Exploring genetic interactions and networks with yeast

A haplotype map of the human genome

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Functional organization of the yeast proteome by systematic analysis of protein complexes

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A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae

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Functional profiling of the Saccharomyces cerevisiae genome

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Functional Characterization of the S. cerevisiae Genome by Gene Deletion and Parallel Analysis

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Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry

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A comprehensive two-hybrid analysis to explore the yeast protein interactome

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Global landscape of protein complexes in the yeast Saccharomyces cerevisiae

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Proteome survey reveals modularity of the yeast cell machinery

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A High-Throughput Strategy for Dissecting Mammalian Genetic Interactions

Stockman, V. B., Ghamsari, L., Lasso, G., Honig, B. H., Shapira, S. D., & Wang, H. H. (2016) · PLoS ONE · doi:10.1371/journal.pone.0167617 · Cited by 5

Structure and dynamics of human complication-disease network

Jiang, X.-F., Xiong, L., Bai, L., Lin, J., Zhang, J., Yan, K. H., Zhu, J., Zheng, B., & Zheng, J. (2022) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.112633 · Cited by 5

Genetic Networks

Costanzo, M., Baryshnikova, A., VanderSluis, B., Andrews, B., Myers, C. L., & Boone, C. M. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-385944-0.00006-x · Cited by 5

Structural similarity of genetically interacting proteins

Dror, O., Schneidman‐Duhovny, D., Shulman‐Peleg, A., Nussinov, R., Wolfson, H. J., & Sharan, R. (2008) · BMC Systems Biology · doi:10.1186/1752-0509-2-69 · Cited by 5

A strategy to apply quantitative epistasis analysis on developmental traits

Labocha, M. K., Wang, Y., Aleman-Meza, B., & Zhong, W. (2017) · BMC Genetics · doi:10.1186/s12863-017-0508-4 · Cited by 4

Comprehensive Analyses of Nitric Oxide-Induced Plant Stem Cell-Related Genes in Arabidopsis thaliana

Shahid, M., Imran, Q. M., Hussain, A., Khan, M., Lee, S., Mun, B. G., & Yun, B. (2019) · Genes · doi:10.3390/genes10020173 · Cited by 5

Reconstructing Gene Networks from Microarray Time-Series Data via Granger Causality

Luo, Q., Liu, X., & Yi, D. (2009) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-02466-5_17 · Cited by 5

Identification of response-modulated genetic interactions by sensitivity-based epistatic analysis

Batenchuk, C., Tepliakova, L., & Kærn, M. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-493 · Cited by 5

Predicting synthetic lethal genetic interactions in Saccharomyces cerevisiae using short polypeptide clusters

Zhang, Y., Li, B., Srimani, P. K., Chen, X., & Luo, F. (2012) · Proteome Science · doi:10.1186/1477-5956-10-s1-s4 · Cited by 5

The use of high-throughput microscopy in the characterisation of phenotypes associated with the Unfolded Protein Response in Saccharomyces cerevisiae

Bircham, P. W. (2014) · doi:10.26686/wgtn.17008045.v1 · Cited by 3

Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG)Preprint

Schmidlin, K., Ogbunugafor, C. B., Sastokas, A., & Geiler‐Samerotte, K. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.08.593194 · Cited by 5

Environment-by-Environment Interactions Differ Across Genetic Backgrounds

Schmidlin, K., Ogbunugafor, C. B., Sastokas, A., & Geiler‐Samerotte, K. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf287 · Cited by 9

Amino acid metabolism and MAP kinase signaling pathway play opposite roles in the regulation of ethanol production during fermentation of sugarcane molasses in budding yeast

Jiang, L., Yu-zhi, S., Jiang, Y., Mei, W., Wei, L., Feng, J., Wei, C., Liao, X., Mo, Y., Pan, L., et al. (2024) · Genomics · doi:10.1016/j.ygeno.2024.110811 · Cited by 5

