Efficient inference of dynamic gene regulatory networks using discrete penalty | Paper Citation Record

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This paper cites Network medicine: a network-based approach to human disease.

Efficient inference of dynamic gene regulatory networks using discrete penalty Network medicine: a network-based approach to human disease

Reference 1

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Efficient inference of dynamic gene regulatory networks using discrete penalty Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Reference 2

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This paper cites Advancements in single-cell RNA sequencing and spatial transcrip- tomics: transforming biomedical research.

Efficient inference of dynamic gene regulatory networks using discrete penalty Advancements in single-cell RNA sequencing and spatial transcrip- tomics: transforming biomedical research

Reference 3

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This paper cites The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.

Efficient inference of dynamic gene regulatory networks using discrete penalty The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells

Reference 4

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Efficient inference of dynamic gene regulatory networks using discrete penalty Network-based integration of multi-omics data for prioritizing cancer genes

Reference 5

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This paper cites Topology-based metrics for finding the optimal sparsity in gene regulatory network inference.

Efficient inference of dynamic gene regulatory networks using discrete penalty Topology-based metrics for finding the optimal sparsity in gene regulatory network inference

Reference 6

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This paper cites Optimal Sparsity Selection Based on an Information Criterion for Gene Regulatory Network Inference.

Efficient inference of dynamic gene regulatory networks using discrete penalty Optimal Sparsity Selection Based on an Information Criterion for Gene Regulatory Network Inference

Reference 7

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Efficient inference of dynamic gene regulatory networks using discrete penalty l {0} sparse inverse covariance estimation

Reference 8

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This paper cites Scalable inference of sparsely-changing Gaussian Markov random fields.

Efficient inference of dynamic gene regulatory networks using discrete penalty Scalable inference of sparsely-changing Gaussian Markov random fields

Reference 9

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This paper cites Sparse inverse covariance estimation with the graphical lasso.

Efficient inference of dynamic gene regulatory networks using discrete penalty Sparse inverse covariance estimation with the graphical lasso

Reference 10

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This paper cites The joint graphical lasso for inverse covariance estimation across multiple classes.

Efficient inference of dynamic gene regulatory networks using discrete penalty The joint graphical lasso for inverse covariance estimation across multiple classes

Reference 11

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Efficient inference of dynamic gene regulatory networks using discrete penalty Network inference via the time-varying graphical lasso

Reference 12

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This paper cites Breakdown in nonlinear regression.

Efficient inference of dynamic gene regulatory networks using discrete penalty Breakdown in nonlinear regression

Reference 13

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This paper cites Robust Regression and Outlier Detection.

Efficient inference of dynamic gene regulatory networks using discrete penalty Robust Regression and Outlier Detection

Reference 14

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This paper cites Deleting outliers in robust regression with mixed integer program- ming.

Efficient inference of dynamic gene regulatory networks using discrete penalty Deleting outliers in robust regression with mixed integer program- ming

Reference 15

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This paper cites Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models.

Efficient inference of dynamic gene regulatory networks using discrete penalty Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models

Reference 16

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Efficient inference of dynamic gene regulatory networks using discrete penalty Consistent second-order conic integer programming for learning Bayesian networks

Reference 17

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Efficient inference of dynamic gene regulatory networks using discrete penalty Integer Programming for Learning Directed Acyclic Graphs from Continuous Data

Reference 18

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Efficient inference of dynamic gene regulatory networks using discrete penalty Scalable network estimation with L0 penalty

Reference 19

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This paper cites A parametric approach for solving convex quadratic optimization with indicators over trees.

Efficient inference of dynamic gene regulatory networks using discrete penalty A parametric approach for solving convex quadratic optimization with indicators over trees

Reference 20

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This paper cites Joint Structural Estimation of Multiple Graphical Models.

Efficient inference of dynamic gene regulatory networks using discrete penalty Joint Structural Estimation of Multiple Graphical Models

Reference 21

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This paper cites A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models.

Efficient inference of dynamic gene regulatory networks using discrete penalty A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models

Reference 22

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This paper cites A constrained ℓ 1 minimization approach to sparse precision matrix estimation.

