東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Research in computational molecular ...

RECOMB (Conference : 2005- ) (2025 :)

FindBook

Google Book

Amazon

博客來

Research in computational molecular biology = 29th International Conference, RECOMB 2025, Seoul, South Korea, April 26-29, 2025 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Research in computational molecular biology/ edited by Sriram Sankararaman.
其他題名:	29th International Conference, RECOMB 2025, Seoul, South Korea, April 26-29, 2025 : proceedings /
其他題名:	RECOMB 2025
其他作者:	Sankararaman, Sriram.
團體作者:	RECOMB (Conference : 2005- )
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xxiv, 440 p. :ill. (some col.), digital ;24 cm.
內容註:	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 DataOld 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 μ-PBWT: Dynamic Run-length Compressed PBWT for Biobank Scale DataProkrustean 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 DataOMKar: 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 DNaInferring cell differentiation maps from lineage tracing dataAlignment-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 dataSynthetic 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 DataUntying 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.
Contained By:	Springer Nature eBook
標題:	Computational biology - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-031-90252-9
ISBN:	9783031902529

Research in computational molecular biology = 29th International Conference, RECOMB 2025, Seoul, South Korea, April 26-29, 2025 : proceedings /
Research in computational molecular biology29th International Conference, RECOMB 2025, Seoul, South Korea, April 26-29, 2025 : proceedings /[electronic resource] :RECOMB 2025edited by Sriram Sankararaman. - Cham :Springer Nature Switzerland :2025. - xxiv, 440 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,156471611-3349 ;. - Lecture notes in computer science ;15647..

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 DataOld 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 μ-PBWT: Dynamic Run-length Compressed PBWT for Biobank Scale DataProkrustean 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 DataOMKar: 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 DNaInferring cell differentiation maps from lineage tracing dataAlignment-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 dataSynthetic 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 DataUntying 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.

This book constitutes the proceedings of the 29th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2025, held in Seoul, South Korea, during April 26-29, 2025. The 14 full papers and 41 short papers were carefully reviewed and selected from 339 submissions. They focus on advances in computational biology and applications in molecular biology and medicine. The conference aims at bridging the computational, mathematical, statistical, and biological sciences, and bringing together researchers, professionals, students and industrial practitioners from all over the world for interaction and exchange of new developments in all areas of bioinformatics and computational biology.

ISBN: 9783031902529

Standard No.: 10.1007/978-3-031-90252-9doiSubjects--Topical Terms:

893170
Computational biology
--Congresses.

LC Class. No.: QH324.2 / .R43 2025

Dewey Class. No.: 570.285

Research in computational molecular biology = 29th International Conference, RECOMB 2025, Seoul, South Korea, April 26-29, 2025 : proceedings /
LDR:06947nmm a2200373 a 4500 001 2409812
003 DE-He213
005 20250425130209.0
006 m d
007 cr nn 008maaau
008 260204s2025 sz s 0 eng d
020 $a 9783031902529 $q (electronic bk.)
020 $a 9783031902512 $q (paper)
024 7 $a 10.1007/978-3-031-90252-9 $2 doi
035 $a 978-3-031-90252-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QH324.2 $b .R43 2025
072 7 $a UY $2 bicssc
072 7 $a PS $2 bicssc
072 7 $a COM082000 $2 bisacsh
072 7 $a PSAX $2 thema
082 0 4 $a 570.285 $2 23
090 $a QH324.2 $b .R311 2025
111 2 $a RECOMB (Conference : 2005- ) $n (29th : $d 2025 : $c Seoul, Korea) $3 3783273
245 1 0 $a Research in computational molecular biology $h [electronic resource] : $b 29th International Conference, RECOMB 2025, Seoul, South Korea, April 26-29, 2025 : proceedings / $c edited by Sriram Sankararaman.
246 3 $a RECOMB 2025
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xxiv, 440 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 1611-3349 ; $v 15647
490 1 $a Lecture notes in bioinformatics
505 0 $a 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 DataOld 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 μ-PBWT: Dynamic Run-length Compressed PBWT for Biobank Scale DataProkrustean 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 DataOMKar: 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 DNaInferring cell differentiation maps from lineage tracing dataAlignment-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 dataSynthetic 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 DataUntying 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.
520 $a This book constitutes the proceedings of the 29th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2025, held in Seoul, South Korea, during April 26-29, 2025. The 14 full papers and 41 short papers were carefully reviewed and selected from 339 submissions. They focus on advances in computational biology and applications in molecular biology and medicine. The conference aims at bridging the computational, mathematical, statistical, and biological sciences, and bringing together researchers, professionals, students and industrial practitioners from all over the world for interaction and exchange of new developments in all areas of bioinformatics and computational biology.
650 0 $a Computational biology $v Congresses. $3 893170
650 0 $a Bioinformatics $x Congresses. $3 731254
650 1 4 $a Bioinformatics. $3 553671
650 2 4 $a Mathematical and Computational Biology. $3 1566274
650 2 4 $a Computational and Systems Biology. $3 3531279
650 2 4 $a Statistics and Computing. $3 3594429
700 1 $a Sankararaman, Sriram. $3 1683901
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 15647. $3 3783274
830 0 $a Lecture notes in bioinformatics. $3 3382603
856 4 0 $u https://doi.org/10.1007/978-3-031-90252-9
950 $a Computer Science (SpringerNature-11645)