Research in Computational Molecular Biology: 29th International Conference, RECOMB 2025, Seoul, South Korea, April 2629, 2025, Proceedings [Pehme köide]

Orientation-Aware Graph Neural Networks for Protein Structure
Representation Learning.- Active Learning for Protein Structure Prediction.-
Sequence-based TCR-Peptide Representations Using Cross-Epitope Contrastive
Fine-tuning of Protein Language Models.- DualGOFiller: A Dual-Channel Graph
Neural Network with Contrastive Learning for Enhancing Function Prediction in
Partially Annotated Proteins.- Detecting antimicrobial resistance through
MALDI-TOF mass spectrometry with statistical guarantees using conformal
prediction.- Hierarchical Spatio-Temporal State-Space Modeling for fMRI
Analysis.- A Phylogenetic Approach to Genomic Language Modeling.- Dynamic
Programming Algorithms for Fast and Accurate Cell Lineage Tree Reconstruction
from CRISPR-based Lineage Tracing Data.- Old dog, new tricks: Exact seeding
strategy improves RNA design performances.- Scalable and Interpretable
Identification of Minimal Undesignable RNA Structure Motifs with Rotational
Invariance.- An Exact and Fast SAT Formulation for the DCJ Distance.-
Improved pangenomic classification accuracy with chain statistics.- Dynamic
-PBWT: Dynamic Run-length Compressed PBWT for Biobank Scale Data.-
Prokrustean Graph: A substring index for rapid k-mer size analysis.- Rag2Mol:
Structure-based drug design based on Retrieval Augmented Generation.-
Rewiring protein sequence and structure generative models to enhance protein
stability prediction.- Learning a CoNCISE language for small molecule binding
and function.- An adversarial scheme for integrating multi-modal data on
protein function.- Decoding the Functional Interactome of Non-Model Organisms
with PHILHARMONIC.- The tree labeling polytope: a unified approach to
ancestral reconstruction problems.- ScisTree2: An Improved Method for
Large-scale Inference of Cell Lineage Trees and Genotype Calling from Noisy
Single Cell Data.- OMKar: optical map based automated karyotyping of genomes
to identify constitutional disorders.- TarDis: Achieving Robust and
Structured Disentanglement of Multiple Covariates.- devider: long-read
reconstruction of many diverse haplotypes.- Pharming: Joint Clonal Tree
Reconstruction of SNV and CNA Evolution from Single-cell DNA Sequencing of
Tumors.- GEM-Finder: dissecting GWAS variants via long-range interacting
cis-regulatory elements with differentiation-specific genes.- Learning
multi-cellular representations of single-cell transcriptomics data enables
characterization of patient-level disease states.- cfDecon: Accurate and
interpretable methylation based cell type deconvolution for cell-free DNA.-
Inferring cell differentiation maps from lineage tracing data.-
Alignment-free estimation of read to genome distances and its applications.-
ML-MAGES: A machine learning framework for multivariate genetic association
analyses with genes and effect size shrinkage.- TX-Phase: Secure Phasing of
Private Genomes in a Trusted Execution Environment.- Hyper-k-mers: efficient
streaming k-mers representation.- Characterizing the Solution Space of
Migration Histories of Metastatic Cancers with MACH2.- Causal Disentanglement
of Treatment Effects in Single-cell RNA Sequencing through Counterfactual
Inference.- Integration and querying of multimodal single-cell data with
PoE-VAE.- ralphi: a deep reinforcement learning framework for haplotype
assembly.- GeneCover: A Combinatorial Approach for Label-free Marker Gene
Selection.- Joint imputation and deconvolution of gene expression across
spatial transcriptomics platforms.- ScatTR: Estimating the Size of Long
Tandem Repeat Expansions from Short-Reads.- Learning Latent Trajectories in
Developmental Time Series with Hidden-Markov Optimal Transport.- Unified
integration of spatial transcriptomics across platforms.- Tree reconstruction
guarantees from CRISPR-Cas9 lineage tracing data using Neighbor-Joining.-
mcRigor: a statistical method to enhance the rigor of metacell partitioning
in single-cell data analysis.- TissueMosaic enables cross-sample differential
analysis of spatial transcriptomics datasets through self-supervised
representation learning.- Accurate Detection of Tandem Repeats from
Error-Prone Sequences with EquiRep.- ALPINE: an interpretable approach for
decoding phenotypes from multi-condition sequencing data.- Synthetic control
removes spurious discoveries from double dipping in single-cell and spatial
transcriptomics data analyses.- Integer programming framework for
pangenome-based genome inference.- A Partition Function Algorithm to Evaluate
Inferred Subclonal Structures in Single-Cell Sequencing Data.- Untying Rates
of Gene Gain and Loss Leads to a New Phylogenetic Approach.- Learning
maximally spanning representations improves protein function annotation.-
Optimal marker genes for c-separated cell types.- Bayesian Aggregation of
Multiple Annotations Enhances Rare Variant Association Testing.- Steamboat:
Attention-Based Multiscale Delineation of Cellular Interactions in Tissues.