Efficient inference of dynamic gene regulatory networks using discrete penalty A constrained ℓ 1 minimization approach to sparse precision matrix estimation

Reference 23

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Efficient inference of dynamic gene regulatory networks using discrete penalty Elementary estimators for graphical models

Reference 24

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Efficient inference of dynamic gene regulatory networks using discrete penalty Joint estimation of multiple precision matrices with common structures

Reference 25

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This paper cites Solution Path of Time-varying Markov Random Fields with Discrete Regularization.

Efficient inference of dynamic gene regulatory networks using discrete penalty Solution Path of Time-varying Markov Random Fields with Discrete Regularization

Reference 26

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This paper cites BEST SUBSET SELECTION VIA A MODERN OPTIMIZATION LENS.

Efficient inference of dynamic gene regulatory networks using discrete penalty BEST SUBSET SELECTION VIA A MODERN OPTIMIZATION LENS

Reference 27

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This paper cites Certifiably optimal sparse regression via mixed- integer optimization.

Efficient inference of dynamic gene regulatory networks using discrete penalty Certifiably optimal sparse regression via mixed- integer optimization

Reference 28

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Efficient inference of dynamic gene regulatory networks using discrete penalty On polynomial-time solvability of combinatorial Markov random fields

Reference 29

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This paper cites Graph learning with tridiagonal Hessians: Efficient algorithms and applications.

Efficient inference of dynamic gene regulatory networks using discrete penalty Graph learning with tridiagonal Hessians: Efficient algorithms and applications

Reference 30

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This paper cites Real-time solution of quadratic optimization problems with banded matrices and indicator variables.

Efficient inference of dynamic gene regulatory networks using discrete penalty Real-time solution of quadratic optimization problems with banded matrices and indicator variables

Reference 31

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This paper cites Efficient inference of spatially-varying Gaussian Markov random fields with applications in gene regulatory networks.

Efficient inference of dynamic gene regulatory networks using discrete penalty Efficient inference of spatially-varying Gaussian Markov random fields with applications in gene regulatory networks

Reference 32

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Efficient inference of dynamic gene regulatory networks using discrete penalty SCANPY: large-scale single-cell gene expression data analysis

Reference 33

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Efficient inference of dynamic gene regulatory networks using discrete penalty Dictionary learning for integrative, multimodal and scalable single-cell analysis

Reference 34

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Efficient inference of dynamic gene regulatory networks using discrete penalty A comparison of single-cell trajectory inference methods

Reference 35

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Efficient inference of dynamic gene regulatory networks using discrete penalty Graphical models, exponential families, and variational inference

Reference 36

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Efficient inference of dynamic gene regulatory networks using discrete penalty Computer solution of large sparse positive definite

Reference 37

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Efficient inference of dynamic gene regulatory networks using discrete penalty Chordal graphs and semidefinite optimization

Reference 38

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Efficient inference of dynamic gene regulatory networks using discrete penalty Model selection and estimation in the Gaussian graphical model

Reference 39

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Efficient inference of dynamic gene regulatory networks using discrete penalty Extended Bayesian information criteria for Gaussian graphical models

Reference 40

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Efficient inference of dynamic gene regulatory networks using discrete penalty A fast and scalable joint estimator for learning multiple related sparse Gaussian graphical models

Reference 41

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Efficient inference of dynamic gene regulatory networks using discrete penalty GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks

Reference 42

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Efficient inference of dynamic gene regulatory networks using discrete penalty Statistical mechanics of complex networks

Reference 43

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Efficient inference of dynamic gene regulatory networks using discrete penalty Glioblastoma heterogeneity at single cell resolution

Reference 44

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Efficient inference of dynamic gene regulatory networks using discrete penalty An integrative model of cellular states, plasticity, and genetics for glioblastoma

Reference 45

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This paper cites Pathway-based classi- fication of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities.

Efficient inference of dynamic gene regulatory networks using discrete penalty Pathway-based classi- fication of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities

Reference 46

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Efficient inference of dynamic gene regulatory networks using discrete penalty Cancer cell heterogeneity and plasticity: A paradigm shift in glioblastoma

Reference 47

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Efficient inference of dynamic gene regulatory networks using discrete penalty Collagen in the central nervous system: contributions to neurodegeneration and promise as a therapeutic target

Reference 48

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This paper cites Phosphorylation, dephosphorylation, and multiprotein assemblies regulate dynamic behavior of neuronal cytoskeleton: a mini-review.

