Hongzhan Huang

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15ranked-venue papers

1as first author

4since 2021 · last 2024

—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 13 · 1 first-author · 2 since 2021Systems, architecture and hardware · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing 9 papers		Bioinformatics and computational biology · 100%
Artificial intelligence 1 paper		Efficient and distributed learning · 100%
Computer graphics and multimedia 1 paper		Image and video processing · 100%
Computer architecture, parallel and distributed computing, and storage systems 1 paper		Hardware accelerators and domain-specific architectures · 100%

Topics — the 15 heaviest of 17, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Bioinformatics and computational biology › genome annotation automatic annotation	0.9	2	2021	UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase · Bioinform. 2021 UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase · Bioinform. 2020
Bioinformatics and computational biology protein function prediction	0.9	2	2021	UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase · Bioinform. 2021 UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase · Bioinform. 2020
Bioinformatics and computational biology › protein analysis › protein bioinformatics protein annotation	0.7	2	2023	Annotation of biologically relevant ligands in UniProtKB using ChEBI · Bioinform. 2023 PIR: a new resource for bioinformatics · Bioinform. 2000
Machine learning › Efficient and distributed learning model compression	0.7	1	2023	Hardware-friendly Scalable Image Super Resolution with Progressive Structured Sparsity · ACM Multimedia 2023
Machine learning › Efficient and distributed learning › model compression › pruning structured pruning	0.7	1	2023	Hardware-friendly Scalable Image Super Resolution with Progressive Structured Sparsity · ACM Multimedia 2023
Image and video processing › super-resolution image super-resolution	0.7	1	2023	Hardware-friendly Scalable Image Super Resolution with Progressive Structured Sparsity · ACM Multimedia 2023
Bioinformatics and computational biology › genome annotation functional annotation	0.2	2	2015	UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches · Bioinform. 2015 PIR: a new resource for bioinformatics · Bioinform. 2000
Bioinformatics and computational biology › sequence analysis sequence similarity search	0.2	2	2015	UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches · Bioinform. 2015 PIR: a new resource for bioinformatics · Bioinform. 2000
Bioinformatics and computational biology proteomics	0.2	1	2013	A fast Peptide Match service for UniProt Knowledgebase · Bioinform. 2013
Bioinformatics and computational biology identifier mapping	0.1	1	2011	A comprehensive protein-centric ID mapping service for molecular data integration · Bioinform. 2011
Bioinformatics and computational biology › protein sequence analysis protein sequence annotation	0.1	1	2016	Computational clustering for viral reference proteomes · Bioinform. 2016
Bioinformatics and computational biology › protein sequence analysis protein sequence clustering	0.1	1	2007	UniRef: comprehensive and non-redundant UniProt reference clusters · Bioinform. 2007
Bioinformatics and computational biology › data integration database integration	0.0	1	2011	A comprehensive protein-centric ID mapping service for molecular data integration · Bioinform. 2011
Bioinformatics and computational biology › sequence analysis › sequence similarity search sequence database search	0.0	1	2000	PIR: a new resource for bioinformatics · Bioinform. 2000
Bioinformatics and computational biology › sequence analysis sequence database redundancy reduction	0.0	1	2007	UniRef: comprehensive and non-redundant UniProt reference clusters · Bioinform. 2007

Methods — techniques the papers use, named apart from their topics

structured iterative sparsity · 2.0knowledge distillation · 2.0interpro signatures · 0.9ontology-based annotation · 0.7SPARQL querying · 0.7BLASTP · 0.5sequence similarity clustering · 0.2bootstrap analysis · 0.2text search engine · 0.2web services · 0.1sequence identity clustering · 0.1

Year	Publication	Venue	Position