Inferring Gene Network from Candidate SNP Association Studies Using a Bayesian Graphical Model: Application to a Breast Cancer Case-Control Study from Ontario

Kang, S., Savas, S., Özçelik, H., & Briollais, L. (2014) · Human Heredity · doi:10.1159/000365923 · Cited by 3

Reducing False-Positive Prediction of Minimotifs with a Genetic Interaction Filter

Merlin, J. C., Rajasekaran, S., Mi, T., & Schiller, M. R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032630 · Cited by 4

Genetically Encoded Fluorescence Barcodes Allow for Single-Cell Analysis via Spectral Flow Cytometry

Lu, X., Pritko, D. J., Abravanel, M., Huggins, J. R., Ogunleye, O., Biswas, T., Ashy, K. C., Woods, S. K., Livingston, M. W. T., Blenner, M., et al. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00807 · Cited by 5

Predicting Gene Function Using Omics Data: From Data Preparation to Data Integration

Tian, W., Dong, X., Zhou, Y., & Ren, R. (2011) · Springer eBooks · doi:10.1007/978-94-007-0881-5_12 · Cited by 4

To Control False Positives in Gene-Gene Interaction Analysis: Two Novel Conditional Entropy-Based Approaches

Zuo, X., Rao, S., An, F., Lin, M., Li, H., Zhao, X., & Qin, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081984 · Cited by 4

Inferring Genetic Interactions via a Data-Driven Second Order Model

Jiang, C., Hung, Y., Chen, C., & Shieh, G. S. (2012) · Frontiers in Genetics · doi:10.3389/fgene.2012.00071 · Cited by 4

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern

Amini, S., Jacobsen, A., Ivanova, O., Lijnzaad, P., Heringa, J., Holstege, F. C. P., Feenstra, K. A., & Kemmeren, P. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007061 · Cited by 4

Chromosome instability and tumor lethality suppression in carcinogenesis

Shen, C., Zhou, Y., Zhan, J., Reske, S. N., & Buck, A. K. (2008) · Journal of Cellular Biochemistry · doi:10.1002/jcb.21937 · Cited by 3

Synthetic lethality and the minimal genome size problem

Rahiminejad, S., Sanctis, B. D., Pevzner, P. A., & Mushegian, A. R. (2024) · mSphere · doi:10.1128/msphere.00139-24 · Cited by 3

MPASL: multi-perspective learning knowledge graph attention network for synthetic lethality prediction in human cancer

Zhang, G., Chen, Y., Yan, C., Wang, J., Liang, W., Luo, J., & Luo, H. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1398231 · Cited by 3

Synergistic Effect of Cytochrome P450 Family 3 Subfamily A Member 5 (CYP3A5) Genetic Variants in Tacrolimus Dose Determination in Indian Renal Transplant Patients

Hemani, R. J., Chauhan, P. M., Srivastava, R., Shete, N. B., Jojera, A., Soni, S. M., Gang, S. D., Konnur, A., Hegde, U. N., Patel, H. B., et al. (2024) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.4c00055 · Cited by 3

Identification of Thermoregulatory Factors Involved in Validamycin Biosynthesis and Reconstitution of a Thermoregulated Expression System

Yu, Y., Kang, Q., Yan, Y.-S., Liu, X., & Bai, L. (2025) · Biotechnology Journal · doi:10.1002/biot.70068 · Cited by 3

Polygenic backgrounds influence phenotypic consequences of variants in cells, individuals, and populations

Chapel, M., Dennis, J. K., & de Boer, C. G. (2026) · Cell Genomics · doi:10.1016/j.xgen.2025.101131 · Cited by 3

EFFECTIVE MODELS FOR GENE NETWORKS AND THEIR APPLICATIONS

Shulman, J., Seemann, L., Roman, G., & Gunaratne, G. H. (2012) · Biophysical Reviews and Letters · doi:10.1142/s1793048012300034 · Cited by 3