Efficient inference of dynamic gene regulatory networks using discrete penalty Phosphorylation, dephosphorylation, and multiprotein assemblies regulate dynamic behavior of neuronal cytoskeleton: a mini-review

Reference 49

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Efficient inference of dynamic gene regulatory networks using discrete penalty Phenotypic and functional consequences of haploinsufficiency of genes from exocyst and retinoic acid pathway due to a recurrent microdeletion of 2p13

Reference 50

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Efficient inference of dynamic gene regulatory networks using discrete penalty Exploring the pathological mechanisms underlying Cohen syndrome

Reference 51

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This paper cites Single-cell transcriptomics unveils gene regulatory network plasticity.

Efficient inference of dynamic gene regulatory networks using discrete penalty Single-cell transcriptomics unveils gene regulatory network plasticity

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:44.172117Z

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This paper cites Transcription factor BACH1 in cancer: roles, mechanisms, and prospects for targeted therapy.

Efficient inference of dynamic gene regulatory networks using discrete penalty Transcription factor BACH1 in cancer: roles, mechanisms, and prospects for targeted therapy

Reference 53

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This paper cites Single-cell multi-omics sequencing uncovers region-specific plasticity of glioblastoma for complementary therapeutic targeting.

Efficient inference of dynamic gene regulatory networks using discrete penalty Single-cell multi-omics sequencing uncovers region-specific plasticity of glioblastoma for complementary therapeutic targeting

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:44.142869Z

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This paper cites TopicNet: a framework for measuring transcriptional regulatory network change.

Efficient inference of dynamic gene regulatory networks using discrete penalty TopicNet: a framework for measuring transcriptional regulatory network change

Reference 55

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Observation e89e28ba-f009-4c68-bba9-f17f53ef47b3 · outbound

This paper cites Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets.

Efficient inference of dynamic gene regulatory networks using discrete penalty Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:44.111819Z

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Observation 4eea12a2-2ec1-4538-b573-7a607c8cf7f9 · outbound

This paper cites topicmodels: An R package for fitting topic models.

Efficient inference of dynamic gene regulatory networks using discrete penalty topicmodels: An R package for fitting topic models

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:44.097771Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 97c24257-87ad-4bd9-b5fd-fccbb52c0b1a · outbound

This paper cites Package ‘ldatuning’; 2016.

Efficient inference of dynamic gene regulatory networks using discrete penalty Package ‘ldatuning’; 2016

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:44.083639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2724ffd9-341a-41b7-868b-3a0c86be1cf4 · outbound

This paper cites ZHX2 Interacts with Ephrin-B and regulates neural progenitor maintenance in the developing cerebral cortex.

Efficient inference of dynamic gene regulatory networks using discrete penalty ZHX2 Interacts with Ephrin-B and regulates neural progenitor maintenance in the developing cerebral cortex

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:44.069171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6d4f5bc2-b3d4-42b6-b59a-81da3ebc9fe6 · outbound

This paper cites Transcription factor AP2 epsilon (Tfap2e) regulates neural crest specification in Xenopus.

Efficient inference of dynamic gene regulatory networks using discrete penalty Transcription factor AP2 epsilon (Tfap2e) regulates neural crest specification in Xenopus

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:44.053733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation afb56674-9ed0-4724-9083-f0ae8b83dca7 · outbound

This paper cites The expanding roles of Nr6a1 in development and evolution.

Efficient inference of dynamic gene regulatory networks using discrete penalty The expanding roles of Nr6a1 in development and evolution

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:44.038857Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 96cce433-35b2-40be-bedd-9ec7f5e8578e · outbound

This paper cites Current understanding of hypoxia in glioblastoma multiforme and its response to immunotherapy.

Efficient inference of dynamic gene regulatory networks using discrete penalty Current understanding of hypoxia in glioblastoma multiforme and its response to immunotherapy

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:44.025187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b463c158-0dd0-44a5-ab7f-f82c37ab7761 · outbound

This paper cites Integrative spatial analysis reveals a multi-layered organization of glioblastoma.

Efficient inference of dynamic gene regulatory networks using discrete penalty Integrative spatial analysis reveals a multi-layered organization of glioblastoma

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:44.010195Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 443e49ec-5938-44dd-9e8e-d36aabf0170d · outbound

This paper cites The molecular signatures database hallmark gene set collection.

