---
title: "Bioinformatics 2024 — all accepted papers — the most cited first"
description: "All 788 papers of Bioinformatics 2024 · page 1 of 8, the most cited first; those with our summary link to it."
canonical: "https://research.elamur.ai/free/en/venues/bioinformatics/2024/papers/"
---
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# Bioinformatics 2024: all accepted papers

788 papers · page 1 of 8 · the most cited first · [← Bioinformatics 2024](https://md.research.elamur.ai/free/en/venues/bioinformatics/2024/)

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1. **[ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries ↗](https://doi.org/10.1093/bioinformatics/btae416)**Kyle Swanson, Parker Walther, Jeremy Leitz et al.*372 citations*
2. **[RCSB protein Data Bank: exploring protein 3D similarities via comprehensive structural alignments ↗](https://doi.org/10.1093/bioinformatics/btae370)**Sebastian Bittrich, Joan Segura, José M. Duarte et al.*211 citations*
3. **[GeneGPT: augmenting large language models with domain tools for improved access to biomedical information](https://md.research.elamur.ai/free/en/papers/jin-2023-genegpt-augmenting-large-language-models/)**Qiao Jin, Yifan Yang, Qingyu Chen et al.*174 citations · our summary*
4. **[Biomedical knowledge graph-optimized prompt generation for large language models ↗](https://doi.org/10.1093/bioinformatics/btae560)**Karthik Soman, Peter W. Rose, John H Morris et al.*136 citations*
5. **[Improving medical reasoning through retrieval and self-reflection with retrieval-augmented large language models](https://md.research.elamur.ai/free/en/papers/jeong-2024-improving-medical-reasoning-through-retrieval/)**Minbyul Jeong, Jiwoong Sohn, Mujeen Sung et al.*131 citations · our summary*
6. **[BCFtools/liftover: an accurate and comprehensive tool to convert genetic variants across genome assemblies ↗](https://doi.org/10.1093/bioinformatics/btae038)**Giulio Genovese, Nicole B. Rockweiler, Bryan R. Gorman et al.*120 citations*
7. **[Conumee 2.0: enhanced copy-number variation analysis from DNA methylation arrays for humans and mice ↗](https://doi.org/10.1093/bioinformatics/btae029)**Bjarne Daenekas, Eilís Pérez, Fabio Boniolo et al.*120 citations*
8. **[Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning ↗](https://doi.org/10.1093/bioinformatics/btae104)**John Harry Caufield, Harshad Hegde, Vincent Emonet et al.*120 citations*
9. **[DAVID Ortholog: an integrative tool to enhance functional analysis through orthologs ↗](https://doi.org/10.1093/bioinformatics/btae615)**Brad T. Sherman, Ganesh Prabhakar Panzade, Tomozumi Imamichi et al.*110 citations*
10. **[KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models ↗](https://doi.org/10.1093/bioinformatics/btae353)**Nicholas Matsumoto, Jay Moran, Hyun‐Jun Choi et al.*108 citations*
11. **[selscan 2.0: scanning for sweeps in unphased data ↗](https://doi.org/10.1093/bioinformatics/btae006)**Zachary A. Szpiech*106 citations*
12. **[Parsnp 2.0: scalable core-genome alignment for massive microbial datasets ↗](https://doi.org/10.1093/bioinformatics/btae311)**Bryce L. Kille, Michael Nute, Victor Huang et al.*105 citations*
13. **[Advancing entity recognition in biomedicine via instruction tuning of large language models ↗](https://doi.org/10.1093/bioinformatics/btae163)**Vipina K. Keloth, Yan Hu, Qianqian Xie et al.*104 citations*
14. **[Bin2cell reconstructs cells from high resolution Visium HD data ↗](https://doi.org/10.1093/bioinformatics/btae546)**Krzysztof Polański, Raquel Bartolomé-Casado, Ioannis Sarropoulos et al.*100 citations*
