Persistent Manifold Learning of Protein Properties | Paper Citation Record

Observation e7437b20-8fef-4f13-871d-32847b18fbc3 · outbound

This paper cites Bennett, Giang T.

Persistent Manifold Learning of Protein Properties Bennett, Giang T

Reference 1

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Observation 14a94bb7-ea06-40d4-bc9d-ed670125730a · outbound

This paper cites Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.PLOS Computational Biology, 14(1):e1005929, 2018.

Persistent Manifold Learning of Protein Properties Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.PLOS Computational Biology, 14(1):e1005929, 2018

Reference 2

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Observation 8fa29a46-fd4e-476e-a7d2-99ae104d6b3d · outbound

This paper cites Topologynet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.PLOS Computational Biology, 13(7):e1005690, 2017.

Persistent Manifold Learning of Protein Properties Topologynet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.PLOS Computational Biology, 13(7):e1005690, 2017

Reference 3

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Reference 4

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Observation 5f2e2e9c-ef17-4ed4-9b17-f588bbd8cdaf · outbound

This paper cites Persistent laplacian projected omicron ba.4 and ba.5 to become new dominating variants.Computers in Biology and Medicine, 151:106262, 2022.

Persistent Manifold Learning of Protein Properties Persistent laplacian projected omicron ba.4 and ba.5 to become new dominating variants.Computers in Biology and Medicine, 151:106262, 2022

Reference 5

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Observation 6d22bcc0-4d84-4baa-952e-2885c40387a8 · outbound

This paper cites Evolutionary de Rham–Hodge method.

Persistent Manifold Learning of Protein Properties Evolutionary de Rham–Hodge method

Reference 6

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Observation 97830519-3701-446d-ac4d-31476e92d9dc · outbound

This paper cites Protein–protein interactions: General trends in the relationship between binding affinity and interfacial buried surface area.Protein Science, 22(4):510– 515, 2013.

Persistent Manifold Learning of Protein Properties Protein–protein interactions: General trends in the relationship between binding affinity and interfacial buried surface area.Protein Science, 22(4):510– 515, 2013

Reference 7

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Observation 9cbf7b27-3dd6-414c-ae4d-cc9a5aa7f654 · outbound

This paper cites ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction.

Persistent Manifold Learning of Protein Properties ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 8

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Observation 1dcecf9a-de48-4b30-a20f-60a2974d9e96 · outbound

This paper cites DBAC: A simple prediction method for protein binding hot spots based on burial levels and deeply buried atomic contacts.BMC Systems Biology, 5(Suppl 1):S5, 2011.

Persistent Manifold Learning of Protein Properties DBAC: A simple prediction method for protein binding hot spots based on burial levels and deeply buried atomic contacts.BMC Systems Biology, 5(Suppl 1):S5, 2011

Reference 9

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Observation e6640372-6267-4f6f-ad69-0b1bfecf4be7 · outbound

This paper cites Discrete differential forms for computational modeling.

Persistent Manifold Learning of Protein Properties Discrete differential forms for computational modeling

Reference 10

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Observation 16742d13-80db-42a2-a583-daee06b95afb · outbound

This paper cites Insights into Protein–Ligand interactions: Mechanisms, models, and methods.International Journal of Molecular Sciences, 17(2):144, 2016.

Persistent Manifold Learning of Protein Properties Insights into Protein–Ligand interactions: Mechanisms, models, and methods.International Journal of Molecular Sciences, 17(2):144, 2016

Reference 11

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Observation 839f6eca-0b60-47f9-b5ac-c6c8ee888173 · outbound

This paper cites First–second shell interactions in metal binding sites in proteins: A PDB survey and DFT/CDM calculations.Journal of the American Chemical Society, 125(10):3168–3180, 2003.

Persistent Manifold Learning of Protein Properties First–second shell interactions in metal binding sites in proteins: A PDB survey and DFT/CDM calculations.Journal of the American Chemical Society, 125(10):3168–3180, 2003

Reference 12

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Observation 767161ed-c10f-49b6-bbd4-b8a7d7ac1955 · outbound

This paper cites Durrant and J.

Persistent Manifold Learning of Protein Properties Durrant and J

Reference 13

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Persistent Manifold Learning of Protein Properties Unresolved cited work

Reference 14

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Observation 65d5b133-1746-47cf-8281-1087dc44e72b · outbound

This paper cites Feinberg, Debnil Sur, Zhenqin Wu, Brooke E.