Efficient inference of dynamic gene regulatory networks using discrete penalty The molecular signatures database hallmark gene set collection

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.995406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b81cfb1c-0082-4310-8210-359d45b149fe · outbound

This paper cites Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity.

Efficient inference of dynamic gene regulatory networks using discrete penalty Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.981022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation de3b33f3-b340-4424-85f0-af13c67135ed · outbound

This paper cites Epithelial-mesenchymal transition in glioblastoma progression.

Efficient inference of dynamic gene regulatory networks using discrete penalty Epithelial-mesenchymal transition in glioblastoma progression

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.966809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 536c9d93-693b-4a39-92e4-8a3763020a26 · outbound

This paper cites Adapt to persist: Glioblas- toma microenvironment and epigenetic regulation on cell plasticity.

Efficient inference of dynamic gene regulatory networks using discrete penalty Adapt to persist: Glioblas- toma microenvironment and epigenetic regulation on cell plasticity

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.951850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation edc475e2-b6dd-4ee7-a04e-98aaf31af7ec · outbound

This paper cites The role of hypoxia in glioblastoma invasion.

Efficient inference of dynamic gene regulatory networks using discrete penalty The role of hypoxia in glioblastoma invasion

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.937263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f72f173a-2a26-4b60-ab11-294f06f9b04c · outbound

This paper cites Cancer genetics and genomics of human FOX family genes.

Efficient inference of dynamic gene regulatory networks using discrete penalty Cancer genetics and genomics of human FOX family genes

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.922861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1fecdf0d-9a12-4403-9105-35e8344060ba · outbound

This paper cites Har- monized single-cell landscape, intercellular crosstalk and tumor architecture of glioblastoma.

Efficient inference of dynamic gene regulatory networks using discrete penalty Har- monized single-cell landscape, intercellular crosstalk and tumor architecture of glioblastoma

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.908481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a1f87c43-43c8-479d-9570-9f6aa473dc92 · outbound

This paper cites Hypoxia coordinates the spatial landscape of myeloid cells within glioblastoma to affect survival.

Efficient inference of dynamic gene regulatory networks using discrete penalty Hypoxia coordinates the spatial landscape of myeloid cells within glioblastoma to affect survival

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.894943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 70316cfd-2c3d-4262-b749-6d8096a7a009 · outbound

This paper cites Multiomics analyses reveal DARS1-AS1/YBX1–controlled posttranscriptional circuits promoting glioblastoma tumori- genesis/radioresistance.

Efficient inference of dynamic gene regulatory networks using discrete penalty Multiomics analyses reveal DARS1-AS1/YBX1–controlled posttranscriptional circuits promoting glioblastoma tumori- genesis/radioresistance

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.880676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d6671194-e42b-4fef-98df-49683db3275d · outbound

This paper cites Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma.

Efficient inference of dynamic gene regulatory networks using discrete penalty Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.865523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8ce15456-520e-4c63-b6ff-d754454212a0 · outbound

This paper cites Chromatin remodeler HELLS maintains glioma stem cells through E2F3 and MYC.

Efficient inference of dynamic gene regulatory networks using discrete penalty Chromatin remodeler HELLS maintains glioma stem cells through E2F3 and MYC

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.848842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 01831a64-1541-474d-ba16-7f2907181b20 · outbound

This paper cites Glioblastoma stem-like cells, metabolic strategy to kill a challenging target.

Efficient inference of dynamic gene regulatory networks using discrete penalty Glioblastoma stem-like cells, metabolic strategy to kill a challenging target

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.833001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 17e16397-9f6b-4fa5-a9a9-a10c51a45992 · outbound

This paper cites Tumor cell plasticity, heterogene- ity, and resistance in crucial microenvironmental niches in glioma.

Efficient inference of dynamic gene regulatory networks using discrete penalty Tumor cell plasticity, heterogene- ity, and resistance in crucial microenvironmental niches in glioma

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.818391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e19791f0-2c6b-40ef-8303-e54e73d33227 · outbound

This paper cites Mechanism of notch signaling pathway in malignant progression of glioblastoma and targeted therapy.

