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Articles are listed in reverse chronological order. Acceptance rates (ARs) are provided where available. Links to publishers are provided for each article. Local copies are also made available, under the warning that articles are provided under the copyright permission for noncommercial dissemination of academic work.
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Shehu's advisees indicated by: undergraduate (u), graduate (g), and postdoctoral (p) students.
Corresponding authors are indicated by (*).
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Revisiting Evolutionary Algorithms for Optimization for Deep Learning: Introducing DL-HEA.
GECCO,
Melbourne, Australia 2024.
Optimization Effectiveness versus Generalization Capability of Stochastic Optimization Algorithms for Deep Learning.
Intl Conf on Learning Representations (ICLR) 2024 Workshop on Bridging the Gap Between Practice and Theory in Deep Learning,
Vienna, Austria, 2024.
@inproceedings{InanLiuShehu-BGPT-ICLR24, author = {Inan, T. T. AND Liu, M. AND Shehu, A.}, title = {Optimization Effectiveness versus Generalization Capability of Stochastic Optimization Algorithms for Deep Learning}, booktitle = {ICLR Workshops: Workshop on Bridging the Gap Between Practice and Theory in Deep Learning (BGPT)}, year = {2024}, pages = {1-18} }A More Informative and Reproducible Remote Homology Evaluation for Protein Language Models.
LLMs4Bio Workshop at the 38th Annual AAAI Conference on Artificial Intelligence (AAAI),
Vancouver, CA, 2024.
@inproceedings{MoldwinKabirShehu-LLMs4Bio-AAAI24, author = {Moldwin, A. AND Kabir, A. AND Shehu, A.}, title = {A More Informative and Reproducible Remote Homology Evaluation for Protein Language Models}, booktitle = {AAAI Workshops: LLMs4Bio Workshop}, year = {2024}, pages = {1-8} }Global Convergence Analysis of Local SGD for One-hidden-layer Convolutional Neural Network without Overparameterization.
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS),
New Orleans, Louisiana, 2023.
@inproceedings{BaoShehuLiu-Neurips23, author = {Bao, Y. AND Shehu, A. AND Liu, M.}, title = {Global Convergence Analysis of Local SGD for One-hidden-layer Convolutional Neural Network without Overparameterization}, booktitle = {Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)}, year = {2023} }A Comparative Analysis of Transformer-based Protein Language Models for Remote Homology Prediction.
ACM Conf on Bioinformatics and Computational Biology (ACM-BCB) Workshops: Computational Structural Biology Workshop (CSBW),
Houston, TX, 2023 (best paper award).
@inproceedings{KabirMoldwinShehu-CSBW23, author = {Kabir, A. AND Moldwin, A. AND Shehu, A.}, title = {A Comparative Analysis of Transformer-based Protein Language Models for Remote Homology Prediction}, booktitle = {ACM BCB Workshops: Computational Structural Biology Workshop}, year = {2023}, pages = {1-8} }Structure- and Energy-based Analysis of Small Molecule Ligand Binding to Steroid Nuclear Receptors.
ACM Conf on Bioinformatics and Computational Biology (ACM-BCB) Workshops: Computational Structural Biology Workshop (CSBW),
Houston, TX, 2023.
@inproceedings{HercegShehu-CSBW23, author = {Herceg, M. AND Shehu, A.}, title = {Structure- and Energy-based Analysis of Small Molecule Ligand Binding to Steroid Nuclear Receptors}, booktitle = {ACM BCB Workshops: Computational Structural Biology Workshop}, year = {2023}, pages = {1-8} }The Cultural, Economic, and Institutional Determinants in National Artificial Intelligence Infrastructures: Insights from Social and Computer Sciences.
International Studies Association (ISA),
Montreal, CA, 2023.
@inproceedings{SinghShehuISA23, author = {Singh, J.P. AND others}, title = {The Cultural, Economic, and Institutional Determinants in National Artificial Intelligence Infrastructures: Insights from Social and Computer Sciences}, booktitle = {ISA}, year = {2023}, pages = {1-30} }What can Artificial Intelligence Tell Us About American Grand Strategy?
International Studies Association (ISA),
Montreal, CA, 2023.
@inproceedings{HunzekerShehuISA23, author = {Hunzeker, M.A. AND others}, title = {What can Artificial Intelligence Tell Us About American Grand Strategy?}, booktitle = {ISA}, year = {2023}, pages = {1-14} }Multi-objective Deep Data Generation with Correlated Property Control.
Neurips,
New Orleans, LA, 2022.
@inproceedings{WangZhaoShehuNeurips22, author = {Wang, Y. AND others}, title = {Multi-objective Deep Data Generation with Correlated Property Control}, booktitle = {Neurips}, year = {2022}, pages = {1-10} }Sequence-Structure Embeddings via Protein Language Models Improve on Prediction Tasks.
IEEE Intl Conf on Knowledge Graphs (ICKG),
Orlando, FL, 2022.
@inproceedings{InanShehuICKG22, author = {Kabir, A. AND Shehu, A.}, title = {Sequence-Structure Embeddings via Protein Language Models Improve on Prediction Tasks}, booktitle = {IEEE Intl Conf on Knowledge Graph (ICKG)}, year = {2022}, pages = {1-8} }Equivariant Encoding based GVAE (EqEn-GVAE) for Protein Tertiary Structure Generation.
IEEE Intl Conf on Bioinformatics and Biomedicine (BIBM) Workshops: Computational Structural Biology Workshop (CSBW),
Las Vegas, Nevada, 2022, pg. 1-8.
@inproceedings{RahmanShehuBIBMW22, author = {Rahman, T. AND Alam, F. F. AND Shehu, A.}, title = {Equivariant Encoding based GVAE (EqEn-GVAE) for Protein Tertiary Structure Generation}, booktitle = {BIBM Workshops}, year = {2022}, pages = {1-8} }Generation and Characterization of Quaternary Ammonium Compounds via Deep Learning.
IEEE Intl Conf on Bioinformatics and Biomedicine (BIBM) Workshops: Computational Structural Biology Workshop (CSBW),
Las Vegas, Nevada, 2022, pg. 1-8.
@inproceedings{WangGhaemiShehuBIBMW22, author = {Wang, Y. AND Ghaemi, S. AND Ding, A. AND Du, Y. AND Pan, B. AND Qi, K. AND Lin, X. AND Petersen, A. A. AN Leitgeb, A. AND Alkhalifa, S. AND Minbiole, K. AND Wuest, W. AND Zhao, L. AND Shehu, A.}, title = {Generation and Characterization of Quaternary Ammonium Compounds via Deep Learning}, booktitle = {BIBM Workshops}, year = {2022}, pages = {1-8} }Property-Controllable Generation of Quaternary Ammonium Compounds.
IEEE Intl Conf on Bioinformatics and Biomedicine (BIBM) Workshops: Computational Structural Biology Workshop (CSBW),
Las Vegas, Nevada, 2022, pg. 1-8.
