PLAYPEN: Plug-and-Play Visual Graph Query Interfaces for Top-down and Bottom-Up Search on Large Networks

In the Proceedings of 48th ACM SIGMOD International Conference on Management of Data (ACM SIGMOD 2022)

ACM Press, Philadelphia, USA (Demo).

LANTERN: Boredom-conscious Natural Language Description Generation of Query Execution Plans for Database Education

In the Proceedings of 48th ACM SIGMOD International Conference on Management of Data (ACM SIGMOD 2022)

ACM Press, Philadelphia, USA (Demo).

Towards Plug-and-Play Visual Graph Query Interfaces: Data-driven Canned Pattern Selection for Large Networks

In Proceedings of 47th International Conference on Very Large Data Bases (VLDB 2021)

ACM Press, Copenhagen, Denmark.

Technical Report

Video

This work exploits the notion of cognitive load from cognitive psychology to generate canned patterns from large network that are easy to visually intepret by humans to enhance usability of data-driven visual graph query interfaces.

MIDAS: Towards Efficient and Effective Maintenance of Canned Patterns in Visual Graph Query Interfaces

In the Proceedings of 47th ACM SIGMOD International Conference on Management of Data (ACM SIGMOD 2021)

ACM Press, Xi'an, China.

Technical Report

Code

This work builds on CATAPULT by exploiting the notion of cognitive load from cognitive psychology to maintain canned patterns in a data-driven visual graph query interface as the underlying graph repository evolves.

Towards Enhancing Database Education: Natural Language Generation Meets Query Execution Plans

In the Proceedings of 47th ACM SIGMOD International Conference on Management of Data (ACM SIGMOD 2021)

ACM Press, Xi'an, China.

Technical Report

Video

This paper presents a AI-based framework that for the first time exploits psychology theories on habituation and boredom to generate natural language (NL) description of query execution plans (QEP) of SQL queries to facilitate database education. This facilitates diversification of NL description of QEPs.

CHASSIS: Conformity Meets Online Information Diffusion

In the Proceedings of 46th ACM SIGMOD International Conference on Management of Data (ACM SIGMOD 2020)

ACM Press, Portland, USA.

Technical Report

Talk

CHASSIS marries a social psychology theory (conformity) with online information diffusion model for superior predictive power.

AURORA: Data-driven Construction of Visual Graph Query Interfaces for Graph Databases

In the Proceedings of 46th ACM SIGMOD International Conference on Management of Data (ACM SIGMOD 2020)

ACM Press, Portland, USA (Demo).

Video

AURORA is a demonstration of a data-driven visual graph query interface whose components are grounded on the principles of cognitive psychology. Specifically, it exploits CATAPULT for displaying cognitive load-conscious canned patterns on the GUI.

CATAPULT: Data-driven Selection of Canned Patterns for Efficient Visual Graph Query Formulation

In the Proceedings of 45th ACM SIGMOD International Conference on Management of Data (ACM SIGMOD 2019)

ACM Press, Amsterdam, The Netherlands.

Code

This is the first work that exploits the notion of cognitive load from cognitive psychology for selecting canned patterns in a data-driven visual graph query interface from a large collection of small- or medium-sized data graphs. The idea is to maintain the existing patterns with those which are visually easy to intepret by humans (i.e., low cognitive load).

Interruption-Sensitive Empty Result Feedback: Rethinking the Visual Query Feedback Paradigm for Semistructured Data

In the Proceedings of 24th ACM International Conference on Information and Knowledge Management (CIKM 2015)

ACM Press, Melbourne, Australia

This paper integrates the impact of interruption, a concept well-studied in cognitive psychology, with query feedback in the context of semistructured data. In particular, the adverse effect of interruption during query processing has not be investigated in data management prior to this work.

Conformity-aware Influence Maximization in Online Social Networks

The VLDB Journal, 24(1), Springer-Verlag.

This paper is an extension of CINEMA and integrates the notion of conformity from social psychology into online influence maximization problem.

CINEMA: Conformity-Aware Greedy Algorithm for Influence Maximization in Online Social Networks

In the Proceedings of 16th International Conference on Extending Database Technology (EDBT 2013)

ACM Press, Genoa, Italy

This paper is the first work that integrates the notion of conformity from social psychology into online influence maximization problem leading to superior influence spread prediction.