Persistent Manifold Learning of Protein Properties Feinberg, Debnil Sur, Zhenqin Wu, Brooke E

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Observation 48f1516c-0fd1-471e-8c2c-15796b62a8bd · outbound

This paper cites CAML: Commutative algebra machine learning—a case study on protein–ligand binding affinity prediction.Journal of Chemical Information and Modeling, 2025.

Persistent Manifold Learning of Protein Properties CAML: Commutative algebra machine learning—a case study on protein–ligand binding affinity prediction.Journal of Chemical Information and Modeling, 2025

Reference 16

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Observation b810cf48-5a4b-4600-86d4-2b46e1b0d043 · outbound

This paper cites Algebraic connectivity of graphs.Czechoslovak Mathematical Journal, 23(2):298–305, 1973.

Persistent Manifold Learning of Protein Properties Algebraic connectivity of graphs.Czechoslovak Mathematical Journal, 23(2):298–305, 1973

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Observation 80016f31-ceff-4b95-94b5-4cc0bd3f7472 · outbound

This paper cites Fleishman and David Baker.

Persistent Manifold Learning of Protein Properties Fleishman and David Baker

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Persistent Manifold Learning of Protein Properties Unresolved cited work

Reference 19

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Observation e724413e-be2b-41b0-b52b-081aded1b0e8 · outbound

This paper cites Gilson, James A.

Persistent Manifold Learning of Protein Properties Gilson, James A

Reference 20

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Reference 21

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Reference 22

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Reference 23

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Observation c1199802-0d1c-4e28-9aaa-675e98719fd0 · outbound

This paper cites Interactiongraphnet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions.

Persistent Manifold Learning of Protein Properties Interactiongraphnet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions

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Observation b6b9b877-0760-4bce-b033-f35746bb05cc · outbound

This paper cites Metalprognet: A structure-based deep graph model for metalloprotein– ligand interaction predictions.Chemical Science, 14(8):2054–2069, 2023.

Persistent Manifold Learning of Protein Properties Metalprognet: A structure-based deep graph model for metalloprotein– ligand interaction predictions.Chemical Science, 14(8):2054–2069, 2023

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Observation c36be5cc-2f90-44b8-b67c-a9435a795e24 · outbound

This paper cites Kitchen, H´ el` ene Decornez, John R.

Persistent Manifold Learning of Protein Properties Kitchen, H´ el` ene Decornez, John R

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Observation 928a4fc5-a0ea-4473-839a-bd7501ba18e3 · outbound

This paper cites A simple physical model for binding energy hot spots in protein– protein complexes.Proceedings of the National Academy of Sciences, 99(22):14116–14121, 2002.

Persistent Manifold Learning of Protein Properties A simple physical model for binding energy hot spots in protein– protein complexes.Proceedings of the National Academy of Sciences, 99(22):14116–14121, 2002

Reference 27

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Observation 660df086-aa02-4c86-b1fb-0da9aff391bc · outbound

This paper cites Monn: A multi- objective neural network for predicting compound–protein interactions and affinities.Cell Systems, 10(4):308–322.e11, 2020.

Persistent Manifold Learning of Protein Properties Monn: A multi- objective neural network for predicting compound–protein interactions and affinities.Cell Systems, 10(4):308–322.e11, 2020

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Observation a3960ca8-47a3-48dc-928b-16158b6df98a · outbound

This paper cites Evolutionary-scale prediction of atomic- level protein structure with a language model.Science, 379(6637):1123–1130, 2023.

Persistent Manifold Learning of Protein Properties Evolutionary-scale prediction of atomic- level protein structure with a language model.Science, 379(6637):1123–1130, 2023

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Observation 4e54f67a-f56c-4de2-ae59-14926ba0d73f · outbound

This paper cites The algebraic stability for persistent laplacians.Homology, Homotopy and Applications, 26(2):297–323, 2024.

Persistent Manifold Learning of Protein Properties The algebraic stability for persistent laplacians.Homology, Homotopy and Applications, 26(2):297–323, 2024

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Observation 82463fac-4b51-4d4b-9692-451919f6ea0f · outbound

This paper cites Manifold topological deep learning for biomedical data.Nature Communications, 17:4710, 2026.

Persistent Manifold Learning of Protein Properties Manifold topological deep learning for biomedical data.Nature Communications, 17:4710, 2026

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Observation a017f9e8-7f2d-4bdd-81cc-0cd058edc218 · outbound

This paper cites Long et al.