@inproceedings{PanShehuZhaoBIBMW22, author = {Pan, B. AND Wang, Y. AND Lin, X. AND Qin, M. AND Du, Y. AND Ghaemi, S. AND Ding, A. AND Wang, S. AND Alkhalifa, S. AND Minbiole, K. AND Wuest, W. M. AND Petersen, A. A. AND Leitgeb, A. AND Shehu, A. AND Zhao, L.}, title = {Property-Controllable Generation of Quaternary Ammonium Compounds}, booktitle = {BIBM Workshops}, year = {2022}, pages = {1-8} }F-Measure Optimization for Multi-Class, Imbalanced Emotion Classification Tasks.
Intl Conf on Artificial Neural Networks (ICANN),
Bristol, UK, 2022.
@inproceedings{InanShehuICANN22, author = {Inann, T. AND Liu, M. AND Shehu, A.}, title = {F-Measure Optimization for Multi-Class, Imbalanced Emotion Classification Tasks}, booktitle = {Intl Conf on Artificial Neural Networks (ICANN)}, year = {2022}, pages = {1-12} }Property-Controllable Generation of Quaternary Ammonium Compounds.
KDD'22 Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD‘22),
Washington, D.C., 2022.
@inproceedings{PanShehuZhaoDLGKKD22, author = {Pan, B AND Wang, Y. AND Lin, X. AND Du, Y. AND Ghaemi, S. AND Ding, A. AND Wang, S. AND Shehu, A. AND Zhao, L.}, title = {Property-Controllable Generation of Quaternary Ammonium Compounds}, booktitle = {KDD'22 Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD‘22)}, year = {2022}, pages = {1-9} }Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets.
KDD'22 Workshop on Anomaly and Novelty Detection (ANDEA) 2022,
Washington, D.C., 2022.
@inproceedings{KamranfarBarbaraANDEAKKD22, author = {Kamranfar, P AND Lattanzi, D. AND Shehu, A. AND Barbara, D.}, title = {Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets}, booktitle = {KDD'22 Workshop on Anomaly and Novelty Detection (ANDEA)}, year = {2022}, pages = {1-9} }Interpretable Molecular Graph Generation via Monotonic Constraints.
SDM 2022,
Virtual, 2022.
@inproceedings{DuShehuSDM22, author = {Du, Y AND Guo, X. AND Shehu, A. AND Zhao, L.}, title = {Interpretable Molecular Graph Generation via Monotonic Constraints}, booktitle = {SDM}, year = {2022}, pages = {1-9} }Guiding Protein Conformation Sampling with Conformation Space Maps.
BICOB, volume 83,
Virtual, 2022, pg. 20-30 (finalist for best paper award).
@inproceedings{ZamanDeJongShehu_BICOB22, author = {Zaman, A. AND De Jong, K. A. AND Shehu, A.}, title = {Guiding Protein Conformation Sampling with Conformation Space Maps, booktitle = {BICOB}, year = {2022}, pages = {1-11} }Analysis of AlphaFold2 for Modeling Structures of Wildtype and Variant Protein Sequences.
BICOB, volume 83,
Virtual, 2022, pg. 53-65 (best paper award).
@inproceedings{KabirInanShehu_BICOB22, author = {Kabir, A. AND Inan, T. T. AND Shehu, A.}, title = {Analysis of AlphaFold2 for Modeling Structures of Wildtype and Variant Protein Sequences, booktitle = {BICOB}, year = {2022}, pages = {1-12} }Interpretable Molecular Graph Generation via Monotonic Constraints.
ML4Molecules Workshop at NeurIPS 2021,
Virtual, 2021.
@inproceedings{DuShehuML4Molecules21, author = {Du, Y AND Guo, X. AND Shehu, A. AND Zhao, L.}, title = {Interpretable Molecular Graph Generation via Monotonic Constraints}, booktitle = {ML4Molecules at NeurIPS}, year = {2021}, pages = {1-6} }Single Model Quality Estimation of Protein Structures via Non-negative Tensor Factorization.
IEEE Intl Conf on Comput Adv in Bio and Medical Sciences (ICCABS) 2021,
Virtual, 2021.
@inproceedings{KabirShehuICCABS21, author = {Kabir, K. AND Bhattarai, M. AND Alexandrov, B. S. AND Shehu, A.}, title = {Single Model Quality Estimation of Protein Structures via Non-negative Tensor Factorization}, booktitle = {ICCABS}, year = {2021}, pages = {1-12} }Graph Representation Learning for Protein Conformation Sampling.
IEEE Intl Conf on Comput Adv in Bio and Medical Sciences (ICCABS) 2021,
Virtual, 2021.
@inproceedings{RahmanShehuICCABS21, author = {Rahman, T. AND Du, Y. AND Shehu, A.}, title = {Graph Representation Learning for Protein Conformation Sampling}, booktitle = {ICCABS}, year = {2021}, pages = {1-12} }PsychBERT: A Mental Health Language Model for Social Media Mental Health Behavioral Analysis.
IEEE Intl Conf on Bioinformatics and Biomedicine (BIBM),
Virtual, 2021.
@inproceedings{VajreShehuBIBM21, author = {Vajre, V. AND Naylor, M. AND Kamath, U. AND Shehu, A.}, title = {PsychBERT: A Mental Health Language Model for Social Media Mental Health Behavioral Analysis}, booktitle = {BIBM}, year = {2021}, pages = {1-6} }Deep Latent-Variable Models for Controllable Molecule Generation.
IEEE Intl Conf on Bioinformatics and Biomedicine (BIBM),
Virtual, 2021, pg. 1-6.
@inproceedings{DuShehuBIBM21, author = {Du, Y. AND Wang, Y. AND Alam, F. F. AND Lu, Y. AND Guo, X. AND Zhao, L. AND Shehu, A.}, title = {Deep Latent-Variable Models for Controllable Molecule Generation}, booktitle = {BIBM}, year = {2021}, pages = {1-4} }Generating Physically-Realistic Tertiary Protein Structures with Deep Latent Variable Models Learning Over Experimentally-available Structures.
IEEE Intl Conf on Bioinformatics and Biomedicine (BIBM) Workshops: Computational Structural Biology Workshop (CSBW),
Virtual, 2021, pg. 1-8.
@inproceedings{AlamShehuBIBMW21, author = {Alam, F. F. AND Shehu, A.}, title = {Generating Physically-Realistic Tertiary Protein Structures with Deep Latent Variable Models Learning Over Experimentally-available Structures}, booktitle = {BIBM Workshops}, year = {2021}, pages = {1-8} }Antigen Binding Reshapes Antibody Energy Landscape and Conformation Dynamics.
IEEE Intl Conf on Bioinformatics and Biomedicine (BIBM) Workshops: Computational Structural Biology Workshop (CSBW),
Virtual, 2021, pg. 1-8.
@inproceedings{KabirShehuBIBMW21, author = {Kabir, K. AND Ma, B. AND Nussinov, R. AND Shehu, A.}, title = {Antigen Binding Reshapes Antibody Energy Landscape and Conformation Dynamics}, booktitle = {BIBM Workshops}, year = {2021}, pages = {1-8} }Detecting Scarce Emotions Via BERT and Hyperparameter Optimization.
Intl Conf on Artificial Neural Networks (ICANN),
Virtual, 2021, pg. 1-12.