2024	Data-Free Learning for Lightweight Multi-Weather Image Restoration abstract Image restoration has made a remarkable performance with the large-scale training data and increasing model capacity. However, the burdensome model complexity hinders the mode deployment on resource-constrained devices. Besides, the training data may be unavailable due to some constraints, which undoubtedly affects the efficient model learning. In this paper, we propose an effective data-free model compression framework for lightweight multi-weather image restoration, which consists of data generation and model distillation stages. Specifically, a data generator is first utilized to synthesize degradation-aware samples from a latent distribution. Then, the on-the-shelf teacher model provides a pseudo-label to supervise the training of the student model. To ensure the diversity of the training data, adversarial learning is adopted to maximize the dependency between teacher and student models. Moreover, we adopt a contrastive regularization constraint to further improve model representation. Experimental results show that our proposal achieves comparable performance with the student model trained with the original data and some unsupervised methods for image dehazing and deraining tasks. Hongzhan Huang, Xiaotong Luo, Yanyun Qu	ISCAS	2
2023	Hardware-friendly Scalable Image Super Resolution with Progressive Structured Sparsity abstract Single image super-resolution (SR) is an important low-level vision task, and the dynamic SR trading off performance and efficiency are increasingly in demand. The existing dynamic SR methods are divided into two classes: the structured pruning and non-structured compressing methods. The former removes redundant structures in the network, which often leads to significant performance degradation, and the latter searches for extremely sparse parameter masks, achieving promising performance, but they are not deployable in hardware platforms with irregular memory access. In order to solve the mentioned problems, we propose Hardware-friendly Scalable SR (HSSR) with progressively structured sparsity. The superiority of our method is that with only a single scalable model it covers multiple SR models with different sizes, without extra retraining or post-processing. HSSR contains the forward and backward processing. In the forward process, we gradually shrink the SR networks with structured iterative sparsity where grouping convolution together with knowledge distillation is conducted to reduce the amount of SR parameters and the computational complexity while keeping the performance, and in the backward process, we gradually expand the compressed SR networks with structured iterative recovery. Comprehensive experiments on benchmark datasets show that HSSR is perfectly compatible with common convolution baselines. Compared with the Slimmable method, our model is superior in performance, flops, and model size. Experimental results demonstrate that HSSR achieves significant compression, saving up to 1500K parameters and 100 GFlops calculation compared to the original model in real-world applications. Fangchen Ye, Hongzhan Huang, Jianping Fan 0007, Zhongchao Shi, Yuan Xie 0006, Yanyun Qu	ACM Multimedia	3
2023	Annotation of biologically relevant ligands in UniProtKB using ChEBI abstract MOTIVATION: To provide high quality, computationally tractable annotation of binding sites for biologically relevant (cognate) ligands in UniProtKB using the chemical ontology ChEBI (Chemical Entities of Biological Interest), to better support efforts to study and predict functionally relevant interactions between protein sequences and structures and small molecule ligands. RESULTS: We structured the data model for cognate ligand binding site annotations in UniProtKB and performed a complete reannotation of all cognate ligand binding sites using stable unique identifiers from ChEBI, which we now use as the reference vocabulary for all such annotations. We developed improved search and query facilities for cognate ligands in the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that ChEBI provides. AVAILABILITY AND IMPLEMENTATION: Binding site annotations for cognate ligands described using ChEBI are available for UniProtKB protein sequence records in several formats (text, XML and RDF) and are freely available to query and download through the UniProt website (www.uniprot.org), REST API (www.uniprot.org/help/api), SPARQL