Ecological Responses to Habitat Fragmentation Per Se von Fahrig, Lenore

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Graph embedding on biomedical networks: methods, applications and evaluations von Yue, Xiang, Wang, Zhen, Huang, Jingong, Parthasarathy, Srinivasan, Moosavinasab, Soheil, Huang, Yungui, Lin, Simon M, Zhang, Wen, Zhang, Ping, Sun, Huan

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deepDR: a network-based deep learning approach to in silico drug repositioning von Zeng, Xiangxiang, Zhu, Siyi, Liu, Xiangrong, Zhou, Yadi, Nussinov, Ruth, Cheng, Feixiong

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DeepGOPlus: improved protein function prediction from sequence von Kulmanov, Maxat, Hoehndorf, Robert

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Discovering protein drug targets using knowledge graph embeddings von Mohamed, Sameh K, Nováček, Vít, Nounu, Aayah

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DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces von Réau, Manon, Renaud, Nicolas, Xue, Li C, Bonvin, Alexandre M J J

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ProDy 2.0: increased scale and scope after 10 years of protein dynamics modelling with Python von Zhang, She, Krieger, James M, Zhang, Yan, Kaya, Cihan, Kaynak, Burak, Mikulska-Ruminska, Karolina, Doruker, Pemra, Li, Hongchun, Bahar, Ivet

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Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes von Zhu, Wensi, Shenoy, Aditi, Kundrotas, Petras, Elofsson, Arne

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Automatic local resolution-based sharpening of cryo-EM maps von Ramírez-Aportela, Erney, Vilas, Jose Luis, Glukhova, Alisa, Melero, Roberto, Conesa, Pablo, Martínez, Marta, Maluenda, David, Mota, Javier, Jiménez, Amaya, Vargas, Javier, Marabini, Roberto, Sexton, Patrick M, Carazo, Jose Maria, Sorzano, Carlos Oscar S

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AlphaPulldown—a python package for protein–protein interaction screens using AlphaFold-Multimer von Yu, Dingquan, Chojnowski, Grzegorz, Rosenthal, Maria, Kosinski, Jan

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COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology von Domingo-Fernández, Daniel, Baksi, Shounak, Schultz, Bruce, Gadiya, Yojana, Karki, Reagon, Raschka, Tamara, Ebeling, Christian, Hofmann-Apitius, Martin, Kodamullil, Alpha Tom

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DeepSurf: a surface-based deep learning approach for the prediction of ligand binding sites on proteins von Mylonas, Stelios K, Axenopoulos, Apostolos, Daras, Petros

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DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks von Savojardo, Castrense, Bruciaferri, Niccolò, Tartari, Giacomo, Martelli, Pier Luigi, Casadio, Rita

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Spectrum: fast density-aware spectral clustering for single and multi-omic data von John, Christopher R, Watson, David, Barnes, Michael R, Pitzalis, Costantino, Lewis, Myles J

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PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein–protein interaction information von Yang, Hangyuan, Wang, Minghui, Liu, Xia, Zhao, Xing-Ming, Li, Ao

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Current structure predictors are not learning the physics of protein folding von Outeiral, Carlos, Nissley, Daniel A, Deane, Charlotte M

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ACME: pan-specific peptide–MHC class I binding prediction through attention-based deep neural networks von Hu, Yan, Wang, Ziqiang, Hu, Hailin, Wan, Fangping, Chen, Lin, Xiong, Yuanpeng, Wang, Xiaoxia, Zhao, Dan, Huang, Weiren, Zeng, Jianyang

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DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model von Fang, Yitian, Jiang, Yi, Wei, Leyi, Ma, Qin, Ren, Zhixiang, Yuan, Qianmu, Wei, Dong-Qing

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Predicting protein–peptide binding residues via interpretable deep learning von Wang, Ruheng, Jin, Junru, Zou, Quan, Nakai, Kenta, Wei, Leyi

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Structure-aware protein self-supervised learning von Chen, Can (Sam), Zhou, Jingbo, Wang, Fan, Liu, Xue, Dou, Dejing

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