@inproceedings{RajabiShehuICANN21, author = {Rajabi, Z. AND Uzuner, O. AND Shehu, A.}, title = {Detecting Scarce Emotions Via BERT and Hyperparameter Optimization}, booktitle = {ICANN}, year = {2021}, pages = {1-12} }Decoy Selection in Protein Structure Determination via Symmetric Non-negative Matrix Factorization.
IEEE Intl Conf on Bioinformatics and Biomedicine (BIBM),
Virtual, 2020, pg. 23-28.
@inproceedings{KabirShehuBIBM20, author = {Kabir, K. L. AND Chennupati, G. AND Vangara, J. AND Djidjev, H. AND Alexandrov, B. AND Shehu, A.}, title = {Decoy Selection in Protein Structure Determination via Symmetric Non-negative Matrix Factorization}, booktitle = {IEEE BIBM}, year = {2020}, pages = {23-28}, publisher = {IEEE} }Protein Decoy Generation via Adaptive Stochastic Optimization for Protein Structure Determination.
IEEE Intl Conf on Bioinformatics and Biomedicine (BIBM),
Virtual, 2020, pg. 50-55.
@inproceedings{ZamanInanShehuBIBM20, author = {Zaman, A. B. AND Inan, T. T. AND Shehu, A.}, title = {Protein Decoy Generation via Adaptive Stochastic Optimization for Protein Structure Determination}, booktitle = {IEEE BIBM}, year = {2020}, pages = {50-55}, publisher = {IEEE} }Variational Autoencoders for Protein Structure Prediction.
ACM Conference of Bioinformatics and Computational Biology (BCB),
Virtual, 2020, pg. 1-10.
@inproceedings{AlamShehuBCB20, author = {Alam, F. F. AND Shehu, A.}, title = {Variational Autoencoders for Protein Structure Prediction}, booktitle = {ACM Conf on Bioinf and Comput Biol (BCB)}, year = {2020}, publisher = {ACM}, pages = {1-10} }Interpretable Molecule Generation via Disentanglement Learning.
ACM Conference of Bioinformatics and Computational Biology (BCB) Workshops: Computational Structural Biology Workshop (CSBW),
Virtual, 2020, pg. 1-8.
@inproceedings{DuGuoZhaoShehuCSBW20, author = {Du, Y. AND Guo, X. AND Zhao, L. AND Shehu, A.}, title = {Interpretable Molecule Generation via Disentanglement Learning}, booktitle = {ACM Conf on Bioinf and Comput Biol (BCB) Workshops: Comput Struct Biol Workshop (CSBW)}, year = {2020}, publisher = {ACM}, pages = {1-8} }From Interatomic Distances to Protein Tertiary Structures with a Deep Convolutional Neural Network.
ACM Conference of Bioinformatics and Computational Biology (BCB) Workshops: Computational Structural Biology Workshop (CSBW),
Virtual, 2020, pg. 1-8
@inproceedings{DuKabirZhaoShehuCSBW20, author = {Du, Y. AND Kabir, A. AND Zhao, L. AND Shehu, A.}, title = {From Interatomic Distances to Protein Tertiary Structures with a Deep Convolutional Neural Network}, booktitle = {ACM Conf on Bioinf and Comput Biol (BCB) Workshops: Comput Struct Biol Workshop (CSBW)}, year = {2020}, publisher = {ACM}, pages = {1-8} }Deep Ranking in Template-free Protein Structure Prediction.
ACM Conference of Bioinformatics and Computational Biology (BCB),
Virtual, 2020, pg. 1-10.
@inproceedings{GuoAkhterShehuChengBCB20, author = {Guo, X. AND Akhter, N. AND Guo, Z. AND Wu, T. AND Hou, J. AND Shehu, A. AND Cheng, J.}, title = {Deep Ranking in Template-free Protein Structure Prediction}, booktitle = {ACM Conf on Bioinf and Comput Biol (BCB)}, year = {2020}, publisher = {ACM}, note = {accepted} }Reconstruction and Decomposition of High-Dimensional Landscapes via Unsupervised Learning.
KDD,
San Diego, CA, 2020, pg. 2505–2513.
@inproceedings{LeiAkhterQiaoShehu20, author = {Lei, J. AND Akhter, N. AND Qiao, W. AND Shehu, A.}, title = {Reconstruction and Decomposition of High-Dimensional Landscapes via Unsupervised Learning}, booktitle = {KDD}, year = {2020}, location = {San Diego, CA}, pages = {2505–2513}, publisher = {ACM} }Node-Edge Co-disentangled Representation Learning for Attributed Graph Generation.
KDD,
San Diego, CA, 2020, pg. 1697–1707.
@inproceedings{, author = {Guo, X. AND Zhao, L. AND Qin, Z. AND Wu, L. AND Shehu, A. AND Ye, Y.}, title = {Node-Edge Co-disentangled Representation Learning for Attributed Graph Generation}, booktitle = {KDD}, year = {2020}, location = {San Diego, CA}, pages = {1697–1707}, publisher = {ACM} }Meta-Learning for Industrial System Monitoring via Multi-objective Optimization.
16th Intl Conf on Data Science (ICDATA),
Las Vegas, NV, 2020, accepted.
@inproceedings{KamranfarLattanziShehu20, author = {Kamranfar, P. AND Lattanzi, D. AND Shehu, A.}, title = {Meta-Learning for Industrial System Monitoring via Multi-objective Optimization}, booktitle = {16th Intl Conf on Data Science (ICDATA)}, year = {2020}, location = {Las Vegas, NV} }Exploring Deep Neural Network Architectures: A Case Study on Improving Antimicrobial Peptide Recognition.
Intl Conf on Bioinf and Comput Biol (BICOB), vol. 70,
San Francisco, CA, 2020, pg. 182-191.
@inproceedings{DuaBarbaraShehuBICOB20, author = {Dua, M. AND Barbara, D. AND Shehu, A.}, title = {Exploring Deep Neural Network Architectures: A Case Study on Improving Antimicrobial Peptide Recognition}, booktitle = {Intl Conf on Bioinf and Comput Biol (BICOB)}, year = {2020}, volume = {70}, pages = {182-191}, publisher = {IEEE}, location = {San Francisco, CA} }From Unsupervised Multi-Instance Learning to Identification of Near-Native Protein Structures.
Intl Conf on Bioinf and Comput Biol (BICOB), vol. 70,
San Francisco, CA, 2020, pg. 59-68.
@inproceedings{AlamShehuBICOB20, author = {Alam, F. F. AND Shehu, A.}, title = {From Unsupervised Multi-Instance Learning to Identification of Near-Native Protein Structures}, booktitle = {Intl Conf on Bioinf and Comput Biol (BICOB)}, year = {2020}, volume = {70}, pages = {59-68}, publisher = {IEEE}, location = {San Francisco, CA} }A Multi-channel BiLSTM-CNN Model for Multilabel Emotion Classification of Informal Text.
IEEE Intl Conf on Semantic Computing (ICSC),
San Diego, CA, 2020, accepted.
@inproceedings{RajabiShehuICSC20, author = {Rajabi, Z. AND Uzuner, O. AND Shehu, A.}, title = {A Multi-channel BiLSTM-CNN Model for Multilabel Emotion Classification of Informal Text}, booktitle = {Intl Conf on Semantic Computing (ICSC)}, year = {2020}, publisher = {IEEE}, location = {San Diego, CA} }Identifying Near-Native Protein Structures via Anomaly Detection.