endpoint (sparql.uniprot.org/) and FTP site (https://ftp.uniprot.org/pub/databases/uniprot/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Elisabeth Coudert, Sebastien Gehant, Edouard De Castro, Monica Pozzato, Delphine Baratin, Teresa Batista Neto, Christian J. A. Sigrist, Nicole Redaschi, Alan J. Bridge, Lucila Aimo, Ghislaine Argoud-Puy, Andrea H. Auchincloss, Kristian B. Axelsen, Parit Bansal, Marie-Claude Blatter, Jerven T. Bolleman, Emmanuel Boutet, Lionel Breuza, Blanca Cabrera Gil, Cristina Casals-Casas, Kamal Chikh Echioukh, Béatrice A. Cuche, Anne Estreicher, Maria Livia Famiglietti, Marc Feuermann, Elisabeth Gasteiger, Pascale Gaudet, Vivienne Baillie Gerritsen, Arnaud Gos, Nadine Gruaz-Gumowski, Chantal Hulo, Nevila Hyka-Nouspikel, Florence Jungo, Arnaud Kerhornou, Philippe Le Mercier, Damien Lieberherr, Patrick Masson, Anne Morgat, Venkatesh Muthukrishnan, Salvo Paesano, Ivo Pedruzzi, Sandrine Pilbout, Lucille Pourcel, Sylvain Poux, Manuela Pruess, Catherine Rivoire, Karin Sonesson, Shyamala Sundaram, Alex Bateman, Maria Jesus Martin, Sandra E. Orchard, Michele Magrane, Shadab Ahmad, Emanuele Alpi, Emily H. Bowler-Barnett, Ramona Britto, Hema Bye-A-Jee, Austra Cukura, Paul Denny 0002, Tunca Dogan, Thankgod Ebenezer, Penelope Garmiri, Leonardo Jose da Costa Gonzales, Emma Hatton-Ellis, Abdulrahman Hussein, Alexandr Ignatchenko, Giuseppe Insana, Rizwan Ishtiaq, Vishal Joshi, Dushyanth Jyothi, Swaathi Kandasamy, Antonia Lock, Aurelien Luciani, Marija Lugaric, Yvonne Lussi, Alistair MacDougall, Fábio Madeira, Mahdi Mahmoudy, Alok Mishra 0004, Katie Moulang, Andrew Nightingale, Sangya Pundir, Guoying Qi, Shriya Raj, Pedro Raposo, Daniel Rice, Rabie Saidi, Elena Speretta, James D. Stephenson, Prabhat Totoo, Edward Turner, Nidhi Tyagi, Preethi Vasudev, Kate Warner, Xavier Watkins, Rossana Zaru, Hermann Zellner, Cathy H. Wu, Cecilia N. Arighi, Leslie Arminski, Chuming Chen, Yongxing Chen, Hongzhan Huang, Kati Laiho, Peter B. McGarvey, Darren A. Natale, Karen E. Ross, C. R. Vinayaka, Qinghua Wang 0003	Bioinform.	106
2021	UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase Alistair MacDougall, Vladimir Volynkin, Rabie Saidi, Diego Poggioli, Hermann Zellner, Emma Hatton-Ellis, Vishal Joshi, Claire O'Donovan, Sandra E. Orchard, Andrea H. Auchincloss, Delphine Baratin, Jerven T. Bolleman, Elisabeth Coudert, Edouard De Castro, Chantal Hulo, Patrick Masson, Ivo Pedruzzi, Catherine Rivoire, Cecilia N. Arighi, Qinghua Wang 0003, Chuming Chen, Hongzhan Huang, John S. Garavelli, C. R. Vinayaka, Lai-Su Yeh, Darren A. Natale, Kati Laiho, Maria Jesus Martin, Alexandre Renaux, Klemens Pichler	Bioinform.	22
2020	UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase abstract MOTIVATION: The number of protein records in the UniProt Knowledgebase (UniProtKB: https://www.uniprot.org) continues to grow rapidly as a result of genome sequencing and the prediction of protein-coding genes. Providing functional annotation for these proteins presents a significant and continuing challenge. RESULTS: In response to this challenge, UniProt has developed a method of annotation, known as UniRule, based on expertly curated rules, which integrates related systems (RuleBase, HAMAP, PIRSR, PIRNR) developed by the members of the UniProt consortium. UniRule uses protein family signatures from InterPro, combined with taxonomic and other constraints, to select sets of reviewed proteins which have common functional properties supported by experimental evidence. This annotation is propagated to unreviewed records in UniProtKB that meet the same selection criteria, most of which do not have (and are never likely to have) experimentally verified functional annotation. Release 2020_01 of UniProtKB contains 6496 UniRule rules which provide annotation for 53 million proteins, accounting for 30% of the 178 million records in UniProtKB. UniRule provides scalable enrichment of annotation in UniProtKB. AVAILABILITY AND IMPLEMENTATION: UniRule rules are integrated into UniProtKB and can be viewed at https://www.uniprot.org/unirule/. UniRule rules and the code required to run the rules, are publicly available for researchers who wish to annotate their own sequences. The implementation used to run the rules is known as UniFIRE and is available at https://gitlab.ebi.ac.uk/uniprot-public/unifire. Alistair MacDougall, Vladimir Volynkin, Rabie Saidi, Diego Poggioli, Hermann Zellner, Emma Hatton-Ellis, Vishal Joshi, Claire O'Donovan, Sandra E. Orchard, Andrea H. Auchincloss, Delphine Baratin, Jerven T. Bolleman, Elisabeth Coudert, Edouard De Castro, Chantal Hulo, Patrick Masson, Ivo Pedruzzi, Catherine Rivoire, Cecilia N. Arighi, Qinghua Wang 0003, Chuming Chen, Hongzhan Huang, John S. Garavelli, C. R. Vinayaka, Lai-Su Yeh, Darren A. Natale, Kati Laiho, Maria Jesus Martin, Alexandre Renaux, Klemens Pichler	Bioinform.	22