Dango: Predicting higher-order genetic interactions

Zhang, R., Bafna, M., Ma, J., & Ma, J. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101593 · Cited by 3

The Involvement of YNR069C in Protein Synthesis in the Baker’s Yeast, Saccharomyces cerevisiae

Takallou, S., Hajikarimlou, M., Al‐gafari, M., Wang, J., Kazmirchuk, T. D. D., Said, K. B., Samanfar, B., & Golshani, A. (2024) · Biology · doi:10.3390/biology13030138 · Cited by 3

Optimizing network structures: solving the maximum subgraph problem in grid networks

Dominic, R., Rajan, R. S., Rajalaxmi, T. M., & Manuel, P. D. (2025) · International Journal of Parallel Emergent and Distributed Systems · doi:10.1080/17445760.2025.2472203 · Cited by 3

SLAYER: a computational framework for identifying synthetic lethal interactions through integrated analysis of cancer dependencies

Cohen, Z., Petrenko, E., Barisaac, A. S., Abu‐Zhayia, E. R., Yanovich-Ben-Uriel, C., Ayoub, N., & Aran, D. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf052 · Cited by 3

Functional Analysis of iGlur Delta-2 and other Depression Associated Proteins

Siam, M. K. S., Kabir, E. R., Jurashe, P. S., Shohan, M. U. S., & Rajib, S. A. (2018) · International Conference Computational Systems-Biology and Bioinformatics · doi:10.1145/3291757.3291765 · Cited by 3

Transcriptional regulation of the genes involved in protein metabolism and processing inSaccharomyces cerevisiae

Dikicioǧlu, D., Nightingale, D. J. H., Wood, V., Lilley, K. S., & Oliver, S. G. (2019) · FEMS Yeast Research · doi:10.1093/femsyr/foz014 · Cited by 3

Epi-ID: Systematic and Direct Screening for Chromatin Regulators in Yeast by Barcode-ChIP-Seq

Poramba‐Liyanage, D. W., Korthout, T., & van Leeuwen, F. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9736-7_5 · Cited by 3

Analyzing the Structure, Function and Information Flow in Signaling Networks using Quantitative Cellular Signatures

Kulkarni, M. M., & Perrimon, N. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-385944-0.00005-8 · Cited by 3

Identification of Synchronized Role of Transcription Factors, Genes, and Enzymes in Arabidopsis thaliana under Four Abiotic Stress Responsive Pathways

Razzaque, S., Mahdi, R., & Islam, A. (2014) · Computational Biology Journal · doi:10.1155/2014/896513 · Cited by 3

Genome-Wide Scoring of Positive and Negative Epistasis through Decomposition of Quantitative Genetic Interaction Fitness Matrices

Eronen, V.-P., Lindén, R., Lindroos, A., Kanerva, M., & Aittokallio, T. A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0011611 · Cited by 3

Introduction to Focus Issue: Genetic Interactions

Segrè, D., & Marx, C. J. (2010) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.3456057 · Cited by 5

Improved functional overview of protein complexes using inferred epistatic relationships

Ryan, C. J., Greene, D., Guénolé, A., van Attikum, H., Krogan, N. J., Cunningham, P., & Cagney, G. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-80 · Cited by 3

Quantitative cell array screening to identify regulators of gene expression

Kainth, P., & Andrews, B. (2009) · Briefings in Functional Genomics · doi:10.1093/bfgp/elp047 · Cited by 3

Integrating gene expression and epidemiological data for the discovery of genetic interactions associated with cancer risk

Bonifaci, N., Colás, E., Serra-Musach, J., Karbalai, N., Brunet, J., Gómez, A., Esteller, M., Fernández-Taboada, E., Berenguer, A., Reventós, J., et al. (2013) · Carcinogenesis · doi:10.1093/carcin/bgt403 · Cited by 3

Yeast Genetics as a Powerful Tool to Study Human Diseases

Dabas, P., Kumar, D., & Sharma, N. (2017) · Springer eBooks · doi:10.1007/978-981-10-2621-8_8 · Cited by 3