Persistent Manifold Learning of Protein Properties Long et al

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Observation e37fe049-a90a-44dc-99fc-eeb2119a1e32 · outbound

This paper cites Recent advances in the development of Protein–Protein Interaction modulators: Mechanisms and clinical trials.Signal Transduction and Targeted Therapy, 5:213, 2020.

Persistent Manifold Learning of Protein Properties Recent advances in the development of Protein–Protein Interaction modulators: Mechanisms and clinical trials.Signal Transduction and Targeted Therapy, 5:213, 2020

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Observation cb6a5f99-6e51-47b2-8a96-f8b763daf33b · outbound

This paper cites Persistent laplacians: Properties, algorithms and implications.SIAM Journal on Mathematics of Data Science, 4(2):858–884, 2022.

Persistent Manifold Learning of Protein Properties Persistent laplacians: Properties, algorithms and implications.SIAM Journal on Mathematics of Data Science, 4(2):858–884, 2022

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Observation 8e4c69f9-08ed-48e8-9dc5-c35e81fa4738 · outbound

This paper cites Persistent spectral-based machine learning (PerSpect ML) for protein– ligand binding affinity prediction.Science Advances, 7(19):eabc5329, 2021.

Persistent Manifold Learning of Protein Properties Persistent spectral-based machine learning (PerSpect ML) for protein– ligand binding affinity prediction.Science Advances, 7(19):eabc5329, 2021

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Observation aad3ff3a-a5fb-4da6-8ac9-c15d8c6e4e5e · outbound

This paper cites Moesser, Dominik K.

Persistent Manifold Learning of Protein Properties Moesser, Dominik K

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Observation 3dee65d0-a1d9-4f89-a21d-45d45d33a258 · outbound

This paper cites Meanwell, and Kyeong Lee.

Persistent Manifold Learning of Protein Properties Meanwell, and Kyeong Lee

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Observation 3ea45348-9955-4d94-b6f7-6e2a76d928f9 · outbound

This paper cites DG-GL: Differential geometry-based geometric learning of molecular datasets.International Journal for Numerical Methods in Biomedical Engineering, 35(3):e3179, 2019.

Persistent Manifold Learning of Protein Properties DG-GL: Differential geometry-based geometric learning of molecular datasets.International Journal for Numerical Methods in Biomedical Engineering, 35(3):e3179, 2019

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Observation 6b3b9e21-836c-407a-8b4a-1db231c443ad · outbound

This paper cites Quinn, Tri Nguyen, Truyen Le, and Svetha Venkatesh.

Persistent Manifold Learning of Protein Properties Quinn, Tri Nguyen, Truyen Le, and Svetha Venkatesh

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Observation fc6c44ce-a2b3-4824-90b7-4e3f82d771e7 · outbound

This paper cites Permyakov.

Persistent Manifold Learning of Protein Properties Permyakov

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Observation 00e59779-a366-4252-999d-a458b9f7bbfc · outbound

This paper cites Combinatorial and Hodge PERSISTENT MANIFOLD LEARNING 25 Laplacians: Similarities and differences.SIAM Review, 2024.

Persistent Manifold Learning of Protein Properties Combinatorial and Hodge PERSISTENT MANIFOLD LEARNING 25 Laplacians: Similarities and differences.SIAM Review, 2024

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Observation a691103d-6b93-44aa-8a0c-bb46a5c4fda2 · outbound

This paper cites Lawrence Zitnick, Jerry Ma, and Rob Fergus.

Persistent Manifold Learning of Protein Properties Lawrence Zitnick, Jerry Ma, and Rob Fergus

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Observation 9b955a23-d6e6-4415-b1c5-3d2f6a7c074f · outbound

This paper cites Sorzano, Jos´ e Mar ´ ıa Carazo, and Joan Segura.

Persistent Manifold Learning of Protein Properties Sorzano, Jos´ e Mar ´ ıa Carazo, and Joan Segura

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Observation e8bec893-c963-4b81-ba35-632c2c654e8a · outbound

This paper cites Springer, Berlin, 1995.

Persistent Manifold Learning of Protein Properties Springer, Berlin, 1995

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Observation 36074489-e4c6-4ce7-8e47-0d7c61482f79 · outbound

This paper cites Scott, Andrew R.

Persistent Manifold Learning of Protein Properties Scott, Andrew R

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Observation 55525251-2f62-45e5-8446-ab7ede73f11f · outbound

This paper cites Topological data analysis and topological deep learning beyond persistent homology: a review.