2016	Computational clustering for viral reference proteomes abstract MOTIVATION: The enormous number of redundant sequenced genomes has hindered efforts to analyze and functionally annotate proteins. As the taxonomy of viruses is not uniformly defined, viral proteomes pose special challenges in this regard. Grouping viruses based on the similarity of their proteins at proteome scale can normalize against potential taxonomic nomenclature anomalies. RESULTS: We present Viral Reference Proteomes (Viral RPs), which are computed from complete virus proteomes within UniProtKB. Viral RPs based on 95, 75, 55, 35 and 15% co-membership in proteome similarity based clusters are provided. Comparison of our computational Viral RPs with UniProt's curator-selected Reference Proteomes indicates that the two sets are consistent and complementary. Furthermore, each Viral RP represents a cluster of virus proteomes that was consistent with virus or host taxonomy. We provide BLASTP search and FTP download of Viral RP protein sequences, and a browser to facilitate the visualization of Viral RPs. AVAILABILITY AND IMPLEMENTATION: http://proteininformationresource.org/rps/viruses/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Chuming Chen, Hongzhan Huang, Raja Mazumder, Darren A. Natale, Peter B. McGarvey, Jian Zhang 0051, Shawn W. Polson, Cathy H. Wu	Bioinform.	2
2015	UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches abstract Abstract Motivation: UniRef databases provide full-scale clustering of UniProtKB sequences and are utilized for a broad range of applications, particularly similarity-based functional annotation. Non-redundancy and intra-cluster homogeneity in UniRef were recently improved by adding a sequence length overlap threshold. Our hypothesis is that these improvements would enhance the speed and sensitivity of similarity searches and improve the consistency of annotation within clusters. Results: Intra-cluster molecular function consistency was examined by analysis of Gene Ontology terms. Results show that UniRef clusters bring together proteins of identical molecular function in more than 97% of the clusters, implying that clusters are useful for annotation and can also be used to detect annotation inconsistencies. To examine coverage in similarity results, BLASTP searches against UniRef50 followed by expansion of the hit lists with cluster members demonstrated advantages compared with searches against UniProtKB sequences; the searches are concise (∼7 times shorter hit list before expansion), faster (∼6 times) and more sensitive in detection of remote similarities (>96% recall at e-value <0.0001). Our results support the use of UniRef clusters as a comprehensive and scalable alternative to native sequence databases for similarity searches and reinforces its reliability for use in functional annotation. Availability and implementation: Web access and file download from UniProt website at http://www.uniprot.org/uniref and ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. BLAST searches against UniRef are available at http://www.uniprot.org/blast/ Contact: [email protected] Baris E. Suzek, Hongzhan Huang, Peter B. McGarvey, Cathy H. Wu	Bioinform.	3
2013	An Integrated Approach for Interpretation of Clinical NGS Genomic Variant Data Erin L. Crowgey, Chuming Chen, Shawn W. Polson, Hongzhan Huang, Cathy H. Wu	AMIA	4