15. **[DockQ v2: improved automatic quality measure for protein multimers, nucleic acids, and small molecules ↗](https://doi.org/10.1093/bioinformatics/btae586)**Claudio Mirabello, Björn Wallner*95 citations*
16. **[DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm ↗](https://doi.org/10.1093/bioinformatics/btae305)**Matee Ullah, Shahid Akbar, Ali Raza et al.*74 citations*
17. **[Predicting the subcellular location of prokaryotic proteins with DeepLocPro ↗](https://doi.org/10.1093/bioinformatics/btae677)**Jaime Moreno, Henrik Nielsen, Ole Winther et al.*74 citations*
18. **[POCP-nf: an automatic Nextflow pipeline for calculating the percentage of conserved proteins in bacterial taxonomy ↗](https://doi.org/10.1093/bioinformatics/btae175)**Martin Hölzer*72 citations*
19. **[Addressing the antibody germline bias and its effect on language models for improved antibody design ↗](https://doi.org/10.1093/bioinformatics/btae618)**Tobias Hegelund Olsen, Iain H. Moal, Charlotte Mary Deane*67 citations*
20. **[lefser: implementation of metagenomic biomarker discovery tool, LEfSe, in R ↗](https://doi.org/10.1093/bioinformatics/btae707)**Asya Khleborodova, Samuel David Gamboa-Tuz, Marcel Ramos et al.*66 citations*
21. **[TemStaPro: protein thermostability prediction using sequence representations from protein language models ↗](https://doi.org/10.1093/bioinformatics/btae157)**Ieva Pudžiuvelytė, Kliment Olechnovič, Eglė Godliauskaitė et al.*65 citations*
22. **[DTI-LM: language model powered drug–target interaction prediction ↗](https://doi.org/10.1093/bioinformatics/btae533)**Khandakar Tanvir Ahmed, Md. Istiaq Ansari, Wei Zhang*62 citations*
23. **[ModDotPlot—rapid and interactive visualization of tandem repeats ↗](https://doi.org/10.1093/bioinformatics/btae493)**Alexander P. Sweeten, Michael C. Schatz, Adam M. Phillippy*62 citations*
24. **[iNGNN-DTI: prediction of drug–target interaction with interpretable nested graph neural network and pretrained molecule models ↗](https://doi.org/10.1093/bioinformatics/btae135)**Yan Sun, Yan Yi Li, Carson Kai-Sang Leung et al.*59 citations*
25. **[RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification ↗](https://doi.org/10.1093/bioinformatics/btae084)**Jun Xue, Bingyi Wang, Hongchao Ji et al.*56 citations*
26. **[Bioframe: operations on genomic intervals in Pandas dataframes ↗](https://doi.org/10.1093/bioinformatics/btae088)**Open2C, Nezar A. Abdennur, Geoffrey Fudenberg et al.*53 citations*
27. **[Chainsaw: protein domain segmentation with fully convolutional neural networks ↗](https://doi.org/10.1093/bioinformatics/btae296)**Jude Wells, Alex Hawkins‐Hooker, Nicola Bordin et al.*53 citations*
28. **[scMAE: a masked autoencoder for single-cell RNA-seq clustering ↗](https://doi.org/10.1093/bioinformatics/btae020)**Zhaoyu Fang, Ruiqing Zheng, Min Li*50 citations*
29. **[HGTDR: Advancing drug repurposing with heterogeneous graph transformers ↗](https://doi.org/10.1093/bioinformatics/btae349)**Ali Gharizadeh, Karim Abbasi, Amin Ghareyazi et al.*49 citations*
30. **[Effect of tokenization on transformers for biological sequences ↗](https://doi.org/10.1093/bioinformatics/btae196)**Edo Dotan, Gal Jaschek, Tal Pupko et al.*48 citations*
31. **[Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data ↗](https://doi.org/10.1093/bioinformatics/btae316)**Xiao Fan Liu, Yuhuan Tao, Zilin Cai et al.*48 citations*
