## RECOMB 2026 Accepted Papers

- Quartet-based species tree methods enable fast and consistent tree of blobs reconstruction under the network multispecies coalescent\
*Junyan Dai, Yunheng Han and Erin Molloy*

- Fast and Flexible Flow Decompositions in General Graphs via Dominators\
*Francisco Sena and Alexandru I. Tomescu*

- Predicting interaction-specific protein–protein interaction perturbations by missense variants with MutPred-PPI\
*Ross Stewart, Florent Laval, Michael Calderwood, Matthew Mort, David Cooper, Marc Vidal and Predrag Radivojac*

- KuPID: Kmer-based Upstream Preprocessing of Long Reads for Isoform Discovery\
*Molly Borowiak and Yun William Yu*

- Private Information Leakage from Polygenic Risk Scores\
*Kirill Nikitin and Gamze Gursoy*

- Privacy-Preserving Pangenome Graphs\
*Jacob Blindenbach, Shaunak Soni and Gamze Gursoy*

- ProtFlow: Flow Matching-based Protein Sequence Design with Comprehensive Protein Semantic Distribution Learning and High-quality Generation\
*Zitai Kong, Yiheng Zhu, Yinlong Xu, Mingze Yin, Tingjun Hou, Jian Wu, Hongxia Xu and Chang-Yu Hsieh*

- POTTR: Identifying Recurrent Trajectories in Evolutionary and Developmental Processes using Posets\
*Sara C. Käufler, Henri Schmidt, Martin Jürgens, Gunnar W. Klau, Palash Sashittal and Benjamin J. Raphael*

- SpecLig: Energy-Guided Hierarchical Model for Target-Specific 3D Ligand Design\
*Peidong Zhang, Rong Han, Xiangzhe Kong, Ting Chen and Jianzhu Ma*

- STELAR-X: Scaling Coalescent-Based Species Tree Inference to 100,000 Species and Beyond\
*Anik Saha and Md Shamsuzzoha Bayzid*

- A Cophylogenetic Approach for Virus-Host Interaction Prediction\
*Md Zarzees Uddin Shah Chowdhury, T. M. Murali and Palash Sashittal*

- Random Matrix Theory-guided sparse PCA for single-cell RNA-seq data\
*Victor Chardès*

- Prediction of lifetime disease liability from EHR features\
*Yazheng Di and Na Cai*

- Quantum and Temporal Graph Neural Networks Reveal New Accuracy Limits in Predicting Protein–Ligand Dissociation Kinetics\
*Azamat Salamatov, Jun Bai, Gowtham Atluri and Chaowen Guan*

- MaxGeomHash: An Algorithm for Variable-Size Random Sampling of Distinct Elements\
*Mahmudur Rahman Hera, David Koslicki and Conrado Martínez*

- Evolutionary dynamics under phenotypic uncertainty\
*Vaibhav Mohanty, Anna Sappington, Eugene Shakhnovich and Bonnie Berger*

- Deconvolving Phylogenetic Distance Mixtures\
*Shayesteh Arasti, Ali Osman Berk Şapcı, Eleonora Rachtman, Mohammed El-Kebir and Siavash Mirarab*

- Achieving spatial multi-omics integration from unaligned serial sections with DIME\
*Pengyu Sun, Tian Mou, Xubin Zheng and Xinlei Huang*

- Deep genomic models of allele-specific measurements\
*Xinming Tu, Alexander Sasse, Kaitavjeet Chowdhary, Anna E. Spiro, Liang Yang, Maria Chikina, Christophe O. Benoist and Sara Mostafavi*

- On Deriving Synteny Blocks by Compacting Elements\
*Leonard Bohnenkämper, Luca Parmigiani, Cedric Chauve and Jens Stoye*

- PaNDA: Efficient Optimization of Phylogenetic Diversity in Networks\
*Niels Holtgrefe, Leo van Iersel, Ruben Meuwese, Yukihiro Murakami and Jannik Schestag*

- Information Geometry Reconciles Discrete and Continuous Variation in Single-Cell and Spatial Transcriptomic Analysis\
*Jinpu Cai, Yuxuan Wang, Yunhao Qiao, Cheng Wang, Ziqi Rong, Luting Zhou, Haoyang Liu, Meng Jiang, Hongbin Shen, Jingyi Jessica Li and Hongyi Xin*