IEEE Intl Conf on Bioinformatics and Biomedicine (BIBM),
San Diego, CA, 2019, pg. 30-35.
@inproceedings{TadepalliShehuBIBM19, author = {Tadepalli, S. AND Akhter, N. AND Barbara, C. AND Shehu, A.}, title = {Identifying Near-Native Protein Structures via Anomaly Detection}, booktitle = {IEEE BIBM}, year = {2019}, publisher = {IEEE}, location = {San Diego, CA}, pages = {30-35}, }Non-Negative Matrix Factorization for Selection of Near-Native Protein Tertiary Structures.
IEEE BIBM,
San Diego, CA, 2019, accepted.
@inproceedings{TadepalliShehuBIBM19, author = {Akhter, N. AND Vangara, R. AND Chennupati, G. AND Alexandrov, B. AND Djidjev, H. AND Shehu, A.}, title = {Non-Negative Matrix Factorization for Selection of Near-Native Protein Tertiary Structures}, booktitle = {IEEE BIBM}, year = {2019}, publisher = {IEEE}, location = {San Diego, CA} }Decoy Ensemble Reduction in Template-free Protein Structure Prediction.
Comput Struct Biol Workshop (CSBW) - ACM BCB Workshops,
Niagara Falls, NY, 2019, pg. 562-567.
@inproceedings{ZamanShehuCSBW19, author = {Zaman, AB. AND Kamranfar, P. AND Domeniconi, C. AND Shehu, A.}, title = { Decoy Ensemble Reduction in Template-free Protein Structure Prediction}, booktitle = {ACM BCB Workshops}, year = {2019}, publisher = {ACM}, location = {Niagara Falls, NY}, pages = {562-567} }Learning Reduced Latent Representations of Protein Structure Data.
Comput Struct Biol Workshop (CSBW) - ACM BCB Workshops,
Niagara Falls, NY, 2019, 592-597.
@inproceedings{AlamRahmanShehuCSBW19, author = {Alam, F. AND Rahman, T. AND Shehu, A.}, title = {Learning Reduced Latent Representations of Protein Structure Data}, booktitle = {ACM BCB Workshops}, year = {2019}, publisher = {ACM}, location = {Niagara Falls, NY}, pages = {592-597} }Using Sequence-Predicted Contacts to Guide Template-free Protein Structure Prediction.
ACM BCB,
Niagara Falls, NY, 2019, 154-160.
@inproceedings{ZamanShehuBCB19, author = {Zaman, A. AND Parthasarathy, P. AND Shehu, A.}, title = {Using Sequence-Predicted Contacts to Guide Template-free Protein Structure Prediction}, booktitle = {ACM BCB}, year = {2019}, publisher = {ACM}, location = {Niagara Falls, NY}, pages = {154-160} }User Behavioral Modeling for Fake Information Mitigation on Social Web.
SBP-BRiMS,
Washington, D.C. 2019, 234-244.
Using Subpopulation EAs to Map Molecular Structure Landscapes.
GECCO,
Prague, Czech Republic 2019, pg. 960-967.
Connecting Molecular Energy Landscape Analysis with Markov Model-based Analysis of Equilibrium Structural Dynamics.
BiCoB,
Honolulu, HI 2019, vol 60, pg. 181-189. Best Paper Award .
Equipping Decoy Generation Algorithms for Template-free Protein Structure Prediction with Maps of the Protein Conformation Space.
BiCoB,
Honolulu, HI 2019, vol. 60, pg. 161-169 (finalist for best paper award).
Guiding Exploration of Antimicrobial Peptide Space with a Deep Neural Network.
IEEE BIBM Workshops: Artificial Intelligence Techniques for BioMedicine and HealthCare (AIBH),
Madrid, Spain 2018, pg. 2082-2087.
ML-Select: Improved Decoy Selection via Machine Learning and Ranking.
IEEE Intl Conf on Comput Adv in Bio and Medical Sciences (ICCABS),
Las Vegas, Nevada 2018.
Reconstructing and Decomposing Protein Energy Landscapes to Organize Structure Spaces and Reveal Biologically-active States.
IEEE Intl Conf on Bioinf and Biomed (BIBM),
Madrid, Spain 2018, pg. 56-60.
Community Detection for Decoy Selection in Template-free Protein Structure Prediction.
Comput Struct Biol Workshop (CSBW) - ACM BCB Workshops,
Washington, D.C. 2018, pg. 621-625.
@inproceedings{AlmsnedShehuCSBW18, author = {Hassan, L. AND Rajabi, Z. AND Akhter, N. AND Shehu, A.}, title = {Community Detection for Decoy Selection in Template-free Protein Structure Prediction}, booktitle = {ACM BCB Workshops}, year = {2018}, publisher = {ACM}, location = {Washington, D.C.} }Modeling the Tertiary Structure of a Multi-domain Protein.
Comput Struct Biol Workshop (CSBW) - ACM BCB Workshops,
Washington, D.C. 2018, pg. 615-620.
@inproceedings{AlmsnedShehuCSBW18, author = {Almsned, F. AND Gogovi, G. AND Bracci, N. AND Kehn-Hall, K. AND Blaisten-Barojas, E. AND Shehu, A.}, title = {Modeling the Tertiary Structure of a Multi-domain Protein}, booktitle = {ACM BCB Workshops}, year = {2018}, publisher = {ACM}, location = {Washington, D.C.} }Analysis of Energy Landscapes for Improved Decoy Selection in Template-free Protein Structure Prediction.
Intl Conf on Bioinf and Comp Biol (BICoB),
Las Vegas, NV 2018, pg. 111-116 (finalist for best paper award).
@inproceedings{AkhterShehu18, author = {Akhter, N. AND Shehu, A.}, title = {Analysis of Energy Landscapes for Improved Decoy Selection in Template-free Protein Structure Prediction}, booktitle = {Intl Conf on Bioinf and Comp Biol (BICoB)}, year = {2018}, pages = {111-116}, publisher = {IEEE}, location = {Las Vegas, NV} }Reconstructing and Mining Protein Energy Landscape to Understand Disease.
IEEE Intl Conf on Bioinf and Biomed (BIBM),
Kansas City, MO 2017, pg. 22-27.