2013	A fast Peptide Match service for UniProt Knowledgebase abstract SUMMARY: We have developed a new web application for peptide matching using Apache Lucene-based search engine. The Peptide Match service is designed to quickly retrieve all occurrences of a given query peptide from UniProt Knowledgebase (UniProtKB) with isoforms. The matched proteins are shown in summary tables with rich annotations, including matched sequence region(s) and links to corresponding proteins in a number of proteomic/peptide spectral databases. The results are grouped by taxonomy and can be browsed by organism, taxonomic group or taxonomy tree. The service supports queries where isobaric leucine and isoleucine are treated equivalent, and an option for searching UniRef100 representative sequences, as well as dynamic queries to major proteomic databases. In addition to the web interface, we also provide RESTful web services. The underlying data are updated every 4 weeks in accordance with the UniProt releases. AVAILABILITY: http://proteininformationresource.org/peptide.shtml. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Chuming Chen, Hongzhan Huang, Baris E. Suzek, Cathy H. Wu	Bioinform.	3
2012	Building a classifier for identifying sentences pertaining to disease-drug relationships in tardive dyskinesia abstract In this paper, we attempt to build a pipeline that identifies and extracts disease-drug relationships via sentence classification, and demonstrate the feasibility and utility of our approach using tardive dyskinesia as a case study. We manually developed and annotated a biomedicai training corpus for tardive dyskinesia. Using 10-fold cross validation, we tested and trained a naïve Bayes classifier to identify sentences pertaining to disease-drug relationships. Our precision, recall, and F-measure were all approximately 66%, and area under the ROC curve was over 80%. Our method helps to elucidate various drug effects on tardive dyskinesia and constitutes an initial effort toward the task of disease-drug relationship extraction. Xia Bi, Hongzhan Huang, Sherri Matis-Mitchell, Peter B. McGarvey, Manabu Torii, Hagit Shatkay, Cathy H. Wu	BIBM	2
2012	Robust segmentation of biomedical figures for image-based document retrieval abstract Figures play an important role in illustrating concepts, methodology and results in biomedicai literature. However, figures in biomedicai literature are often composed of multiple subfigures (panels), which may illustrate diverse methodologies or results. Robust and accurate panel partitioning is crucial to support article categorization based on methods or experimental results and to provide the evidence source for derived assertions. But, it is a challenging task. In this paper, we present a comprehensive framework for harvesting multimodal panels in biomedicai literature, and demonstrate its application to protein-protein interaction (PPI)-related literature as a use case. A unique feature of our solution is that we combine pixel-level representations of images with figure captions. Our approach first analyzes figure captions to identify the label style used to mark panels. We then use pixel-level representations to partition a figure into a set of bounding boxes of connected components. We also perform a lexical analysis on the text within the figure to locate panel labels that match the caption analysis results. Finally, we estimate the optimal panel layout and use the layout to partition the figure. We tested our system on a dataset provided by the Molecular INTeraction database (MINT), and show that our approach surpasses pure caption-based and pure image-based approaches, achieving a 96.64% precision. Luis D. Lopez, Jingyi Yu 0001, Catalina O. Tudor, Cecilia N. Arighi, Hongzhan Huang, K. Vijay-Shanker, Cathy H. Wu	BIBM	5
2011	An Automatic System for Extracting Figures and Captions in Biomedical PDF Documents abstract Figures in biomedical articles often constitute direct evidence of experimental results. Image analysis methods can be coupled with text-based methods to improve knowledge discovery. However, automatically harvesting figures along with their associated captions from full-text articles remains challenging. In this paper, we present an automatic system for robustly harvesting figures from biomedical literature. Our approach relies on the idea that the PDF specification of the document layout can be used to identify encoded figures and figure boundaries within the PDF and enforce constraints among figure-regions. This allows us to harvest fragments of figures (subflgures), from the PDF, correctly identify subfigures that belong to the same figure, and identify the captions associated with each figure. Our method simultaneously recovers figures and captions and applies additional filtering process to remove irrelevant figures such as logos, to eliminate text passages that were incorrectly identified as captions, and to re-group subflgures to generate a putative figure. Finally, we associate figures with captions. Our preliminary experiments suggest that our method achieves an accuracy of 95% in harvesting figures-caption pairs from a set of 2,035 full-text biomedical documents from BioCreative III, containing 12,574 figures. Luis D. Lopez, Jingyi Yu 0001, Cecilia N. Arighi, Hongzhan Huang, Hagit Shatkay, Cathy H. Wu	BIBM	4