Systems Genetics and Complex Traits

Carter, G. W., & Dudley, A. M. (2009) · Springer eBooks · doi:10.1007/978-0-387-30440-3_549 · Cited by 2

Elucidating the anti-cancer mechanisms for transition-state structure inhibitors of nucleoside phosphorylases methylthioadenosine-DADMe-immucillin A and forodesine

Coorey, N. (2017) · doi:10.26686/wgtn.17067545.v1 · Cited by 2

Nested interaction networks represent a missing link in the study of behavioural and community ecologyPreprint

Montiglio, P., Gotanda, K. M., Kratochwil, C. F., Laskowski, K. L., Nadell, C. D., & Farine, D. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.00927 · Cited by 3

Poor statistical power in population-based association study of gene interaction

Ma, J., Li, J., Chen, Y., Yang, Z., & He, Y. (2024) · BMC Medical Genomics · doi:10.1186/s12920-024-01884-w · Cited by 2

Cofitness network connectivity determines a fuzzy essential zone in open bacterial pangenome

Zhang, P., Zhang, B., Ji, Y., Jiao, J., Zhang, Z., & Tian, C. F. (2024) · mLife · doi:10.1002/mlf2.12132 · Cited by 2

dotears: Scalable and consistent directed acyclic graph estimation using observational and interventional data

Xue, A., Rao, J., Sankararaman, S., & Pimentel, H. (2024) · iScience · doi:10.1016/j.isci.2024.111673 · Cited by 9

BiGSM: Bayesian inference of gene regulatory network via sparse modelling

Qin, H., Garbulowski, M., Sonnhammer, E. L. L., & Chatterjee, S. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf318 · Cited by 2

From Topology to Phenotype in Protein–Protein Interaction Networks

Pržulj, N. (2010) · Springer eBooks · doi:10.1007/978-1-84996-396-1_3 · Cited by 2

Genetic interaction network has a very limited impact on the evolutionary trajectories in continuous culture-grown populations of yeast

Klim, J., Zielenkiewicz, U., Skoneczny, M., Skoneczna, A., Kurlandzka, A., & Kaczanowski, S. (2021) · BMC Ecology and Evolution · doi:10.1186/s12862-021-01830-9 · Cited by 2

Applications of microbial omics in healthcare

Shahid, F., Zaheer, T., Awan, H. S., Waseem, M., Basheer, A., & Ali, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-89837-9.00009-7 · Cited by 2

Network Analysis Reveals the Molecular Bases of Statin Pleiotropy That Vary with Genetic Background

Hernandez, C. E. D. R., Campbell, L. J., Atkinson, P. H., & Munkacsi, A. B. (2023) · Microbiology Spectrum · doi:10.1128/spectrum.04148-22 · Cited by 2

Mslar: Microbial synthetic lethal and rescue database

Zhu, S.-B., Jiang, Q.-H., Chen, Z., Zhou, X. Y., Jin, Y.-T., Deng, Z., & Guo, F. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011218 · Cited by 2

Robust Methods for Expression Quantitative Trait Loci Mapping

Cheng, W. D., Zhang, X., & Wang, W. W. (2016) · Springer eBooks · doi:10.1007/978-3-319-41279-5_2 · Cited by 2

DNA repair defects ascribed to pby1 are caused by disruption of Holliday junction resolvase Mus81-Mms4

Ölmezer, G., Klein, D., & Rass, U. (2015) · DNA repair · doi:10.1016/j.dnarep.2015.05.006 · Cited by 3

Network-Based Models in Molecular Biology

Beyer, A. (2009) · Birkhäuser Boston eBooks · doi:10.1007/978-0-8176-4751-3_3 · Cited by 2

Modelling by building blocks

Swami, M. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2510 · Cited by 2

Chapter 3 Synthetic Genetic Interactions

Gray, J. V., & Krause, S. A. (2009) · Advances in genetics · doi:10.1016/s0065-2660(09)66003-x · Cited by 3