CASINO: Towards Conformity-Aware Social Influence Analysis in Online Social Networks

In the Proceedings of 20th ACM Conference of Information and Knowledge Management (ACM CIKM 2011)

ACM Press, Glasgow, Scotland (Short)

Extended version

This is the first work that marries conformity from social pyschology with online social influence.

ArcheGEO: Towards Improving Relevance of Gene Expression Omnibus Search Results

In the Proceedings of 13th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM BCB 2022)

ACM Press, Chicago, USA

Code

ArcheGEO is an end-to-end framework which is targeted towards users of the Gene Expression Omnibus (GEO) repository, and delivers value by separating relevant and irrelevant matches in results of a keyword query, thereby improving access to relevant biomedical information. Reasons for irrelevance are reported and such details can be used to correct misannotations or to include missing annotations of gene expression records in the GEO repository to improve search results relevance.

TROVE: A User-friendly Tool for Visualizing and Analyzing Cancer Hallmarks in Signaling Networks

Bioinformatics, 34(2), Oxford Univ Press.

Video

A software framework for visualizing and analyzing cancer hallmarks in cancer-related signaling neworks.

Synergistic Target Combination Prediction From Curated Signaling Networks: Machine Learning Meets Systems Biology and Pharmacology

Methods, Vol 129, Elsevier Science (Invited Paper)

One of the early work that is at the intersection of Machine Learning, Systems Biology, and Pharmacology for predicting synergistic target combinations from curated disease-related signaling networks to advance combination therapy.

TINTIN: Exploiting Target Features for Signaling Network Similarity Computation and Ranking

In the Proceedings of 8th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM BCB 2017)

ACM Press, Boston, USA

This work is one of the first that aims to rank signaling networks by exploiting target characteristics.

Springer Verlag, ISBN 978-3-319-54620-9, Pages: 146.

A book that consolidates all major efforts related to summarizing biological networks. It show cases works and opportunities that lie at the intersection of network analytics and biological networks.

TENET: A Machine Learning-based System for Target Characterization in Signaling Networks

In the Proceedings of 16th IEEE Conference on Data Mining (IEEE ICDM 2016)

IEEE CS, Barcelona, Spain (Demo)

Software prototype of ML-based target characterization in signaling networks.

FacetsViewer: A Tool for Multi-faceted Decomposition of Complex Networks

In the Proceedings of 16th IEEE Conference on Data Mining (IEEE ICDM 2016)

IEEE CS, Barcelona, Spain (Demo)

Video

Software prototype of FACET, a mult-faceted decomposition technique of biological networks.

TAPESTRY: Network-centric Target Prioritization in Disease-related Signaling Networks

In the Proceedings of 7th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM BCB 2016)

ACM Press, Seattle, USA

Software

TAPESTRY exploits data analytics to prioritize targets in disease-related signaling networks. This is an improved version of our prior work (PANI).

Predictive Modeling of Drug Effects on Signaling Pathways in Diverse Cancer Cell Lines

In the Proceedings of 7th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM BCB 2016)

ACM Press, Seattle, USA

The paper presents a computational model for predicting effects of drugs on cancer-related signaling networks.

Clustering and Summarizing Protein-Protein Interaction Networks: A Survey

IEEE Transactions on Data and Knowledge Engineering (TKDE), 28(3), IEEE CS

A survey paper that exhaustively reviews work on clustering and summarizing PPI networks.

TENET: Topological Feature-based Target Characterization in Signaling Networks

Bioinformatics, 31(20), Oxford Univ Press.

Technical Report

Software

A software that exploits machine learning to characterize topological features of nodes in signaling networks.

Computational Cell Fate Modelling for Discovery of Rewiring in Apoptotic Network for Enhanced Cancer Drug Sensitivity

BMC Systems Biology, 9:S4, Biomed Central.

The paper presents a computational model for discovering rewiring in apoptotic network, which plays an important role in cancer.

DiffNet: Automatic Differential Functional Summarization of dE-MAP Networks

Methods (IF 4.15), 69, Elsevier Science (Invited Paper)

This work takes a data-driven approach to find differential functional summaries in a differential network from two static gene interaction networks in order to map the interaction differences between them under environment or condition change.