Persistent Manifold Learning of Protein Properties Topological data analysis and topological deep learning beyond persistent homology: a review

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Observation deaec040-dcbd-4b8e-bf50-9c54d5039dbf · outbound

This paper cites Persistent de Rham–Hodge laplacians in Eulerian representation for manifold topological learning.AIMS Mathematics, 9(10):27438–27470, 2024.

Persistent Manifold Learning of Protein Properties Persistent de Rham–Hodge laplacians in Eulerian representation for manifold topological learning.AIMS Mathematics, 9(10):27438–27470, 2024

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Observation 49db6ea5-926a-432d-8cdd-f1c55c89c934 · outbound

This paper cites Topology-preserving hodge decomposition in the eulerian representation.Beijing Journal of Pure and Applied Mathematics, 2(2):619–657, 2025.

Persistent Manifold Learning of Protein Properties Topology-preserving hodge decomposition in the eulerian representation.Beijing Journal of Pure and Applied Mathematics, 2(2):619–657, 2025

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Observation f04b3771-bad3-4f55-b50d-78d3438e0c55 · outbound

This paper cites Representation of molecular structures with persistent homology for machine learning applications in chemistry.Nature communications, 11(1):3230, 2020.

Persistent Manifold Learning of Protein Properties Representation of molecular structures with persistent homology for machine learning applications in chemistry.Nature communications, 11(1):3230, 2020

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Observation 77d1fda8-53c6-4bbd-b66b-d4acf71f5202 · outbound

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Persistent Manifold Learning of Protein Properties Unresolved cited work

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Observation 5af2fa4f-f114-4566-80bf-b96997912e7d · outbound

This paper cites Persistent path laplacian.Foundations of Data Science, 5(1):26–55, 2023.

Persistent Manifold Learning of Protein Properties Persistent path laplacian.Foundations of Data Science, 5(1):26–55, 2023

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Observation 97553e61-8756-44df-b1a1-f6910a05d54a · outbound

This paper cites Persistent spectral graph.International Journal for Numerical Methods in Biomedical Engineering, 36(9):e3376, 2020.

Persistent Manifold Learning of Protein Properties Persistent spectral graph.International Journal for Numerical Methods in Biomedical Engineering, 36(9):e3376, 2020

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Observation 63ed901d-d71a-4399-9837-118b46e51afd · outbound

This paper cites Join persistent homology (jph)- based machine learning for metalloprotein–ligand binding affinity prediction.Journal of Chemical Information and Modeling, 65(6):2785–2793, 2025.

Persistent Manifold Learning of Protein Properties Join persistent homology (jph)- based machine learning for metalloprotein–ligand binding affinity prediction.Journal of Chemical Information and Modeling, 65(6):2785–2793, 2025

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Observation e03e1aa8-5dd9-42db-b4c9-7b2d1577041e · outbound

This paper cites Join persistent homology (JPH)-based machine learning for metalloprotein–ligand binding affinity prediction.Journal of Chemical Information and Modeling, 2025.

Persistent Manifold Learning of Protein Properties Join persistent homology (JPH)-based machine learning for metalloprotein–ligand binding affinity prediction.Journal of Chemical Information and Modeling, 2025

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Observation 6f690f5a-8f01-47fe-bc0b-6f03cac49bc3 · outbound

This paper cites Persistent sheaf laplacians.Foundations of data science (Springfield, Mo.), 7(2):446, 2025.

Persistent Manifold Learning of Protein Properties Persistent sheaf laplacians.Foundations of data science (Springfield, Mo.), 7(2):446, 2025

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Observation 2530cd56-350c-42a5-8acd-b944c6b19926 · outbound

This paper cites Persistent homology analysis of protein structure, flexibility, and folding.

Persistent Manifold Learning of Protein Properties Persistent homology analysis of protein structure, flexibility, and folding

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Observation 31e6d898-7bca-43df-8e09-760a522a7f8f · outbound

This paper cites Extending the accuracy limits of prediction for side-chain conformations.

Persistent Manifold Learning of Protein Properties Extending the accuracy limits of prediction for side-chain conformations

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Observation 1b698608-0dfe-4f89-99c3-956f657e480f · outbound

This paper cites PLNet: Persistent laplacian neural network for protein–protein binding free energy prediction.Protein Science, 34(12):e70377, 2025.

Persistent Manifold Learning of Protein Properties PLNet: Persistent laplacian neural network for protein–protein binding free energy prediction.Protein Science, 34(12):e70377, 2025

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This paper cites Ciallella, Lauren M.

Persistent Manifold Learning of Protein Properties Ciallella, Lauren M

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