Searching journal content for articles similar to Asthana et al. 14 (6): 1170.

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Methods: Finding friends and enemies in an enemies-only network: A graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions
- Yan Qi,
- Yasir Suhail,
- Yu-yi Lin,
- Jef D. Boeke,
- and Joel S. Bader
Genome Res. December 2008 18: 1991-2004; Published in Advance October 2, 2008, doi:10.1101/gr.077693.108
...Finding friends and enemies in an enemies-only network: A graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions Yan Qi 1 , Yasir Suhail 1 , Yu-yi Lin 2 , 3 , Jef D. Boeke 2 , 3...
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LETTER: Assessing the limits of genomic data integration for predicting protein networks
- Long J. Lu,
- Yu Xia,
- Alberto Paccanaro,
- Haiyuan Yu,
- and Mark Gerstein
Genome Res. July 2005 15: 945-953; doi:10.1101/gr.3610305
....M., Simon, T., et al. 2002 . Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298 : 799 -804. ↵ Lee, I., Date, S.V., Adai, A.T., and Marcotte, E.M. 2004 . A probabilistic functional network of yeast genes. Science 306 : 1555 -1558. Letovsky, S. and Kasif, S. 2003 . Predicting protein...
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Method: Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet
- Yidi Deng,
- Jiadong Mao,
- Jarny Choi,
- and Kim-Anh Lê Cao
Genome Res. March 5, 2026 ; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
...the degree of membership of cell n to that cluster. NMF learns these soft clusters based on cell–cell similarity in their networks, and the corresponding meta-network for cluster i is computed as a weighted average of the individual CSNs: Meta-Networki = CSNn.., Hni. Our NMF approach extracts multiple soft...
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Method: Integration of high-throughput proteomic data and complementary omics layers with PriOmics
- Robin Kosch,
- Katharina Limm,
- Annette M. Staiger,
- Nadine S. Kurz,
- Nicole Seifert,
- Bence Oláh,
- Stefan Solbrig,
- Viola Poeschel,
- Gerhard Held,
- Marita Ziepert,
- Norbert Schmitz,
- Emil Chteinberg,
- Reiner Siebert,
- Rainer Spang,
- Helena U. Zacharias,
- German Ott,
- Peter J. Oefner,
- and Michael Altenbuchinger
Genome Res. January 2026 36: 197-213; Published in Advance November 18, 2025, doi:10.1101/gr.279487.124
...layers statistically using a dedicated network inference method based on probabilistic graphical models.Naive pair-wise measures of association such as Pearson's correlation were shown to be prone to false positives as a consequence of indirect relationships mediated by one or more other variables...
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Method: Graph-based deep reinforcement learning for haplotype assembly with Ralphi
- Enzo Battistella,
- Anant Maheshwari,
- Barış Ekim,
- Bonnie Berger,
- and Victoria Popic
Genome Res. December 2025 35: 2617-2625; Published in Advance November 14, 2025, doi:10.1101/gr.280569.125
...et al. 2022) formulates haplotype phasing as a graph coloring problem on read-overlap graphs and uses a graph neural network (GNN) to learn color assignments, whereas CAECseq (Ke and Vikalo 2022) uses convolutional auto-encoders to cluster reads into haplotypes. These approaches, however, have been...
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Method: Deep structural clustering reveals hidden systematic biases in RNA sequencing data
- Qiang Su,
- Yi Long,
- Deming Gou,
- Junmin Quan,
- Xiaoming Zhou,
- and Qizhou Lian
Genome Res. November 2025 35: 2563-2577; Published in Advance September 19, 2025, doi:10.1101/gr.280713.125
...biological functions (Le and Maizel 1997; Cheng et al. 2001). The VAE-GMM k-mer clustering model, by combining deep neural networks with probabilistic clustering and dimensionality reduction techniques, offers a promising framework for decoding high-dimensional RNA sequence structures and uncovering latent...
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RESOURCE: Computational modeling of the Plasmodium falciparum interactome reveals protein function on a genome-wide scale
- Shailesh V. Date and
- Christian J. Stoeckert, Jr.
Genome Res. April 2006 16: 542-549; Published in Advance March 6, 2006, doi:10.1101/gr.4573206
...) as predicted linkages for individual genes. View larger version: In this window In a new window Figure 1. A generalized strategy for integrating diverse functional genomics data and reconstructing functional interaction networks. ( A ) A salient feature of this strategy involves the use of reference priors...
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Method: Fast and space-efficient taxonomic classification of long reads with hierarchical interleaved XOR filters
- Jens-Uwe Ulrich and
- Bernhard Y. Renard
Genome Res. June 2024 34: 914-924; Published in Advance June 17, 2024, doi:10.1101/gr.278623.123
...quotient filters for indexing and querying k-mers. Those methods have the disadvantage that they index each reference separately using data structures for approximate membership queries, for example, Bloom filters (Bloom 1970). In contrast, sequence bloom tree (SBT) (Solomon and Kingsford 2018; Sun et al...
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Resource: Interspecies regulatory landscapes and elements revealed by novel joint systematic integration of human and mouse blood cell epigenomes
- Guanjue Xiang,
- Xi He,
- Belinda M. Giardine,
- Kathryn J. Isaac,
- Dylan J. Taylor,
- Rajiv C. McCoy,
- Camden Jansen,
- Cheryl A. Keller,
- Alexander Q. Wixom,
- April Cockburn,
- Amber Miller,
- Qian Qi,
- Yanghua He,
- Yichao Li,
- Jens Lichtenberg,
- Elisabeth F. Heuston,
- Stacie M. Anderson,
- Jing Luan,
- Marit W. Vermunt,
- Feng Yue,
- Michael E.G. Sauria,
- Michael C. Schatz,
- James Taylor,
- Berthold Göttgens,
- Jim R. Hughes,
- Douglas R. Higgs,
- Mitchell J. Weiss,
- Yong Cheng,
- Gerd A. Blobel,
- David M. Bodine,
- Yu Zhang,
- Qunhua Li,
- Shaun Mahony,
- and Ross C. Hardison
Genome Res. July 2024 34: 1089-1105; Published in Advance July 1, 2024, doi:10.1101/gr.277950.123
...to learn epigenetic states, generate concise views of epigenetic landscapes, and predict regulatory elements that are comparable in both species. This joint modeling enabled further comparisons using approaches that were not dependent on DNA sequence alignments between species, including a demonstration...
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Method: Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation
- Casey Hanson,
- Junmei Cairns,
- Liewei Wang,
- and Saurabh Sinha
Genome Res. August 2018 28: 1207-1216; Published in Advance June 13, 2018, doi:10.1101/gr.227066.117
....One of the key mechanistic questions related to drug response variation, or indeed for any phenotypic variation under study, is the role of gene regulatory networks (GRNs) in shaping such variation. A major step in characterizing GRNs mediating drug response variation is to identify functionally important...
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