One Feature Doesn’t Fit All: Characterizing Topological Features of Targets in Signaling Networks

In the Proceedings of 5th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM BCB 2014)

ACM Press, Newport Beach, California, USA

This work exploits machine learning to characterize topological features of nodes in signaling networks.

DualAligner: A Dual Alignment-based Strategy to Align Protein Interaction Networks

Bioinformatics (IF 5.32), 30(18), Oxford Univ Press

Software

This is a novel biological network alignment technique that exploits ideas from data management search to generate more efficient but high quality alignment.

INGOT: Towards Network-driven In Silico Combination Therapy

In the Proceedings of 1st International Conference on Big Data and Smart Computing (BigComp 2014)

IEEE CS, Bangkok, Thailand (Invited Paper)

A vision paper that lays down the vision of opportunities and challenges for in silico target combination prediction for combination therapy.

FACETS: Multi-faceted Functional Decomposition of Protein Interaction Networks

Bioinformatics (IF 5.47), 28(20), Oxford Univ Press

Software

Network summarization techniques in computer science typically generate one summary for a given input. This work presents an algorithm called FACET to generate multi-faceted functional summaries of PPI networks, which is more meaningful in biology.

STEROID: In Silico Heuristic Target Combination Identification for Disease-Related Signaling Networks

In the Proceedings of 3rd ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB 2012)

ACM Press, Orlando, Florida, USA

Extended version

One of the early computational efforts to predict target combinations in disease-related signaling networks for combination therapy.

Plasmin Triggers a Switch-like Decrease in Thrombospondin-Dependent Activation of TGF-beta1

Biophysical Journal, 103(5), Elsevier Science (IF 4.22)

A computational model of the role of plasmin on TGF-Beta1 activation that is verified with wet lab experiments.

FUSE: A Profit Maximization Approach for Functional Summarization of Biological Networks

BMC Bioinformatics, 13(S3), Biomed Central (IF 3.03)

(Special Issue on Best Papers of ACM BCB 2011)

Extended version of the FUSE paper that integrates network summarization with PPI networks.

In Silico Identification of Endo16 Regulators in the Sea Urchin Endomesoderm Gene Regulatory Network

In the Proceedings of 2nd ACM SIGHIT International Health Informatics Symposium

ACM Press, Miami, Florida, USA

This work takes a data analytics-driven approach to identify Endo16 regulators in a gene regulatory network.

PANI: An Interactive Data-Driven Tool for Target Prioritization in Signaling Networks

In the Proceedings of 2nd ACM SIGHIT International Health Informatics Symposium

ACM Press, Miami, Florida, USA (Demo)

Video

Software protype that demonstrates data analytics-driven prioritizing of targets in disease-related signaling networks.

FUSE: A System for Data-Driven Multi-Level Functional Summarization of Protein Interaction Networks

In the Proceedings of 2nd ACM SIGHIT International Health Informatics Symposium

ACM Press, Miami, Florida, USA (Demo)

Video

Software prototype of functional summarization of PPI networks that bridges graph summarization with biological networks.

The Steady States and Dynamics of Urokinase-mediated Plasmin Activation In Silico and In Vitro

Biophysical Journal, 101(8), Elsevier Science (IF 4.2)

This paper uses a computational model to understand the steady states and dynamics of plasmin activation, which is verified using wet lab. This is a more complete version of the PSB paper.

FUSE: Towards Multi-Level Functional Summarization of Protein Interaction Networks

In the Proceedings of 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB 2011)

ACM Press, Chicago, USA

Best Paper Award

This paper for the first time brings together network summarization and protein-protein interaction networks under one umbrella to understand functional roles of different components of the network.

PANI: A Novel Algorithm for Fast Discovery of Putative Target Nodes in Signaling Networks

In the Proceedings of 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB 2011)

ACM Press, Chicago, USA (Short)

Extended version

PANI uses a data analytics framework to discover putative target nodes in disease-related signaling networks.

March

2010

Cell Delivery Therapeutics for Liver Regeneration

Advanced Drug Delivery Reviews (ADDR)
62(7-8), Elsevier Science (IF 8.3)

This is a review work on liver regeneration.

The Steady States and Dynamics of Urokinase Mediated Plasmin Activation

In the Proceedings of 15^th Pacific Symposium of Biocomputing (PSB 2010)  Hawai, USA.

This paper uses a computational model to understand the steady states and dynamics of plasmin activation.