@inproceedings{QiaoShehu17, author = {Qiao, W. AND Maximova, T. AND Fang, X. AND Plaku, E. AND Shehu, A.}, title = {Reconstructing and Mining Protein Energy Landscape to Understand Disease}, booktitle = {IEEE Intl Conf on Biomed and Bioinf (BIBM)}, year = {2017}, publisher = {IEEE}, pages = {22-27}, location = {Kansas City, MO} }In many proteins central to human biology, pathogenic mutations percolate to dysfunction by affecting structural rearrangements. In principle, mapping out the structures accessible by a protein sequence and organizing them by their energetics, so reconstructing the energy landscape, promises to relate a protein's (altered) dynamics to (dys)function. Reconstructing protein energy landscapes is generally infeasible, because the disparate spatio-temporal scales, particularly those in slow structural rearrangements, challenge wet and the dry laboratories. Recent algorithmic innovation in our laboratory allows reconstructing energy landscapes of medium-size proteins in the presence of sufficient prior wet-laboratory structure data. The ability to reconstruct energy landscapes of any number of variants of a protein (healthy and pathogenic) is renewing the need for landscape analysis and comparison. Here we describe a novel landscape analysis method that employs concepts from topological and statistical analysis of high-dimensional spatial data. The method detects altered landscape features in response to mutations and allows formulating hypotheses on the impact of mutations on (dys)function. The work presented here opens up an interesting avenue into automated analysis and summarization of landscapes that, as we demonstrate on an enzyme central to human health, yields itself to machine learning approaches at the energy landscape level.Out of One, Many: Exploiting Intrinsic Motions to Explore Protein Structure Spaces.
IEEE Intl Conf on Comput Adv in Bio and Medical Sciences (ICCABS),
Orlando, FL 2017 (accepted).
@inproceedings{MorrisMaximovaShehuICCABS17, author = {Morris, D. AND Maximova, T. AND Plaku, E. AND Shehu, A.}, title = {Out of One, Many: Exploiting Intrinsic Motions to Explore Protein Structure Spaces}, booktitle = {Intl Conf on Comput Adv in Bio and Medical Sciences (ICCABS)}, year = {2017}, publisher = {IEEE}, pages = {1-6}, location = {Orlando, FL} }Nearly all cellular processes involve proteins structurally rearranging to accommodate molecular partners. The energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. Reconstructing a protein's energy landscape holds the key to characterizing the structural dynamics and its regulation of protein function. In practice, the disparate spatio-temporal scales spanned by the slow dynamics challenge wet and dry laboratories. The growing number of deposited structures for proteins central to human biology presents an opportunity to infer the relevant dynamics. Recent computational efforts using extrinsic modes of motion as variables have successfully reconstructed detailed energy landscapes of several medium-size proteins. Here we investigate the extent to which one can reconstruct the energy landscape of a protein in the absence of sufficient, wet-laboratory structural data. We do so by integrating intrinsic modes of motion extracted off a single structure in a stochastic optimization framework that supports the plug-and-play of different variable selection strategies. We demonstrate that, while knowledge of more wet-laboratory structures yields better-reconstructed landscapes, precious information can be obtained even when one structural model is available. The presented work opens up interesting venues of research on structure-based inference of dynamics.Evolving Conformation Paths to Model Protein Structural Transitions.
Comput Struct Biol Workshop (CSBW) - ACM BCB Workshops,
Boston, MA 2017, pg. 673-678.
@inproceedings{SapinDeJongShehuCSBW17, author = {Sapin, E. AND {De Jong}, K. A. AND Shehu, A.}, title = {Evolving Conformation Paths to Model Protein Structural Transitions}, booktitle = {ACM BCB Workshops}, year = {2017}, publisher = {ACM}, pages = {673-678}, location = {Boston, MA} }Proteins are dynamic biomolecules. A structure-by-structure characterization of a protein's transition between two different functional structures is central to elucidating the role of dynamics in modulating protein function and designing therapeutic drugs. Characterizing transitions challenges both dry and wet laboratories. Some computational methods compute discrete representations of the energy landscape that organizes structures of a protein by their potential energies. The representations support queries for paths (series of structures) connecting start and goal structures of interest. In this paper address the problem of modeling protein structural transitions under the umbrella of stochastic optimization and propose a novel evolutionary algorithm (EA). The EA evolves paths without reconstructing the energy landscape, addressing two competing optimization objectives, energetic cost and structural resolution. Rather than seek one path, the EA yields an ensemble of paths to represent a transition. Preliminary applications suggest the EA is effective while operating under a reasonable computational budget.Modeling Protein Structural Transitions as a Multiobjective Optimization Problem.
IEEE Intl Conf on Comput Intel in Bioinf and Comput Biol (CIBCB) Issue of IEEE Computational Intelligence Magazine, 12(2): 8058536, doi: 10.1109/CIBCB.2017.8058536,
Manchester, UK 2017.
@article{SapinDeJongShehuCIBCB7, author = {Sapin, E. AND {De Jong}, K. A. AND Shehu, A.}, title = {Modeling Protein Structural Transitions as a Multiobjective Optimization Problem}, booktitle = {IEEE Comput Intel Magazine}, year = {2017}, volume = {12}, number = {2}, pages = {8058536}, doi = {10.1109/CIBCB.2017.8058536} }Proteins of importance to human biology can populate significantly different three-dimensional (3d) structures at equilibrium. By doing so, a protein is able to interface with different molecules in the cell and so modulate its function. A structure-by-structure characterization of a protein's transition between two structures is central to elucidate the role of structural dynamics in regulating molecular interactions, understand the impact of sequence mutations on function, and design molecular therapeutics. Much wet- and dry-laboratory research is devoted to characterizing structural transitions. Computational approaches rely on constructing a full or partial, structured representation of the energy landscape that organizes structures by potential energy. The representation readily yields one or more paths that consist of series of structures connecting start and goal structures of interest. In this paper, we propose instead to cast the problem of computing transition paths as a multiobjective optimization one. We identify two desired characteristics of computed paths, energetic cost and structural resolution, and propose a novel evolutionary algorithm (EA) to compute low-cost and high-resolution paths. The EA evolves paths representing a specific structural excursion without {\it a priori} constructing the energy landscape. Preliminary applications suggest the EA is effective while operating under a reasonable computational budget.Statistical Analysis of Computed Energy Landscapes to Understand Dysfunction in Pathogenic Protein Variants.
Comput Struct Biol Workshop (CSBW) - ACM BCB Workshops,
Boston, MA 2017 (accepted).
@inproceedings{QiaoMaximovaPlakuShehuCSBW7, author = {Qiao, W. AND Maximova, T. AND Plaku, E. AND Shehu, A.}, title = {Statistical Analysis of Computed Energy Landscapes to Understand Dysfunction in Pathogenic Protein Variants}, booktitle = {ACM BCB Workshops}, year = {2017}, publisher = {ACM}, pages = {1-6}, location = {Boston, MA} }The energy landscape underscores the inherent nature of proteins as dynamic systems interconverting between structures with varying energies. The protein energy landscape contains much of the information needed to characterize protein equilibrium dynamics and relate it to function. It is now possible to reconstruct energy landscapes of medium-size proteins with sufficient prior structure data. These developments turn the focus to tools for analysis and comparison of energy landscapes as a means of formulating hypotheses on the impact of sequence mutations on (dys)function via altered landscape features. We present such a method here and provide a detailed evaluation of its capabilities on an enzyme central to human biology. The work presented here opens up an interesting avenue into automated analysis and summarization of landscapes that yields itself to machine learning approaches at the energy landscape level.An Evolutionary Algorithm to Model Structural Excursions of a Protein.
ACM GECCO Workshop,
Berlin, Germany 2017, pg. 1669-1673.
