
By Anveshi Charuvaka,Huzefa Rangwala
Issue Date:December 2012
pp. 834-839
Several biological databases organize information in taxonomies/hierarchies. These databases differ in terms of curation process, input data, coverage and annotation errors. SCOP and CATH are examples of two databases that classify proteins hierarchically ...
 
LSH-Div: Species diversity estimation using locality sensitive hashing
Found in: 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
By Zeehasham Rasheed,Huzefa Rangwala,Daniel Barbara
Issue Date:October 2012
pp. 1-6
Metagenome sequencing projects attempt to determine the collective DNA of organisms, co-existing as communities across different environments. Computational approaches analyze the large volumes of sequence data obtained from these ecological samples, to pr...
 
Analysis of Microbiome Data across Inflammatory Bowel Disease Patients
Found in: Machine Learning and Applications, Fourth International Conference on
By Nuttachat Wisittipanit,Huzefa Rangwala,Patrick Gillevet
Issue Date:December 2011
pp. 200-205
The interaction and inter-play of microbes with human host cells is responsible for several disease conditions and of criticality to human health. In this study we analyze the microbial communities within the human gut and their roles in Inflammatory Bowel...
 
GPU-Euler: Sequence Assembly Using GPGPU
Found in: High Performance Computing and Communications, 10th IEEE International Conference on
By Syed Faraz Mahmood,Huzefa Rangwala
Issue Date:September 2011
pp. 153-160
Advances in sequencing technologies have revolutionized the field of genomics by providing cost effective and high throughput solutions. In this paper, we develop a parallel sequence assembler implemented on general purpose graphic processor units (GPUs). ...
 
Predicting Network Response Times Using Social Information
Found in: Social Network Analysis and Mining, International Conference on Advances in
By Chen Liang, Sharath Hiremagalore, Angelos Stavrou, Huzefa Rangwala
Issue Date:July 2011
pp. 527-531
Social networks and discussion boards have become a significant outlet where people communicate and express their opinion freely. Although the social networks themselves are usually well-provisioned, the participating users frequently point to external lin...
 
Defining a Coparticipation Network Using Comments on Digg
Found in: IEEE Intelligent Systems
By Huzefa Rangwala, Salman Jamali
Issue Date:July 2010
pp. 36-45
<p>Using comment information available from Digg, the authors define a coparticipation network between users that lets them study users' behavioral characteristics and predict the popularity of online content.</p>
 
Genome Alignments Using MPI-LAGAN
Found in: Bioinformatics and Biomedicine, IEEE International Conference on
By Ruinan Zhang, Huzefa Rangwala, George Karypis
Issue Date:November 2008
pp. 437-440
We develop a parallel algorithm for a widely used whole genome alignment method called LAGAN. We use the MPI-based protocol to develop parallel solutions for two phases of the algorithm which take up a significant portion of the total runtime, and also hav...
 
Classifying protein sequences using regularized multi-task learning
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Anveshi Charuvaka,Huzefa Rangwala
Issue Date:July 2014
pp. 1
Classification problems in which several learning tasks are organized hierarchically pose a special challenge because the hierarchical structure of the problems needs to be considered. Multi-task learning (MTL) provides a framework for dealing with such in...
 
Protein Function Prediction withIncomplete Annotations
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Guoxian Yu,Huzefa Rangwala,Carlotta Domeniconi,Guoji Zhang,Zhiwen Yu
Issue Date:May 2014
pp. 579-591
Automated protein function prediction is one of the grand challenges in computational biology. Multi-label learning is widely used to predict functions of proteins. Most of multi-label learning methods make prediction for unlabeled proteins under the assum...
 
Protein Function Prediction using Multi-label Ensemble Classification
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Carlotta Domeniconi, Guoji Zhang, Guoxian Yu, Huzefa Rangwala, Zhiwen Yu
Issue Date:July 2013
pp. 1-1
High-throughput experimental techniques produce several heterogeneous proteomic and genomic datasets. To computationally annotate proteins, it is necessary and promising to integrate these heterogeneous data sources. Some methods transform these data sourc...
     
Transductive multi-label ensemble classification for protein function prediction
Found in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12)
By Carlotta Domeniconi, Guoji Zhang, Guoxian Yu, Huzefa Rangwala, Zhiwen Yu
Issue Date:August 2012
pp. 1077-1085
Advances in biotechnology have made available multitudes of heterogeneous proteomic and genomic data. Integrating these heterogeneous data sources, to automatically infer the function of proteins, is a fundamental challenge in computational biology. Severa...
     
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