- Bacterial protein function prediction via multimodal deep learning\
*Giulia Muzio, Michael Adamer, Leyden Fernandez, Lukas Miklautz, Karsten Borgwardt and Kemal Avican*

- Alternet: Alternative splicing-aware gene regulatory network inference\
*Juliane Hoffmann, Julia Wallnig, Ziheng Dai, Olga Tsoy, David B. Blumenthal and Anne Hartebrodt*

- Bias in genome-wide association test statistics due to omitted interactions\
*Burak Yelmen, Merve Nur Güler, Tõnu Kollo, Märt Möls, Guillaume Charpiat and Flora Jay*

- Joint Learning of Drug-Drug Combination and Drug-Drug Interaction via Coupled Tensor-Tensor Factorization with Side Information\
*Xiaoge Zhang, Zhengyu Fang, Kaiyu Tang, Huiyuan Chen and Jing Li*

- Sequence-to-graph alignment based copy number calling using a network flow formulation\
*Hugo Magalhães, Jonas Weber, Gunnar W. Klau, Tobias Marschall and Timofey Prodanov*

- Fast, accurate construction of multiple sequence alignments from protein language embeddings\
*Minh Hoang, Isabel Armour-Garb and Mona Singh*

- SLAB: A Sweep Line Algorithm in PBWT for Finding Haplotype Block Cores\
*Ardalan Naseri, Ahsan Sanaullah, Shaojie Zhang and Degui Zhi*

- Compressed inverted indexes for scalable sequence similarity\
*Florian Ingels, Lea Vandamme, Mathilde Girard, Clément Agret, Bastien Cazaux and Antoine Limasset*

- Minimizer Density revisited: Models and Multiminimizers\
*Florian Ingels, Lucas Robidou, Igor Martayan, Camille Marchet and Antoine Limasset*

- DiCoLo: Integration-free and cluster-free detection of localized differential gene co-expression in single-cell data\
*Ruiqi Li, Junchen Yang, Pei-Chun Su, Ariel Jaffe, Ofir Lindenbaum and Yuval Kluger*

- bronko: ultrarapid, alignment-free detection of viral genome variation\
*Ryan Doughty, Michael Tisza and Todd Treangen*

- Arborist: Prioritizing Bulk DNA Inferred Tumor Phylogenies via Low-pass Single-cell DNA Sequencing Data\
*Leah Weber, Chi Yin Ching, Chrisopher Ly, Yixiao Cheng, Chunxu Gao and Peter Van Loo*

- Causal gene regulatory network inference from Perturb-seq via adaptive instrumental variable modeling\
*Zhongxuan Sun, Hyunseung Kang and Sunduz Keles*

- Summarizing RNA Structural Ensembles via Maximum Agreement Secondary Structures\
*Xinyu Gu, Stefan Ivanovic, Daniel Feng and Mohammed El-Kebir*

- CAMP: Coreset Accelerated Metacell Partitioning enables scalable analysis of single-cell data\
*Danrong Li, Young Kun Ko and Stefan Canzar*

- Multi-modal tissue-aware graph neural network for in silico genetic discovery\
*Anusha Aggarwal, Ksenia Sokolova and Olga Troyanskaya*

- CLADES — Contrastive Learning Augmented DifferEntial Splicing with Orthologous Positive Pairs\
*Arghamitra Talukder, Nicholas Keung, Itsik Pe'Er and David A. Knowles*

- MOSAIC: A Spectral Framework for Integrative Phenotypic Characterization Using Population-Level Single-Cell Multi-Omics\
*Chang Lu, Yuval Kluger and Rong Ma*

- GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy\
*Anupama Nandi, Seth Neel and Hyunghoon Cho*

- Identifying Robust Subclonal Structures Through Tumor Progression Tree Alignment\
*Chih Hao Wu, Jacob Gilbert, Salem Malikić and S. Cenk Sahinalp*

- DeltaNMF: A Two-Stage Neural NMF for Differential Gene Program Discovery\
*Anish Karpurapu, Charles Gersbach and Rohit Singh*

- Transforming Biological Foundation Model Representations for Out-of-Distribution Data\
*Aditya Pratapa, Rohit Singh and Purushothama Rao Tata*