32. **[wgd v2: a suite of tools to uncover and date ancient polyploidy and whole-genome duplication ↗](https://doi.org/10.1093/bioinformatics/btae272)**Hengchi Chen, Arthur Zwaenepoel, Yves Van de Peer*47 citations*
33. **[Drug–target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism ↗](https://doi.org/10.1093/bioinformatics/btae346)**Wei Song, Lewen Xu, Chenguang Han et al.*46 citations*
34. **[scDiffusion: conditional generation of high-quality single-cell data using diffusion model ↗](https://doi.org/10.1093/bioinformatics/btae518)**Erpai Luo, Minsheng Hao, Lei Wei et al.*46 citations*
35. **[Revisiting drug–protein interaction prediction: a novel global–local perspective ↗](https://doi.org/10.1093/bioinformatics/btae271)**Zhecheng Zhou, Qingquan Liao, Jinhang Wei et al.*44 citations*
36. **[grenedalf: population genetic statistics for the next generation of pool sequencing ↗](https://doi.org/10.1093/bioinformatics/btae508)**Lucas Czech, Jeffrey P. Spence, Moisés Expósito‐Alonso*44 citations*
37. **[Tiberius: end-to-end deep learning with an HMM for gene prediction ↗](https://doi.org/10.1093/bioinformatics/btae685)**Lars Gabriel, Felix Becker, Katharina J. Hoff et al.*43 citations*
38. **[HiPhase: jointly phasing small, structural, and tandem repeat variants from HiFi sequencing ↗](https://doi.org/10.1093/bioinformatics/btae042)**James Holt, Christopher T. Saunders, William J. Rowell et al.*41 citations*
39. **[PMIpred: a physics-informed web server for quantitative protein–membrane interaction prediction ↗](https://doi.org/10.1093/bioinformatics/btae069)**Niek van Hilten, Nino Verwei, Jeroen Methorst et al.*41 citations*
40. **[Panacus: fast and exact pangenome growth and core size estimation ↗](https://doi.org/10.1093/bioinformatics/btae720)**Luca Parmigiani, Erik P. Garrison, Jens Stoye et al.*41 citations*
41. **[CryoTransformer: a transformer model for picking protein particles from cryo-EM micrographs ↗](https://doi.org/10.1093/bioinformatics/btae109)**Ashwin Dhakal, Rajan Gyawali, Liguo Wang et al.*40 citations*
42. **[Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction ↗](https://doi.org/10.1093/bioinformatics/btae155)**Saisai Sun, Lin Gao*39 citations*
43. **[Molecular property prediction based on graph structure learning ↗](https://doi.org/10.1093/bioinformatics/btae304)**Bangyi Zhao, Weixia Xu, Jihong Guan et al.*39 citations*
44. **[Towards the accurate modelling of antibody−antigen complexes from sequence using machine learning and information-driven docking ↗](https://doi.org/10.1093/bioinformatics/btae583)**Marco Giulini, Constantin Schneider, Daniel Cutting et al.*39 citations*
45. **[PocketDTA: an advanced multimodal architecture for enhanced prediction of drug−target affinity from 3D structural data of target binding pockets ↗](https://doi.org/10.1093/bioinformatics/btae594)**Long Zhao, Hongmei Wang, Shaoping Shi*38 citations*
46. **[The GenoPred pipeline: a comprehensive and scalable pipeline for polygenic scoring ↗](https://doi.org/10.1093/bioinformatics/btae551)**Oliver Pain, Ammar Al‐Chalabi, Cathryn M. Lewis*38 citations*
47. **[ABodyBuilder3: improved and scalable antibody structure predictions ↗](https://doi.org/10.1093/bioinformatics/btae576)**Henry Kenlay, Frédéric A. Dreyer, Daniel Cutting et al.*35 citations*
48. **[VarChat: the generative AI assistant for the interpretation of human genomic variations ↗](https://doi.org/10.1093/bioinformatics/btae183)**Federica De Paoli, Silvia Berardelli, Ivan Limongelli et al.*35 citations*