The Budding Yeast Amphiphysin Complex Is Required for Contractile Actin Ring (CAR) Assembly and Post-Contraction GEF-Independent Accumulation of Rho1-GTP

Cundell, M. J., & Price, C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097663 · Cited by 2

Predicting Yeast Synthetic Lethal Genetic Interactions Using Protein Domains

Li, B., & Luo, F. (2009) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2009.37 · Cited by 2

Subspace Clustering of DNA Microarray Data

Tchagang, A., Famili, F. A., & Pan, Y. (2014) · International Journal of Computational Models and Algorithms in Medicine · doi:10.4018/ijcmam.2014070101 · Cited by 2

Current status of SNPs interaction in genome-wide association study

Li, F., Wang, Z., Hu, G., & Li, H. (2011) · Hereditas (Beijing) · doi:10.3724/sp.j.1005.2011.00901 · Cited by 2

Network Cleansing

Marcatili, P., & Tramontano, A. L. (2009) · IGI Global eBooks · doi:10.4018/978-1-60566-398-2.ch006 · Cited by 2

Growth condition dependency is the major cause of non-responsiveness upon genetic perturbation

Amini, S., Holstege, F. C. P., & Kemmeren, P. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173432 · Cited by 2

Computational Analysis of the Chaperone Interaction Networks

Kumar, A., Rizzolo, K., Zilles, S., Babu, M., & Houry, W. A. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7477-1_20 · Cited by 2

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

Marayati, B. F., Pease, J. B., & Zhang, K. (2019) · Journal of Visualized Experiments · doi:10.3791/59133 · Cited by 2

NSPA: characterizing the disease association of multiple genetic interactions at single-subject resolution

Sha, Z., Chen, Y., & Hu, T. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad010 · Cited by 2

WebPARE: web-computing for inferring genetic or transcriptional interactions

Chuang, C.-L., Wu, J., Cheng, C., & Shieh, G. S. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp684 · Cited by 2

Functional Genomics and Systems Biology Approach for Understanding Agroecosystems

Yadav, B. S., & Mani, A. (2019) · Springer eBooks · doi:10.1007/978-981-13-8739-5_4 · Cited by 2

Biological Data Integration Using Network Models

Kumar, G., & Ranganathan, S. (2013) · Wiley eBooks · doi:10.1002/9781118617151.ch07 · Cited by 2

From Marine Organism to Potential Drug: Using Innovative Techniques to Identify and Characterize Novel Compounds − a Bottom‐Up Approach

Singh, A. J., Field, J. J., Atkinson, P. H., Northcote, P. T., & Miller, J. H. (2014) · Wiley eBooks · doi:10.1002/9783527684403.ch16 · Cited by 2

Structured Input-Output Lasso, with Application to eQTL Mapping, and a Thresholding Algorithm for Fast EstimationPreprint

Lee, S., & Xing, E. P. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.1989 · Cited by 2

Master regulators of genetic interaction networks mediating statin drug response in Saccharomyces cerevisiae vary with genetic backgroundPreprint

Busby, B. P., Niktab, E., Roberts, C. A., Coorey, N., Sheridan, J. P., Senanayake, D., Munkacsi, A. B., & Atkinson, P. H. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/443879 · Cited by 2

The Genetic Profiles of TIF1 and TIF2 Duplicate Genes

Venturi, V. (2012) · Open Access Victoria University of Wellington | Te Herenga Waka (Figshare) · doi:10.26686/wgtn.16999348.v1 · Cited by 1

AVALIAÇÃO DO EFEITO FOTOPROTETOR DE TRÊS EXTRATOS DE PLANTAS DA ANTÁRTICA POR DIFERENTES MODELOS BIOLÓGICOS

Pereira, B. K. (2007) · Americanae (AECID Library) · Cited by 2

Investigating the Activities of CAF20 and ECM32 in the Regulation of PGM2 mRNA Translation