2011	A comprehensive protein-centric ID mapping service for molecular data integration abstract MOTIVATION: Identifier (ID) mapping establishes links between various biological databases and is an essential first step for molecular data integration and functional annotation. ID mapping allows diverse molecular data on genes and proteins to be combined and mapped to functional pathways and ontologies. We have developed comprehensive protein-centric ID mapping services providing mappings for 90 IDs derived from databases on genes, proteins, pathways, diseases, structures, protein families, protein interaction, literature, ontologies, etc. The services are widely used and have been regularly updated since 2006. AVAILABILITY: www.uniprot.org/mappingandproteininformation-resource.org/pirwww/search/idmapping.shtml CONTACT: [email protected]. Hongzhan Huang, Peter B. McGarvey, Baris E. Suzek, Raja Mazumder, Jian Zhang 0051, Yongxing Chen, Cathy H. Wu	Bioinform.	1
2007	UniRef: comprehensive and non-redundant UniProt reference clusters abstract MOTIVATION: Redundant protein sequences in biological databases hinder sequence similarity searches and make interpretation of search results difficult. Clustering of protein sequence space based on sequence similarity helps organize all sequences into manageable datasets and reduces sampling bias and overrepresentation of sequences. RESULTS: The UniRef (UniProt Reference Clusters) provide clustered sets of sequences from the UniProt Knowledgebase (UniProtKB) and selected UniProt Archive records to obtain complete coverage of sequence space at several resolutions while hiding redundant sequences. Currently covering >4 million source sequences, the UniRef100 database combines identical sequences and subfragments from any source organism into a single UniRef entry. UniRef90 and UniRef50 are built by clustering UniRef100 sequences at the 90 or 50% sequence identity levels. UniRef100, UniRef90 and UniRef50 yield a database size reduction of approximately 10, 40 and 70%, respectively, from the source sequence set. The reduced redundancy increases the speed of similarity searches and improves detection of distant relationships. UniRef entries contain summary cluster and membership information, including the sequence of a representative protein, member count and common taxonomy of the cluster, the accession numbers of all the merged entries and links to rich functional annotation in UniProtKB to facilitate biological discovery. UniRef has already been applied to broad research areas ranging from genome annotation to proteomics data analysis. AVAILABILITY: UniRef is updated biweekly and is available for online search and retrieval at http://www.uniprot.org, as well as for download at ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Baris E. Suzek, Hongzhan Huang, Peter B. McGarvey, Raja Mazumder, Cathy H. Wu	Bioinform.	2
2000	PIR: a new resource for bioinformatics abstract UNLABELLED: The Protein Information Resource (PIR) has greatly expanded its Web site and developed a set of interactive search and analysis tools to facilitate the analysis, annotation, and functional identification of proteins. New search engines have been implemented to combine sequence similarity search results with database annotation information. The new PIR search systems have proved very useful in providing enriched functional annotation of protein sequences, determining protein superfamily-domain relationships, and detecting annotation errors in genomic database archives. AVAILABILITY: http://pir.georgetown.edu/. CONTACT: [email protected] Peter B. McGarvey, Hongzhan Huang, Winona C. Barker, Bruce C. Orcutt, John S. Garavelli, Geetha Y. Srinivasarao, Lai-Su L. Yeh, Chunlin Xiao, Cathy H. Wu	Bioinform.	2