@inproceedings{SapinDeJongShehuGECCOW17, author = {Sapin, E. AND {De Jong}, K. A. AND Shehu, A.}, title = {An Evolutionary Algorithm to Model Structural Excursions of a Protein}, booktitle = {ACM Conf on Genetic and Evolutionary Computation (GECCO) Workshop}, year = {2017}, publisher = {ACM}, pages = {1669-1673}, location = {Berlin, Germany} }Excursions of a protein between different structures at equilibrium are key to its ability to modulate its biological function. The energy landscape, which organizes structures available to a protein by their energetics, contains all the information needed to characterize and simulate structural excursions. Computational research aims to uncover such excursions to complement wet-laboratory studies in characterizing protein equilibrium dynamics. Popular strategies adapt the robot motion planning framework and construct full or partial, structured representations of the energy landscape. In this paper, we present a novel, complementary approach based on evolutionary computation. We propose an evolutionary algorithm that evolves path representations of a specific structural excursion without a priori construction of the energy landscape. Preliminary applications on healthy and pathogenic variants of a protein central to human health are promising and warranting further investigation of evolutionary search techniques for modeling protein structural excursions.Sample-based Models of Protein Structural Transitions.
ACM Conf on Bioinf and Comp Biol (BCB),
Seattle, Washington 2016, pg. 128-137.
@inproceedings{MaximovaShehuBCB16, author = {Maximova, T. AND Carr, D. AND Plaku, E. AND Shehu, A.}, title = {Sample-based Models of Protein Structural Transitions}, booktitle = {ACM Conf Bioinf and Comput Biol (BCB)}, year = {2016}, pages = {128-137}, publisher = {ACM}, location = {Seattle, WA, USA} }Modeling structural transitions of a protein at equilibrium is central to understanding function modulation but challenging due to the disparate spatio-temporal scales involved. Of particular interest are sampling-based methods that embed sampled structures in discrete, graph-based models of dynamics to answer path queries. These methods have to balance between further exploiting low-energy regions and exploring unpopulated, possibly high-energy regions needed for a transition. We recently presented a strategy that leverages experimentally-known structures to improve sampling. Here we demonstrate how such structures can further be leveraged to improve both exploitation and exploration and obtain paths of very high granularity. We show that such improvement is key to accurate sample-based modeling of structural transitions. We further demonstrate that ranking methods by the best transition cost obtained can be deceptive, as denser sampling, which follows a rugged landscape more faithfully, may result in higher costs. The work presented here improves understanding of the current capabilities and limitations of sampling-based methods. Proposing strategies to address some of these limitations in this paper is a first step towards sampling-based methods becoming reliable tools for modeling protein structural transitions.Path-based Guidance of an Evolutionary Algorithm in Mapping a Fitness Landscape and its Connectivity.
ACM GECCO Workshop,
Denver, Colorado 2016, pg. 1293-1298.
@inproceedings{SapinDeJongShehuGECCOW16, author = {Sapin, E. AND {De Jong}, K. A. AND Shehu, A.}, title = {Path-based Guidance of an Evolutionary Algorithm in Mapping a Fitness Landscape and its Connectivity}, booktitle = {ACM Conf on Genetic and Evolutionary Computation (GECCO) Workshop}, year = {2016}, publisher = {ACM}, pages = {1293-1298}, location = {Denver, Colorado, USA} }Understanding function regulation in proteins that switch between different structural states at equilibrium requires both finding the basins corresponding to such states and computing the succession of structures employed in basin-basin excursions. Recent work suggests evolutionary strategies can be used to feasibly map protein energy landscapes. Further work has shown that constructed maps can be additionally equipped with connectivity information to query for structural excursions. Here we highlight a potential issue when the problems of mapping and querying for paths (the latter representing structural excursions) are considered separately. We conduct a simple, proof-of-principle study that demonstrates the ability of an EA to allow extracting better paths from an EA-built map when the EA is supplied with the right information. The study is conducted on two key, multi-state proteins of importance to human biology and disease. The results showcased here suggest that further research efforts to guide an EA with path-based information are warranted and feasible.A Novel EA-based Memetic Approach for Efficiently Mapping Complex Fitness Landscapes.
GECCO,
Denver, Colorado 2016, pg. 85-92.
@inproceedings{SapinDeJongShehuGECCO16, author = {Sapin, E. AND {De Jong}, K. A. AND Shehu, A.}, title = {A Novel EA-based Memetic Approach for Efficiently Mapping Complex Fitness Landscapes}, booktitle = {ACM Conf on Genetic and Evolutionary Computation (GECCO)}, year = {2016}, pages = {85-92}, publisher = {ACM}, location = {Denver, Colorado, USA} }Recent work in computational structural biology focuses on modeling intrinsically dynamic proteins important to human biology and health. The energy landscapes of these proteins are rich in minima that correspond to alternative structures with which a dynamic protein binds to molecular partners in the cell. On such landscapes, evolutionary algorithms that switch their objective from classic optimization to mapping are more informative of protein structure-function relationships. While techniques for mapping energy landscapes have been developed in computational chemistry and physics, protein landscapes are more difficult for mapping due to their high dimensionality and multimodality. In this paper, we describe a memetic evolutionary algorithm that is capable of efficiently mapping complex landscapes. In conjunction with a hall of fame mechanism, the algorithm makes use of a novel, lineage- and neighborhood-aware local search procedure for better exploration and mapping of complex landscapes. We evaluate the algorithm on several benchmark problems and demonstrate the superiority of the novel local search mechanism. In addition, we illustrate its effectiveness in mapping the complex multimodal landscape of an intrinsically dynamic protein important to human health.A Principled Comparative Analysis of Dimensionality Reduction Techniques on Protein Structure Decoy Data.
Intl Conf on Bioinf and Comp Biol (BICoB),
Las Vegas, NV, 2016, pg. 43-48.
@INPROCEEDINGS{PanditShehuBICOB16, AUTHOR = {R. Pandit AND A. Shehu}, TITLE = {A Principled Comparative Analysis of Dimensionality Reduction Techniques on Protein Structure Decoy Data}, BOOKTITLE = {Intl Conf on Bioinf and Comput Biol}, EDITOR = {Ioerger, T. AND Haspel, N.}, YEAR = {2016}, PAGES = {43-48}, PUBLISHER = {ISCA}, LOCATION = {Las Vegas, NV} }In this paper we investigate the utility of dimensionality reduction as a tool to analyze and simplify the structure space probed by de novo protein structure prediction methods. We conduct a principled comparative analysis in order to identify which techniques are effective and can be further used in decoy selection. The analysis allows drawing several interesting observations. For instance, many of the reportedly state-of-the-art non-linear dimensionality reduction techniques fare poorly and are outperformed by linear techniques that tend to have consistent performance across various protein structure data sets. The analysis in this paper is likely to open the way to new techniques that make use of the reduced dimensions to organize protein structure data so as to automatically detect the elusive native structure of a protein. We show some preliminary results in this direction.Computing Transition Paths in Multiple-Basin Proteins with a Probabilistic Roadmap Algorithm Guided by Structure Data.
IEEE Intl Conf on Bioinf and BioMed (BIBM),
Washington, D.C. 2015, pg. 35-42.
