Paper Citation Record · LEDGER

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites

As of 11 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2502.01311.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.01311 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:44:41.753306Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:44:41.642006Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-09T15:44:41.794594Z

Reference resolution

37 of 37 outbound references displayed

verified exact1
verified fuzzy31
unresolved4
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b6077cb3-6c85-480a-8f05-daa282b4d5b7 · outbound

This paper cites Sequences are divided into tokens of size k and provided as initial input to DNABERT.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Sequences are divided into tokens of size k and provided as initial input to DNABERT

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:42.084682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f142e265-3944-4bf6-8cf2-eaa2a8f8ac1e · outbound

This paper cites an unresolved cited work.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-09T15:44:42.076498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3bc3db1b-5f8a-451f-8fd7-4f1088cf3351 · outbound

This paper cites As given in [26], the order of the two submodules affects the overall performance and in their work they have considered Channel-Spatial module.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites As given in [26], the order of the two submodules affects the overall performance and in their work they have considered Channel-Spatial module

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:42.068517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 179a98de-d4f2-4e6a-9c5a-f61df4d8ec87 · outbound

This paper cites As shown in Figure 1, three separate convolutions Conv 4,1, Conv 4,2 and Conv 4,3 are applied to each input channel of the feature matrix M2.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites As shown in Figure 1, three separate convolutions Conv 4,1, Conv 4,2 and Conv 4,3 are applied to each input channel of the feature matrix M2

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:42.052105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ee4c6528-d031-44c2-9d3e-311f06caa9d8 · outbound

This paper cites an unresolved cited work.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-09T15:44:42.060220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c061a798-d5e0-4f1a-b690-70bd63133355 · outbound

This paper cites BERT-TFBS: a novel bert- based model for predicting transcription factor binding sites by transfer learning,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites BERT-TFBS: a novel bert- based model for predicting transcription factor binding sites by transfer learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.995398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 797f6577-f09e-45be-bc13-531f855555b0 · outbound

This paper cites This has the effect of parallel attention [27] in the output module, thereby exploiting both MCBAM and MSCA modules.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites This has the effect of parallel attention [27] in the output module, thereby exploiting both MCBAM and MSCA modules

Reference 7

Resolution: malformed identifier; raw_fallback, observed 2026-08-09T15:44:42.043829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2493f773-0ea9-41b0-8ab9-0d0c32c3c8a9 · outbound

This paper cites Assessing computational tools for the discovery of transcription factor binding sites,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Assessing computational tools for the discovery of transcription factor binding sites,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:42.035672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8b369047-b6c9-4490-b520-2e2029600726 · outbound

This paper cites Tfbstools: an r/bioconductor package for transcription factor binding site analysis,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Tfbstools: an r/bioconductor package for transcription factor binding site analysis,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:42.027744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1df0a2d7-788c-4bda-b252-caca2e2ed864 · outbound

This paper cites A review of dna-binding proteins prediction methods,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites A review of dna-binding proteins prediction methods,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:42.019603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ab10f1a5-44cf-44ef-be0c-b33a1b797eab · outbound

This paper cites TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-09T15:44:41.800032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4bdedb73-a166-4c4b-87ae-81cd16da470e · outbound

This paper cites Transcription factors: An overview,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Transcription factors: An overview,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:42.011691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 18a6885c-dc7d-44c3-adb8-0b40b565bc47 · outbound

This paper cites Too many transcription factors: positive and negative inter- actions,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Too many transcription factors: positive and negative inter- actions,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:42.003628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 01ec291a-09c9-40b1-bf38-756f8364a89c · outbound

This paper cites ChIPBase v3.0: the encyclope- dia of transcriptional regulations of non-coding rnas and protein-coding genes,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites ChIPBase v3.0: the encyclope- dia of transcriptional regulations of non-coding rnas and protein-coding genes,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.987380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 567211e6-2b7d-4897-82af-1db565303937 · outbound

This paper cites DNA motif elucidation using belief propagation,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites DNA motif elucidation using belief propagation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.979152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aa1e84f4-867e-4d9e-9077-1e759a8e707d · outbound

This paper cites A Biophysical Approach to Transcription Factor Binding Site Discovery,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites A Biophysical Approach to Transcription Factor Binding Site Discovery,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.971351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d2c89a3a-5721-4b88-a715-9ec8cce01c22 · outbound