49. **[MOIRE: a software package for the estimation of allele frequencies and effective multiplicity of infection from polyallelic data ↗](https://doi.org/10.1093/bioinformatics/btae619)**Maxwell Murphy, Bryan Greenhouse*34 citations*
50. **[MetaCerberus: distributed highly parallelized HMM-based processing for robust functional annotation across the tree of life ↗](https://doi.org/10.1093/bioinformatics/btae119)**Jose L. Figueroa, Eliza Dhungel, Madeline Bellanger et al.*34 citations*
51. **[CodonBERT: a BERT-based architecture tailored for codon optimization using the cross-attention mechanism ↗](https://doi.org/10.1093/bioinformatics/btae330)**Zilin Ren, Lili Jiang, Yaxin Di et al.*33 citations*
52. **[Expert-guided protein language models enable accurate and blazingly fast fitness prediction ↗](https://doi.org/10.1093/bioinformatics/btae621)**Céline Marquet, Julius Schlensok, Marina Abakarova et al.*33 citations*
53. **[Exploring gene content with pangene graphs ↗](https://doi.org/10.1093/bioinformatics/btae456)**Heng Li, Maximillian G. Marin, Maha Reda Farhat*33 citations*
54. **[Representations of lipid nanoparticles using large language models for transfection efficiency prediction ↗](https://doi.org/10.1093/bioinformatics/btae342)**Saeed Moayedpour, Jonathan Broadbent, Saleh Riahi et al.*33 citations*
55. **[Accurate and transferable drug–target interaction prediction with DrugLAMP ↗](https://doi.org/10.1093/bioinformatics/btae693)**Zhengchao Luo, Wei Wu, Qichen Sun et al.*32 citations*
56. **[AutoPeptideML: a study on how to build more trustworthy peptide bioactivity predictors ↗](https://doi.org/10.1093/bioinformatics/btae555)**Raúl Fernández-Díaz, Rodrigo Cossio-Pérez, Clement Agoni et al.*32 citations*
57. **[Flexiplex: a versatile demultiplexer and search tool for omics data ↗](https://doi.org/10.1093/bioinformatics/btae102)**Oliver Cheng, Min Hao Ling, Changqing Wang et al.*32 citations*
58. **[Surface-based multimodal protein–ligand binding affinity prediction ↗](https://doi.org/10.1093/bioinformatics/btae413)**Shiyu Xu, Lian Shen, Menglong Zhang et al.*32 citations*
59. **[Unravelling reference bias in ancient DNA datasets ↗](https://doi.org/10.1093/bioinformatics/btae436)**Stephanie Dolenz, Tom van der Valk, Chenyu Jin et al.*32 citations*
60. **[InstaPrism: an R package for fast implementation of BayesPrism ↗](https://doi.org/10.1093/bioinformatics/btae440)**Mengying Hu, Maria D. Chikina*31 citations*
61. **[Phenotype prediction from single-cell RNA-seq data using attention-based neural networks ↗](https://doi.org/10.1093/bioinformatics/btae067)**Yuzhen Mao, Yen‐Yi Lin, Nelson K.Y. Wong et al.*31 citations*
62. **[ViraLM: empowering virus discovery through the genome foundation model ↗](https://doi.org/10.1093/bioinformatics/btae704)**Cheng Peng, Jiayu Shang, Jiaojiao Guan et al.*31 citations*
63. **[BioCoder: a benchmark for bioinformatics code generation with large language models ↗](https://doi.org/10.1093/bioinformatics/btae230)**Xiangru Tang, Bill Qian, Rick Gao et al.*30 citations*
64. **[Integrated convolution and self-attention for improving peptide toxicity prediction ↗](https://doi.org/10.1093/bioinformatics/btae297)**Shihu Jiao, Xiucai Ye, Tetsuya Sakurai et al.*30 citations*
65. **[NanoCon: contrastive learning-based deep hybrid network for nanopore methylation detection ↗](https://doi.org/10.1093/bioinformatics/btae046)**Chenglin Yin, Ruheng Wang, Jianbo Qiao et al.*30 citations*