Proteins are macromolecules in perpetual motion, switching between structural states to modulate their function. A detailed characterization of the precise yet complex relationship between protein structure, dynamics, and function requires elucidating transitions between functionally-relevant states. Doing so challenges both wet and dry laboratories, as protein dynamics involves disparate temporal scales. In this paper we present a novel, sampling-based algorithm to compute transition paths. The algorithm exploits two main ideas. First, it leverages known structures to initialize its search and define a reduced conformation space for rapid sampling. This is key to address the insufficient sampling issue suffered by sampling-based algorithms. Second, the algorithm embeds samples in a nearest-neighbor graph where transition paths can be efficiently computed via queries. The algorithm adapts the probabilistic roadmap framework that is popular in robot motion planning. In addition to efficiently computing lowest-cost paths between any given structures, the algorithm allows investigating hypotheses regarding the order of experimentally-known structures in a transition event. This novel contribution is likely to open up new venues of research. Detailed analysis is presented on multiple-basin proteins of relevance to human disease. Multiscaling and the AMBER ff12SB force field are used to obtain energetically-credible paths at atomistic detail.Evolutionary Search Strategies for Efficient Sample-based Representations of Multiple-basin Protein Energy Landscapes.
IEEE Intl Conf on Bioinf and BioMed (BIBM),
Washington, D.C. 2015, pg. 13-20.
Protein function is the result of a complex yet precise relationship between protein structure and dynamics. The ability of a protein to assume different structural states is key to biomolecular recognition and function modulation. Protein modeling research is driven by the need to complement experimental techniques in obtaining a comprehensive and detailed characterization of protein equilibrium dynamics. This is a non-trivial task, as it requires mapping the structure space (and underlying energy landscape) available to a protein under physiological conditions. Existing algorithms invariably adopt a stochastic optimization approach to explore the non-linear and multimodal protein energy landscapes. At the present, such algorithms suffer from limited sampling, particularly in high-dimensional and non-linear variable spaces rich in local minima. In this paper, we equip a recently published evolutionary algorithm with novel evolutionary search strategies to enhance the sampling capability for mapping multi-basin protein energy landscapes. We investigate initialization strategies to delay premature convergence and techniques to maintain and update on-the-fly a sample-based representation that serves as a map of the energy landscape. Applications on three proteins central to human disease show that the novel strategies are effective at locating basins in complex energy landscapes with a practical computational budget.Mapping Multiple Minima in Protein Energy Landscapes with Evolutionary Algorithms.
ACM GECCO Workshop,
Madrid, Spain, 2015, pg. 923-927.
Many proteins involved in human proteinopathies exhibit complex energy landscapes with multiple thermodynamically-stable and semi-stable structural states. Landscape reconstruction is crucial to understanding functional modulations, but one is confronted with the multiple minima problem. While traditionally the objective for evolutionary algorithms (EAs) is to find the global minimum, here we present work on an EA that maps the various minima in a protein's energy landscape. Specifically, we investigate the role of initialization of the initial population in the rate of convergence and solution diversity. Results are presented on two key proteins, H-Ras and SOD1, related to human cancers and familial Amyotrophic lateral sclerosis (ALS).Interleaving Global and Local Search for Protein Motion Computation.
LNCS: Bioinformatics Research and Applications, vol. 9096, pg. 175-186 (Proc. of 11th International Symposium on Bioinformatics Research and Applications -- ISBRA),
Norfolk, VA, 2015.
@INPROCEEDINGS{MolloyShehuISBRA15, AUTHOR = {K. Molloy AND A. Shehu}, TITLE = {Interleaving Global and Local Search for Protein Motion Computation}, BOOKTITLE = {LNCS: Bioinformatics Research and Applications}, EDITOR = { R. Harrison AND Y. Li AND I. Mandoiu}, YEAR = {2015}, VOLUME = {9096}, PAGES = {175-186}, PUBLISHER = {Springer International Publishing}, ADDRESS = {Norfolk, VA} }Evolution Strategies for Exploring Protein Energy Landscapes.
GECCO,
Madrid, Spain, 2015, pg. 217-224.
Sampling-based Methods for a Full Characterization of Energy Landscapes of Small Peptides.
IEEE Intl Conf on Bioinf and Biomed (BIBM),
Belfast, UK, 2014, pg. 37-44.
@INPROCEEDINGS{DevaursCortesBIBM14, AUTHOR = {D. Devaurs AND A. Shehu AND T. Simeon AND J. Cortes}, TITLE = {Sampling-based Methods for a Full Characterization of Energy Landscapes of Small Peptides}, BOOKTITLE = {IEEE Intl Conf on Bioinformatics and Biomedicine (BIBM)}, YEAR = {2014}, PAGES = {37-44}, ADDRESS = {Belfast, UK} }Obtaining an accurate representation of energy landscapes of biomolecules such as proteins and peptides is central to structure-function studies. Peptides are particularly interesting, as they exploit structural flexibility to modulate their biological function. Despite their small size, peptide modeling remains challenging due to the complexity of the energy landscape of such highly-flexible dynamic systems. Currently, only sampling-based methods can efficiently explore the conformational space of a peptide. In this paper, we suggest to combine two such methods to obtain a full characterization of energy landscapes of small yet flexible peptides. First, we propose a simplified version of the classical Basin Hopping algorithm to quickly reveal the meta-stable structural states of a peptide and corresponding low-energy basins in the landscape. Then, we present several variants of a robotics-inspired algorithm, the Transition-based Rapidly-exploring Random Tree to quickly determine transition state and transition path ensembles, as well as transition probabilities between meta-stable states. We demonstrate these two methods in this paper on the terminally-blocked alanine.A Novel Method to Improve Recognition of Antimicrobial Peptides through Distal Sequence-based Features.
IEEE Intl Conf on Bioinf and Biomed (BIBM),
Belfast, UK, 2014, pg. 371-378 (Best Student Paper Award).
@INPROCEEDINGS{VeltriShehuBIBM14, AUTHOR = {D. Veltri AND U. Kamath AND A. Shehu}, TITLE = {A Novel Method to Improve Recognition of Antimicrobial Peptides through Distal Sequence-based Features}, BOOKTITLE = {IEEE Intl Conf on Bioinformatics and Biomedicine (BIBM)}, YEAR = {2014}, PAGES = {371-378}, ADDRESS = {Belfast, UK} }Growing bacterial resistance to antibiotics is urging the development of new lines of treatment. The discovery of naturally-occurring antimicrobial peptides (AMPs) is motivating many experimental and computational researchers to pursue AMPs as possible templates. In the experimental community, the focus is generally on systematic point mutation studies to measure the effect on antibacterial activity. In the computational community, the goal is to understand what determines such activity in a machine learning setting. In the latter, it is essential to identify biological signals or features in AMPs that are predictive of antibacterial activity. Construction of effective features has proven challenging. In this paper, we advance research in this direction. We propose a novel method to construct and select complex sequence-based features able to capture information about distal patterns within a peptide. Thorough comparative analysis in this paper indicates that such features compete with the state-of-the-art in AMP recognition while providing transparent summarizations of antibacterial activity at the sequence level. We demonstrate that these features can be combined with additional physicochemical features of interest to a biological researcher to facilitate specific AMP design or modification in the wet laboratory. Code, data, results, and analysis accompanying this paper are publicly available online at: http://cs.gmu.edu/~ashehu/?q=OurTools.A Multiscale Hybrid Evolutionary Algorithm to Obtain Sample-based Representations of Multi-basin Protein Energy Landscapes.