Efficient inference of dynamic gene regulatory networks using discrete penalty Mechanism of notch signaling pathway in malignant progression of glioblastoma and targeted therapy

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.803158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 95a73613-5508-4b57-9b64-8852daaad309 · outbound

This paper cites Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis.

Efficient inference of dynamic gene regulatory networks using discrete penalty Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.787281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 585eb739-bb35-4e08-92d8-b6d674b75fbc · outbound

This paper cites Transcription factor EHF drives cholangio- carcinoma development through transcriptional activation of glioma-associated oncogene homolog 1 and chemokine CCL2.

Efficient inference of dynamic gene regulatory networks using discrete penalty Transcription factor EHF drives cholangio- carcinoma development through transcriptional activation of glioma-associated oncogene homolog 1 and chemokine CCL2

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.767578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c6a04bf1-87da-4037-8fc1-08ae9e137ee3 · outbound

This paper cites Vascular regulation of glioma stem-like cells: a balancing act.

Efficient inference of dynamic gene regulatory networks using discrete penalty Vascular regulation of glioma stem-like cells: a balancing act

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.744693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 06000c8e-1eb3-4c87-a469-1c9b8377d01b · outbound

This paper cites Tumor cell plasticity, heterogeneity, and resistance in crucial microenvironments of glioblastoma.

Efficient inference of dynamic gene regulatory networks using discrete penalty Tumor cell plasticity, heterogeneity, and resistance in crucial microenvironments of glioblastoma

Reference 81

Resolution: verified exact; doi, observed 2026-08-06T11:09:43.203260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dd9fdf0a-cb39-4088-bec1-714102c46f67 · outbound

This paper cites Computational modelling of perivascular- niche dynamics for the optimization of glioblastoma treatment schedules.

Efficient inference of dynamic gene regulatory networks using discrete penalty Computational modelling of perivascular- niche dynamics for the optimization of glioblastoma treatment schedules

Reference 82

Resolution: verified exact; doi, observed 2026-08-06T11:09:43.183626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 13f0c8d8-3ae8-4462-aaa7-67d60cc0e36a · outbound

This paper cites Joint inference of gene regulatory networks from multiple single-cell RNA-seq datasets.

Efficient inference of dynamic gene regulatory networks using discrete penalty Joint inference of gene regulatory networks from multiple single-cell RNA-seq datasets

Reference 83

Resolution: verified exact; doi, observed 2026-08-06T11:09:43.166838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c310e660-f8fa-4dbe-93f9-fb9146f9bc0c · outbound

This paper cites Adaptive penalization improves gene regulatory network inference from single-cell RNA-seq data.

Efficient inference of dynamic gene regulatory networks using discrete penalty Adaptive penalization improves gene regulatory network inference from single-cell RNA-seq data

Reference 84

Resolution: malformed identifier; no resolver link, observed 2026-08-06T11:09:42.783996Z

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Observation d87fe06c-1508-4a00-9d4a-44b9739cd5a2 · outbound

This paper cites Challenges and advances in gene regulatory network infer- ence from single-cell data.

Efficient inference of dynamic gene regulatory networks using discrete penalty Challenges and advances in gene regulatory network infer- ence from single-cell data

Reference 85

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:09:43.693731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8c86496b-6455-4794-87bc-8d826b1f5381 · outbound

This paper cites Single-cell multi-omics reveals regulatory programs in glioblastoma.

Efficient inference of dynamic gene regulatory networks using discrete penalty Single-cell multi-omics reveals regulatory programs in glioblastoma

Reference 86

Resolution: verified exact; doi, observed 2026-08-06T11:09:43.135123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4cd0919e-3994-402e-b6a8-24f84f8a568f · outbound

This paper cites Deep learning for gene regulatory network inference from single-cell data.

Efficient inference of dynamic gene regulatory networks using discrete penalty Deep learning for gene regulatory network inference from single-cell data

Reference 87

Resolution: verified exact; doi, observed 2026-08-06T11:09:43.116213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b42d9dcb-3f3a-401e-8966-d62c1d8a101d · outbound

This paper cites BACH1 as a potential target for immunotherapy in glioblastomas.

Efficient inference of dynamic gene regulatory networks using discrete penalty BACH1 as a potential target for immunotherapy in glioblastomas

Reference 88

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:09:43.607342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dd9cb5ca-a645-4178-a1f0-57b4882e059d · outbound

This paper cites BACH1 promotes temozolomide resistance in glioblastoma.

