Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing | Paper Citation Record

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This paper cites J., Bambrick, J., et al.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing J., Bambrick, J., et al

Reference 1

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This paper cites and Mann, M.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing and Mann, M

Reference 2

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This paper cites Non-Autoregressive Translation by Learning Target Categorical Codes.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Non-Autoregressive Translation by Learning Target Categorical Codes

Reference 3

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This paper cites J., and Cheng, J.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing J., and Cheng, J

Reference 4

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This paper cites Context-Aware Cross-Attention for Non-Autoregressive Translation.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Context-Aware Cross-Attention for Non-Autoregressive Translation

Reference 5

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This paper cites Progressive Multi-Granularity Training for Non-Autoregressive Translation.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Progressive Multi-Granularity Training for Non-Autoregressive Translation

Reference 6

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This paper cites Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation

Reference 7

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This paper cites B., Williams, W., Beljouw, S.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing B., Williams, W., Beljouw, S

Reference 8

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This paper cites Deep multimodal networks for m-type star classification with paired spectrum and photometric image.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Deep multimodal networks for m-type star classification with paired spectrum and photometric image

Reference 9

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This paper cites Mask-Predict: Parallel Decoding of Conditional Masked Language Models.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Mask-Predict: Parallel Decoding of Conditional Masked Language Models

Reference 10

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This paper cites Aligned cross entropy for non-autoregressive machine translation.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Aligned cross entropy for non-autoregressive machine translation

Reference 11

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This paper cites and Jaitly, N.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing and Jaitly, N

Reference 12

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This paper cites Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

Reference 13

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This paper cites Non-Autoregressive Neural Machine Translation.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Non-Autoregressive Neural Machine Translation

Reference 14

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This paper cites Non-autoregressive neural machine translation with enhanced decoder input.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Non-autoregressive neural machine translation with enhanced decoder input

Reference 15

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This paper cites Jointly masked sequence-to-sequence model for non-autoregressive neural machine translation.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Jointly masked sequence-to-sequence model for non-autoregressive neural machine translation

Reference 16

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This paper cites J., Oktay, D., Lin, Z., Verkuil, R., Tran, V.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing J., Oktay, D., Lin, Z., Verkuil, R., Tran, V

Reference 17

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This paper cites R., Mou, L., and Li, L.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing R., Mou, L., and Li, L

Reference 18

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This paper cites Directed acyclic transformer for non-autoregressive machine translation.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Directed acyclic transformer for non-autoregressive machine translation

Reference 19

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This paper cites S., Perez, F., and Volkovs, M.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing S., Perez, F., and Volkovs, M

Reference 20

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This paper cites ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing

Reference 21

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This paper cites Contranovo: A contrastive learning approach to enhance de novo peptide sequencing.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Contranovo: A contrastive learning approach to enhance de novo peptide sequencing

Reference 22

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This paper cites Highly accurate protein structure prediction with alphafold.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Highly accurate protein structure prediction with alphafold

Reference 23

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This paper cites W., Chuangsuwanich, E., and Sriswasdi, S.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing W., Chuangsuwanich, E., and Sriswasdi, S

Reference 24

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This paper cites Adam: A Method for Stochastic Optimization.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Adam: A Method for Stochastic Optimization

Reference 25

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This paper cites Deep learning.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Deep learning

Reference 26

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This paper cites Evolutionary-scale prediction of atomic-level protein structure with a language model.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Evolutionary-scale prediction of atomic-level protein structure with a language model

Reference 27

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This paper cites Accurate de novo peptide sequencing using fully convolutional neural networks.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Accurate de novo peptide sequencing using fully convolutional neural networks

Reference 28

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This paper cites A character-level length-control algorithm for non-autoregressive sentence summarization.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing A character-level length-control algorithm for non-autoregressive sentence summarization

Reference 29

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This paper cites Peaks: powerful software for peptide de novo sequencing by tandem mass spectrometry.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Peaks: powerful software for peptide de novo sequencing by tandem mass spectrometry

Reference 30

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This paper cites FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Reference 31

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This paper cites Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model

Reference 32

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Unresolved cited work

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Unresolved cited work

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This paper cites Glancing Transformer for Non-Autoregressive Neural Machine Translation.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Glancing Transformer for Non-Autoregressive Neural Machine Translation

Reference 35

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This paper cites H., Xin, L., Chen, X., Li, M., Shan, B., and Ghodsi, A.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing H., Xin, L., Chen, X., Li, M., Shan, B., and Ghodsi, A

Reference 36

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This paper cites Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing

Reference 37

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This paper cites Non-Autoregressive Machine Translation with Latent Alignments.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Non-Autoregressive Machine Translation with Latent Alignments

Reference 38

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Simple and effective masked diffusion language models

Reference 39

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Step-unrolled Denoising Autoencoders for Text Generation

Reference 40

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This paper cites Minimizing the bag-of-ngrams difference for non-autoregressive neural machine translation.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Minimizing the bag-of-ngrams difference for non-autoregressive neural machine translation

Reference 41

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This paper cites One Reference Is Not Enough: Diverse Distillation with Reference Selection for Non-Autoregressive Translation.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing One Reference Is Not Enough: Diverse Distillation with Reference Selection for Non-Autoregressive Translation

Reference 42

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This paper cites Latent-variable non-autoregressive neural machine translation with deterministic inference using a delta posterior.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Latent-variable non-autoregressive neural machine translation with deterministic inference using a delta posterior

Reference 43

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This paper cites AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate

Reference 44

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This paper cites Insertion transformer: Flexible sequence generation via insertion operations.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Insertion transformer: Flexible sequence generation via insertion operations

Reference 45

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This paper cites H., Zhang, X., Xin, L., Shan, B., and Li, M.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing H., Zhang, X., Xin, L., Shan, B., and Li, M

Reference 46

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing S., Cha, S

Reference 47

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This paper cites Non-autoregressive machine translation with auxiliary regularization.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Non-autoregressive machine translation with auxiliary regularization

Reference 48

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This paper cites AdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional Mutual Information.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing AdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional Mutual Information

Reference 50

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Unresolved cited work

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This paper cites A survey on non-autoregressive generation for neural machine translation and beyond.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing A survey on non-autoregressive generation for neural machine translation and beyond

Reference 52

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This paper cites pnovo 3: precise de novo peptide sequencing using a learning-to-rank framework.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing pnovo 3: precise de novo peptide sequencing using a learning-to-rank framework

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This paper cites Introducing -helixnovo for practical large-scale de novo peptide sequencing.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Introducing -helixnovo for practical large-scale de novo peptide sequencing

Reference 54

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Unresolved cited work

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing E., Bittremieux, W., Melendez, C

Reference 56

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing -primenovo: An accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing

Reference 58

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing -primenovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing

Reference 59

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Structure-informed language models are protein designers

Reference 60

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Improving Non-autoregressive Neural Machine Translation with Monolingual Data

Reference 61

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics

Reference 62

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing pdeep: predicting ms/ms spectra of peptides with deep learning

Reference 63

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Non-autoregressive neural machine translation with consistency regularization optimized variational framework

Reference 64

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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing write newline

Reference 65

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