ACM Conf on Bioinf and Comp Biol (BCB),
Newport Beach, CA, 2014, pg. 269-278.
Knowledge-based Search and Multiobjective Filters: Proposed Structural Models of GPCR Dimerization.
ACM Conf on Bioinf and Comp Biol (BCB),
Newport Beach, CA, 2014, pg. 279-288.
On the Stochastic Roadmap to Model Functionally-related Structural Transitions in Wildtype and Variant Proteins.
Workshop on Robotics Methods for Structural and Dynamic Modeling of Molecular Systems - Robotics: Science and Systems (RSS) Workshops,
Berkeley, CA, 2014, pg. 1-6.
Multi-Objective, O ff-Lattice, and Multiscale Evolutionary Algorithms for De-novo and Guided Protein Structure Modeling.
Workshop on Natural Computing for Protein Structure Prediction - Intl Conf on Parallel Problem Solving from nature (PPSN) Workshops,
Ljubljana, Slovenia, 2014.
Multi-Objective Optimization Techniques for Conformational Sampling in Template-Free Protein Structure Prediction.
Intl Conf on Bioinf and Comp Biol (BICoB),
Las Vegas, NV, 2014, pg. 143-148.
Probabilistic Roadmap-based Method to Model Conformational Switching of a Protein Among Many Functionally-relevant Structures.
Intl Conf on Bioinf and Comp Biol (BICoB),
Las Vegas, NV, 2014, pg. 137-142 (finalist for best paper award).
Binary Response Models for Recognition of Antimicrobial Peptides.
ACM Conf on Bioinf and Comp Biol (BCB),
Washington, DC, 2013, pg. 76-85.
Multi-Objective Stochastic Search for Sampling Local Minima in the Protein Energy Surface.
ACM Conf on Bioinf and Comp Biol (BCB),
Washington, DC, 2013, pg. 430-439.
Exploring the Structure Space of Wildtype Ras Guided by Experimental Data.
Comput Struct Biol Workshop (CSBW) - ACM BCB Workshops,
Washington, DC, 2013, pg. 757-764.
Informatics-driven Protein-protein Docking.
Comput Struct Biol Workshop (CSBW) - ACM BCB Workshops,
Washington, DC, 2013, pg. 772-779.
An Evolutionary Search Algorithm to Guide Stochastic Search for Near-native Protein Conformations with Multiobjective Analysis.
Workshop on Arti cial Intelligence and Robotics Meth- ods in Computational Biology - Intl Conf of Association for Advancement of Arti cial Intelligence (AAAI) Workshop,
Bellevue, WA, 2013.
Systematic Analysis of Global Features and Model Building for Recognition of Antimicrobial Peptides.
IEEE Intl Conf on Comput Adv in Bio and Medical Sciences (ICCABS),
New Orleans, LA, 2013.
Higher-order Representations for Automated Organization of Protein Structure Space.
IEEE Intl Conf on Comput Adv in Bio and Medical Sciences (ICCABS),
New Orleans, LA, 2013.
Off-Lattice Protein Structure Prediction with Homologous Crossover.
Genet and Evol Comp Conf (GECCO),
Amsterdam, Netherlands, 2013, pg. 287-294.
Physicochemical Determinants of Antimicrobial Activity.
Intl Conf on Bioinf and Comput Biol (BICoB),
Hawaii, 2013.
Biased Decoy Sampling to Aid the Selection of Near-Native Protein Conformations.
ACM Bioinf and Comp Biol (BCB),
Orlando, FL, 2012, pg. 131-138.
Efficient Basin Hopping in the Protein Energy Surface.
IEEE Intl Conference on Bioinformatics and Biomedicine (BIBM),
Philadelphia, PA, 2012, pg. 119-124.
A Basin Hopping Algorithm for Protein-Protein Docking.
IEEE Intl Conference on Bioinformatics and Biomedicine (BIBM),
Philadelphia, PA, 2012, pg. 466-469.
A Robotics-inspired Method to Sample Conformational Paths Connecting Known Functionally-relevant Structures in Protein Systems.
Comput Struct Biol Workshop (CSBW) - IEEE BIBM Workshops,
Philadelphia, PA, 2012, pg. 56-63.
A Population-based Evolutionary Algorithm for Sampling Minima in the Protein Energy Surface.
Comput Struct Biol Workshop (CSBW) - IEEE BIBM Workshops,
Philadelphia, PA, 2012, pg. 48-55.
A Spatial EA Framework for Parallelizing Machine Learning Methods.
Intl Conf on Parallel Problem Solving From Nature (PPSN),
Taormina, Italy, 2012, LNCS vol. 7491, pg. 206-215.
Populating Local Minima in the Protein Conformational Space.
IEEE Intl Conference on Bioinformatics and Biomedicine (BIBM),
Atlanta, GA, 2011, pg. 114-117.
Protein Conformational Search with Geometric Projections.
Comput Struct Biol Workshop (CSBW) - IEEE BIBM Workshops,
Atlanta, GA, 2011, pg. 366-373.
Refinement of Docked Protein Complex Structures Using Evolutionary Traces.
Comput Struct Biol Workshop (CSBW) - IEEE BIBM Workshops,
Atlanta, GA, 2011, pg. 400-404.
Protein Docking with Information on Evolutionary Conserved Interfaces.
Comput Struct Biol Workshop (CSBW) - IEEE BIBM Workshops,
Atlanta, GA, 2011, pg. 358-365.
An Evolutionary-based Approach for Feature Generation: Eukaryotic Promoter Recognition.
IEEE Congress on Evolutionary Computation (CEC),
New Orleans, LA, 2011, pg. 277-284.
Enhancing Sampling of the Conformational Space Near the Protein Native State.
Intl Conference on Bio-inspired Models of Network, Information, and Computing Systems (BIONETICS),
Boston, MA, 2010, LNICST (Springer), vol. 87, pg. 249-263 (best student paper award).
Feature and Kernel Evolution for Recognition of Hypersensitive Sites in DNA Sequences.
Intl Conference on Bio-inspired Models of Network, Information, and Computing Systems (BIONETICS),
Boston, MA, 2010, LNICST (Springer), vol. 87, pg. 213-238.
Using Evolutionary Computation to Improve SVM Classification.
IEEE World Congress on Computational Intelligence (WCCI),
Barcelona, Spain, 2010.
Selecting Predictive Features for Recognition of Hypersensi- tive Sites of Regulatory Genomic Sequences with an Evolutionary Algorithm.
Genet and Evol Comp Conf (GECCO),
Portland, Oregon, 2010, pg. 179-186.
Automated Design of Assemblable, Modular, Synthetic Chromosomes.
Lecture Notes in Computer Science, Parallel Processing and Applied Mathematics (PPAM),
Poland, 2009, vol. 6068, pg. 280-289.
An Ab-initio Tree-based Exploration to Enhance Sampling of Low-energy Protein Conformations.
Robotics: Science and Systems (RSS),
Seattle, WA, 2009, pg. 31-39.