- A biobank-scale method for learning environmental modulators of gene–environment interaction underlying complex traits\
*Zhengtong Liu, Arush Ramteke, Aakarsh Anand, Aditya Gorla, Moonseong Jeong and Sriram Sankararaman*

- Integrative Inference of Spatially Informed Cell Lineage Trees using LineageMap\
*Xinhai Pan, Yiru Chen and Xiuwei Zhang*

- Uncertainty-aware synthetic lethality prediction with pretrained foundation models\
*Kailey Hua, Ellie Haber and Jian Ma*

- Evolutionary profile enhancement improves protein function annotation\
*Shitong Dai, Jiaqi Luo and Yunan Luo*

- Faster and Scalable Parallel External-Memory Construction of Colored Compacted de Bruijn Graphs with Cuttlefish 3\
*Jamshed Khan, Laxman Dhulipala, Prashant Pandey and Rob Patro*

- Nullstrap-DE: A General Framework for Calibrating FDR and Preserving Power in Differential Expression Methods, with Applications to DESeq2 and edgeR\
*Chenxin Flora Jiang, Changhu Wang and Jingyi Jessica Li*

- Gene-First Identity Construction for Robust Cell Identification in Single-Cell Transcriptomics\
*Luqi Yang, Zhenwei Huang, Hongyi Xin and Jinpu Cai*

- scDesignPop generates population-scale single-cell RNA-seq data for power analysis, method benchmarking, and privacy protection\
*Chris Dong, Yihui Cen, Dongyuan Song and Jingyi Jessica Li*

- Error Correction Algorithms for Efficient Gene Quantification in Single Cell Transcriptomics\
*Jens Zentgraf, Johanna Schmitz, Andreas Keller and Sven Rahmann*

- CycleGRN: Inferring Gene Regulatory Networks from Cyclic Flow Dynamics in Single-Cell RNA-seq\
*Wenjun Zhao, Elana Fertig and Genevieve Stein-O'Brien*

- Integrative Multi‑Scale Sequence–Structure Modeling for Antimicrobial Peptide Prediction and Design\
*Jiayi Li, Yanruisheng Shao, Yu Li and Qinze Yu*

- Modeling Multi-Modal Brain Connectomes for Brain Disorder Diagnosis via Graph Diffusion Optimal Transport Network\
*Xiaoqi Sheng, Jiawen Liu, Jiaming Liang, Yiheng Zhang, Sankar Mondal, Yutong Li, Tinghe Zhang, Bing Liu, Jiangning Song and Hongmin Cai*

- Fast and accurate resolution of ecDNA sequence using Cycle-Extractor\
*Mahsa Faizrahnemoon, Jens Luebeck, King L. Hung, Suhas Rao, Matthew G. Jones, Paul S. Mischel, Howard Y. Chang, Kaiyuan Zhu and Vineet Bafna*

- pHapCompass: Probabilistic Assembly and Uncertainty Quantification of Polyploid Haplotype Phase\
*Marjan Hosseini, Ella Veiner, Thomas Bergendahl, Tala Yasenpoor, Zane Smith, Margaret Staton and Derek Aguiar*

- Generating Hybrid Proteins with the MSA-Transformer\
*Sanjana Tule, Samuel Davis, Ivan Koludarov, Ariane Mora and Mikael Boden*

- scProfiterole: Clustering of Single-Cell Proteomic Data Using Graph Contrastive Learning via Spectral Filters\
*Mustafa Coşkun, Filipa Blasco Lopes, Pınar Kubilay Tolunay, Mark R. Chance and Mehmet Koyutürk*

- Constrained diffusion as a paradigm for evolution\
*Daniel Lazarev, Anna Sappington, Grant Chau, Ruochi Zhang and Bonnie Berger*

- Multimodal spatial alignment and morphology mapping with MOSAICField\
*Xinhao Liu, Hongyu Zheng, Peter Halmos, Julian Gold, Erik Storrs, Li Ding and Ben Raphael*

- Deconvolving mutation effects on protein stability and function with disentangled protein language models\
*Kerr Ding, Ziang Li, Tony Tu, Jiaqi Luo and Yunan Luo*

- Integrating morphology and gene expression in unpaired single-cell data using GeoAdvAE\
*Jinqiu Du and Kevin Z. Lin*

- Protein Compositional Ratio Representation (PCRR) Systematically Improves Human Disease Prediction\
*Adithya Madduri, Randall Ellis and Chirag Patel*