This paper cites Identification of yeast transcriptional regula- tion networks using multivariate random forests,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Identification of yeast transcriptional regula- tion networks using multivariate random forests,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.963731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1a80d1c4-f27a-48eb-9792-d10fb911e93f · outbound

This paper cites A flexible integrative approach based on random forest improves prediction of transcription factor binding sites,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites A flexible integrative approach based on random forest improves prediction of transcription factor binding sites,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.955395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 22a908bc-1ea9-499e-a334-32cbd515e080 · outbound

This paper cites Predicting effects of noncoding variants with deep learning–based sequence model,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Predicting effects of noncoding variants with deep learning–based sequence model,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.947347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ff1c8657-3c72-4f88-8981-769ef49f1373 · outbound

This paper cites Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.939321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 51b61cf0-82f9-4fd1-86fc-3cdd8723938a · outbound

This paper cites DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.930937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 530c9d00-3650-41f3-929b-6e1ce813e367 · outbound

This paper cites DeepSite: bidirectional LSTM and CNN models for predicting DNA–protein binding,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites DeepSite: bidirectional LSTM and CNN models for predicting DNA–protein binding,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.922776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bd36110d-5544-4333-bd08-79f314dcd107 · outbound

This paper cites Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.914036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2bed6a74-cf56-4497-9ee1-ef363404e20e · outbound

This paper cites A novel convolution attention model for predicting transcription factor binding sites by combination of sequence and shape,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites A novel convolution attention model for predicting transcription factor binding sites by combination of sequence and shape,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.905980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 191549f5-f284-40c2-8cfd-6b94f3574c06 · outbound

This paper cites Deepstf: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Deepstf: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.897782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 08f8e408-25dd-442c-8d6f-3f49de65bf81 · outbound

This paper cites SAResNet: self-attention residual network for predicting DNA-protein binding,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites SAResNet: self-attention residual network for predicting DNA-protein binding,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.889225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 21edb9d7-987a-4fbd-b83f-cd72411813c2 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T15:44:41.723615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 657079c3-ceb5-4a11-8bc2-9249a0289b3a · outbound

This paper cites DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.880915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3048dd34-5df2-49f5-beb9-9952333b742f · outbound

This paper cites ProteinBERT: a universal deep-learning model of protein sequence and function,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites ProteinBERT: a universal deep-learning model of protein sequence and function,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.872498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 619f89d6-a33e-4e4b-8c70-087da8a98c54 · outbound

This paper cites Predicting transcription factor binding sites with deep learning,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Predicting transcription factor binding sites with deep learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.863264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0c973380-24d9-4e76-bd0d-4cc9ccfeebe7 · outbound

This paper cites An integrated encyclopedia of dna elements in the human genome,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites An integrated encyclopedia of dna elements in the human genome,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.854373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 45651426-21c7-405d-9f0f-e8ca0cb55f67 · outbound

This paper cites Convolutional neural net- work architectures for predicting dna–protein binding,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Convolutional neural net- work architectures for predicting dna–protein binding,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.844916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 328d2d2b-2bb2-46cb-a5be-58c38ea1d972 · outbound

This paper cites CBAM: Convolutional block attention module,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites CBAM: Convolutional block attention module,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.836373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7b8f56a8-ecbb-4436-826c-037c14ed9884 · outbound

This paper cites Integrating multiple visual attention mecha- nisms in deep neural networks,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Integrating multiple visual attention mecha- nisms in deep neural networks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.827699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6137d2c1-88d1-4b81-85fc-be5c0d731d55 · outbound

This paper cites Predicting in-vitro transcription factor binding sites using dna sequence + shape,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Predicting in-vitro transcription factor binding sites using dna sequence + shape,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.818488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2159d1fb-d677-46fb-bf5b-27396476162d · outbound

This paper cites Predicting transcription factor binding sites using dna shape features based on shared hybrid deep learning architecture,.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites Predicting transcription factor binding sites using dna shape features based on shared hybrid deep learning architecture,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:44:41.809907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fbf27ed7-a175-4a62-9cde-4feea7ad5018 · outbound

This paper cites DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome.

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-09T15:44:41.753306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation ab10f1a5-44cf-44ef-be0c-b33a1b797eab · inbound