66. **[OligoFormer: an accurate and robust prediction method for siRNA design ↗](https://doi.org/10.1093/bioinformatics/btae577)**Yilan Bai, Haochen Zhong, Taiwei Wang et al.*30 citations*
67. **[ScType enables fast and accurate cell type identification from spatial transcriptomics data ↗](https://doi.org/10.1093/bioinformatics/btae426)**Kristen Michelle Nader, Mısra Tascı, Aleksandr Ianevski et al.*30 citations*
68. **[pyaging : a Python-based compendium of GPU-optimized aging clocks ↗](https://doi.org/10.1093/bioinformatics/btae200)**Lucas Paulo de Lima Camillo*30 citations*
69. **[AleRax: a tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer, and loss ↗](https://doi.org/10.1093/bioinformatics/btae162)**Benoit Morel, Tom A. Williams, Alexandros Stamatakis et al.*29 citations*
70. **[HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification ↗](https://doi.org/10.1093/bioinformatics/btae159)**Haohua Wang, Kai Yan Lin, Qiang Zhang et al.*29 citations*
71. **[M-Ionic: prediction of metal-ion-binding sites from sequence using residue embeddings ↗](https://doi.org/10.1093/bioinformatics/btad782)**Aditi Shenoy, Yogesh Kalakoti, Durai Sundar et al.*29 citations*
72. **[STRING-ing together protein complexes: corpus and methods for extracting physical protein interactions from the biomedical literature ↗](https://doi.org/10.1093/bioinformatics/btae552)**Farrokh Mehryary, Katerina C. Nastou, Tomoko Ohta et al.*29 citations*
73. **[CryptoBench: cryptic protein–ligand binding sites dataset and benchmark ↗](https://doi.org/10.1093/bioinformatics/btae745)**Vít Škrhák, Marián Novotný, Christos P Feidakis et al.*28 citations*
74. **[GCI: a continuity inspector for complete genome assembly ↗](https://doi.org/10.1093/bioinformatics/btae633)**Quanyu Chen, Chentao Yang, Guojie Zhang et al.*28 citations*
75. **[Interpretable deep learning in single-cell omics ↗](https://doi.org/10.1093/bioinformatics/btae374)**Manoj M Wagle, Siqu Long, Carissa Chen et al.*28 citations*
76. **[PGAT-ABPp: harnessing protein language models and graph attention networks for antibacterial peptide identification with remarkable accuracy ↗](https://doi.org/10.1093/bioinformatics/btae497)**Yuelei Hao, Xuyang Liu, Haohao Fu et al.*28 citations*
77. **[Rapid generation of high-quality structure figures for publication with PyMOL-PUB ↗](https://doi.org/10.1093/bioinformatics/btae139)**Yuting Chen, Haoling Zhang, Wen Wang et al.*28 citations*
78. **[RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models ↗](https://doi.org/10.1093/bioinformatics/btae259)**Han Huang, Ziqian Lin, Dongchen He et al.*28 citations*
79. **[TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records ↗](https://doi.org/10.1093/bioinformatics/btae264)**Mohammad Al Olaimat, Serdar Bozdag, Alzheimer’s Disease Neuroimaging Initiative*28 citations*
80. **[scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism ↗](https://doi.org/10.1093/bioinformatics/btae265)**Qun Jiang, Shengquan Chen, Xiaoyang Chen et al.*28 citations*
81. **[DeepRSMA: a cross-fusion-based deep learning method for RNA–small molecule binding affinity prediction ↗](https://doi.org/10.1093/bioinformatics/btae678)**Zhijian Huang, Yucheng Wang, Song Chen et al.*27 citations*
82. **[FMCA-DTI: a fragment-oriented method based on a multihead cross attention mechanism to improve drug–target interaction prediction ↗](https://doi.org/10.1093/bioinformatics/btae347)**Qi Zhang, Le Zuo, Ying Ren et al.*27 citations*
83. **[PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction ↗](https://doi.org/10.1093/bioinformatics/btae058)**Kexin Shi, Yuanpeng Xiong, Yu Wang et al.*27 citations*