Al‐gafari, M., Jagadeesan, S. K., Kazmirchuk, T. D. D., Takallou, S., Wang, J., Hajikarimlou, M., Ramessur, N. B., Darwish, W., Bradbury-Jost, C., Moteshareie, H., et al. (2024) · Biology · doi:10.3390/biology13110884 · Cited by 1

Knowledge graph-aided Bayesian active learning for top-K genetic interaction discovery

Soper, B., Lisicki, M., Silva, M., Cadena, J., Zhu, H., Sundaram, S., Ray, P., & Drocco, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-13972-7 · Cited by 3

Quantitative genetics of natural S. cerevisiae strains upon mating reveal heritable determinants of fitness and differential mating affinities

Strauss, S. K., Golomb, R., Khoury, D., Aharon-Hefetz, N., Meyer, H., Sheykhkarimli, D., Liti, G., Dahan, O., & Pilpel, Y. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.116972 · Cited by 1

Synthetic rescue in Saccharomyces cerevisiae: Concepts, large-scale genetic mapping, and functional implications

Choi, J. E., & Chung, W. (2026) · The Journal of Microbiology · doi:10.71150/jm.2512017 · Cited by 1

Merging data means more powerful networks

Goymer, P. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2408 · Cited by 1

Systems mapping of HIV-1 infection

Hou, W., Sui, Y., Wang, Z., Wang, Y., Wang, N., Liu, J., Li, Y., Goodenow, M. M., Yin, L., Wang, Z., et al. (2012) · BMC Genetics · doi:10.1186/1471-2156-13-91 · Cited by 1

An Allele of Sequoia Dominantly Enhances a Trio Mutant Phenotype to Influence Drosophila Larval Behavior

Dean, K. E., Fields, A., Geer, M. J., King, E. C., Lynch, B. T., Manohar, R. R., McCall, J. R., Palozola, K. C., Zhang, Y., & Liebl, E. C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0084149 · Cited by 1

Modeling Huntington Disease in Yeast and Invertebrates

Mason, R. P., Breda, C., Kooner, G. S., Mallucci, G. R., Kyriacou, C. P., & Giorgini, F. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-405195-9.00033-0 · Cited by 2

Genetic Evidence for Roles of Yeast Mitotic Cyclins at Single-Stranded Gaps Created by DNA Replication

Signon, L. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.300537 · Cited by 1

Using genetic buffering relationships identified in fission yeast to reveal susceptibilities in cells lacking hamartin or tuberin function

Rayhan, A., Faller, A. C., Chevalier, R., Mattice, A. J. P., & Karagiannis, J. (2017) · Biology Open · doi:10.1242/bio.031302 · Cited by 1

Transcriptome Analysis Reveals that MAPK Signaling Pathway Mediates Salt Tolerance of YMR253C ORF in Saccharomyces cerevisiae

Zhang, Y., Li, M., Deng, J., Bai, C., Ma, J., & Lyu, L. (2022) · Current Microbiology · doi:10.1007/s00284-022-02818-4 · Cited by 1

Genetic Screen for Identification of Multicopy Suppressors in Schizosaccharomyces pombe

Bhardwaj, V., Sweta, K., Gyala, D., & Sharma, N. (2022) · Journal of Visualized Experiments · doi:10.3791/63967 · Cited by 1

Plant System Biology

Haq, I. U., Babar, M., Ali, H. M., Khan, N. A., Habib, A., Waris, M., & Jabeen, Z. (2024) · Springer eBooks · doi:10.1007/978-981-97-0814-7_9 · Cited by 1

L-rhamnose-1-dehydrogenase gene and L-rhamnose catabolism in the yeast Pichia stipitis

Koivistoinen, O. M. (2008) · Työväentutkimus Vuosikirja · Cited by 1

Interacting genes are more evolvable

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Inferring Mechanisms of Compensation from E-MAP and SGA Data Using Local Search Algorithms for Max Cut

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