Efficient inference of dynamic gene regulatory networks using discrete penalty BACH1 promotes temozolomide resistance in glioblastoma

Reference 90

Resolution: verified exact; doi, observed 2026-08-06T11:09:43.086025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4dbdadca-3c97-410e-aa91-56d9c1783682 · outbound

This paper cites Heterogeneity of glioblastoma stem cells in the context of the tumor microenvironment.

Efficient inference of dynamic gene regulatory networks using discrete penalty Heterogeneity of glioblastoma stem cells in the context of the tumor microenvironment

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-06T11:09:42.826877Z

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Observation 3e6c5c85-4c50-42bf-b444-8e78e4faba07 · outbound

This paper cites Glioma Stem Cell Niches in Human Glioblastoma Are Periarteriolar.

Efficient inference of dynamic gene regulatory networks using discrete penalty Glioma Stem Cell Niches in Human Glioblastoma Are Periarteriolar

Reference 92

Resolution: verified exact; doi, observed 2026-08-06T11:09:43.066777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ea04594d-164e-43cf-b5b3-758918e14d17 · outbound

This paper cites Glioblastoma: Microenvironment and Niche Concept.

Efficient inference of dynamic gene regulatory networks using discrete penalty Glioblastoma: Microenvironment and Niche Concept

Reference 93

Resolution: verified exact; raw_fallback, observed 2026-08-06T11:09:43.449553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 344c642f-0b98-4b5a-9450-aa5ededa40eb · outbound

This paper cites Targeting the Glioblastoma Perivascular Stem Cell Niche.

Efficient inference of dynamic gene regulatory networks using discrete penalty Targeting the Glioblastoma Perivascular Stem Cell Niche

Reference 94

Resolution: verified exact; doi, observed 2026-08-06T11:09:43.043303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0b5c2121-961a-4208-836c-66c47a91569e · outbound

This paper cites Glioblastoma: Defining Tumor Niches.

Efficient inference of dynamic gene regulatory networks using discrete penalty Glioblastoma: Defining Tumor Niches

Reference 95

Resolution: verified exact; doi, observed 2026-08-06T11:09:43.025722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1a9112bc-adce-4a1a-87f9-c502140f9888 · outbound

This paper cites Tumor microenvironment in glioblastoma: Current and emerging concepts.

Efficient inference of dynamic gene regulatory networks using discrete penalty Tumor microenvironment in glioblastoma: Current and emerging concepts

Reference 96

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:09:43.351373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 651ac548-71c4-41bd-b7dd-32b71044bca1 · outbound

This paper cites Covariate-adjusted construction of gene regulatory networks using generalized linear models.

Efficient inference of dynamic gene regulatory networks using discrete penalty Covariate-adjusted construction of gene regulatory networks using generalized linear models

Reference 97

Resolution: verified exact; doi, observed 2026-08-06T11:09:43.008154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4b2f7ee5-f4c3-4c36-9b2a-13894fe51666 · outbound

This paper cites Data integration for inferring context-specific gene regulatory networks.

Efficient inference of dynamic gene regulatory networks using discrete penalty Data integration for inferring context-specific gene regulatory networks

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.728353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 45249ccf-19f5-4b89-933d-720741df7f0d · outbound

This paper cites Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells.

Efficient inference of dynamic gene regulatory networks using discrete penalty Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells

Reference 99

Resolution: verified exact; doi, observed 2026-08-06T11:09:42.990284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2c09d414-359d-4229-af08-78432fda76aa · outbound

This paper cites Dissecting cell identity via network-based in silico perturbation of single-cell transcriptomes.

Efficient inference of dynamic gene regulatory networks using discrete penalty Dissecting cell identity via network-based in silico perturbation of single-cell transcriptomes

Reference 100

Resolution: malformed identifier; no resolver link, observed 2026-08-06T11:09:42.874756Z

Source-reported events for the cited work

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Observation 2e699c8b-b1a8-4051-bf62-91c12d056d92 · outbound

This paper cites SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks.

Efficient inference of dynamic gene regulatory networks using discrete penalty SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:43.710681Z

Source-reported events for the cited work

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