84. **[SeedMatchR: identify off-target effects mediated by siRNA seed regions in RNA-seq experiments ↗](https://doi.org/10.1093/bioinformatics/btae011)**Tareian A. Cazares, Richard E. Higgs, Jibo Wang et al.*27 citations*
85. **[DEAttentionDTA: protein–ligand binding affinity prediction based on dynamic embedding and self-attention ↗](https://doi.org/10.1093/bioinformatics/btae319)**Xiying Chen, Jinsha Huang, Tianqiao Shen et al.*26 citations*
86. **[MINDG: a drug–target interaction prediction method based on an integrated learning algorithm ↗](https://doi.org/10.1093/bioinformatics/btae147)**Hailong Yang, Yue Chen, Yun Zuo et al.*26 citations*
87. **[Seq-InSite: sequence supersedes structure for protein interaction site prediction ↗](https://doi.org/10.1093/bioinformatics/btad738)**SeyedMohsen Hosseini, Geoffrey Brian Golding, Lucian Ilie*26 citations*
88. **[Attention-based approach to predict drug–target interactions across seven target superfamilies ↗](https://doi.org/10.1093/bioinformatics/btae496)**Aron Schulman, Juho Rousu, Tero A. Aittokallio et al.*25 citations*
89. **[Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning ↗](https://doi.org/10.1093/bioinformatics/btae169)**Fuqun Chen, Guanhua Zou, Yongxian Wu et al.*25 citations*
90. **[DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 on-target editing efficiency in specific cellular contexts ↗](https://doi.org/10.1093/bioinformatics/btae481)**Shai Elkayam, Ido Tziony, Yaron Orenstein*25 citations*
91. **[SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals ↗](https://doi.org/10.1093/bioinformatics/btae295)**Wenmin Zhang, Tianyuan Lu, ROBERT M. SLADEK et al.*25 citations*
92. **[scNODE : generative model for temporal single cell transcriptomic data prediction ↗](https://doi.org/10.1093/bioinformatics/btae393)**Jiaqi Zhang, Erica Larschan, Jeremy Bigness et al.*25 citations*
93. **[A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data ↗](https://doi.org/10.1093/bioinformatics/btae400)**Qi Jiang, Lin Wan*24 citations*
94. **[BWT construction and search at the terabase scale ↗](https://doi.org/10.1093/bioinformatics/btae717)**Heng Li*24 citations*
95. **[FuncFetch: an LLM-assisted workflow enables mining thousands of enzyme–substrate interactions from published manuscripts ↗](https://doi.org/10.1093/bioinformatics/btae756)**Nathaniel S. S. Smith, Xinyu Yuan, Chesney Melissinos et al.*24 citations*
96. **[GPro: generative AI-empowered toolkit for promoter design ↗](https://doi.org/10.1093/bioinformatics/btae123)**Haochen Wang, Qixiu Du, Ye Wang et al.*24 citations*
97. **[LMCrot: an enhanced protein crotonylation site predictor by leveraging an interpretable window-level embedding from a transformer-based protein language model ↗](https://doi.org/10.1093/bioinformatics/btae290)**Pawel Pratyush, Soufia Bahmani, Suresh Pokharel et al.*24 citations*
98. **[Prediction of antibiotic resistance mechanisms using a protein language model ↗](https://doi.org/10.1093/bioinformatics/btae550)**Kanami Yagimoto, Shion Hosoda, Miwa Sato et al.*24 citations*
99. **[ReMASTER: improved phylodynamic simulation for BEAST 2.7 ↗](https://doi.org/10.1093/bioinformatics/btae015)**Timothy G. Vaughan*24 citations*
100. **[Admix-kit: an integrated toolkit and pipeline for genetic analyses of admixed populations ↗](https://doi.org/10.1093/bioinformatics/btae148)**Kangcheng Hou, Stephanie M. Gogarten, Joohyun Kim et al.*23 citations*

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