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At least 19 recordsLinked to original sources

PubMed1979

The alpha-globin gene adjacent to the gene for HbQ-alpha 74 Asp replaced by His is deleted, but not that adjacent to the gene for HbG-alpha 30 Glu replaced by Gln; three-fourths of the alpha-globin genes are deleted in HbQ-alpha-thalassemia.

Two Chinese patients with HbQ-alpha 2 74 Asp replaced by His beta 2-alpha-thalassemia, one HbQ-alpha 2 74 or 75 Asp replaced by His beta 2 carrier, and one HbG-alpha 2 30 Glu replaced by Gln beta 2 carrier were studied to determine the number of alpha-globin genes in their chromosomes. DNA was isolated from white blood cells and bone marrow cells and studied by liquid hybridization and by hybridization of DNA fragments obtained by restriction enzyme endonuclease digestion (Ecr to nitrocellulose filters. The liquid hybridization analysis showed that in HbQ-alpha 2 74 Asp replaced by His beta 2-alpha-thalassemia, as in HbH disease, only one-fourth of the usual number of alpha-globin genes is present. Hybridization patterns of DNA restriction enzyme fragments showed that in HbQ-alpha 2 74 Asp replaced by His beta 2-alpha-thalassemia one chromosome has both alpha-globin genes deleted and the other chromosome, which carries the alpha-mutant gene, has one alpha-globin gene deleted. Our results show that the HbQ-alpha 74 Asp replaced by His structural gene is located adjacent to a deleted alpha-globin gene, whereas the alpha-globin gene adjacent to HbG-alpha 30 Glu replaced by Gln gene is not deleted.

Aspartic Acid↗

PubMed2024

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes.

BACKGROUND: The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes. RESULTS: We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin. CONCLUSION: We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

Humans↗

PubMed1979

Organization of tRNA and rRNA genes in N. crassa mitochondria: intervening sequence in the large rRNA gene and strand distribution of the RNA genes.

Through analysis of cloned fragments of N. crassa mitochondrial DNA, we have derived a physical map for the region of the mitochondrial genome which encodes the ribosomal RNAs and most of the tRNAs. We have located RNA genes on this map by hybridization of purified 32P end-labeled RNA probes, and our findings are as follows. First, the gene for the large ribosomal RNA contains an intervening sequence of approximately 2000 bp. Second, the genes for the small and large ribosomal RNAs are not adjacent, as previously reported, and the region between them contains a number of tRNA genes, including that for the mitochondrial tRNATyr, which is located close to the small rRNA gene on the same strand of the mitochondrial DNA. Third, there is a second cluster of tRNA genes on the mitochondrial DNA following the large ribosomal RNA gene, but there is no evidence for the presence of tRNA genes in the intervening sequence of the large ribosomal RNA. Fourth, hybridization of labeled ribosomal and transfer RNAs to the separated strands of a cloned 16 kbp DNA fragment covering this region indicates that the two ribosomal RNAs and most, if not all, of the mitochondrial tRNAs are encoded on one strand of the mitochondrial DNA.

Base Sequence↗

PubMed2026

Identification and Classification of Expressed Orphan Genes, Spurious Orphan Genes, and Conserved Genes in the Human Gut Microbiome.

Orphan genes (OGs)-genes lacking detectable homologs outside a species-are widespread in microbial genomes and are thought to contribute to their adaptation and molecular innovation. However, not all predicted OGs may represent novel functional coding sequences. False positive OGs, also called spurious OGs, can arise from gene prediction errors. We reason that OGs lacking detectable expression are more likely to be spurious. To test this, we combined large-scale metatranscriptomic profiling of the human gut microbiome with machine learning to distinguish expressed OGs from spurious ones and compare them with conserved genes (CGs) found in multiple species. Using nearly 5,000 metatranscriptome libraries, we identified ∼218,000 OGs supported by expression evidence, while ∼330,000 predicted OGs lacked detectable expression and were classified as spurious. We extracted 154 features for sequence, structural, and evolutionary properties for each gene and trained XGBoost classifiers while accounting for genomic representation. The models achieved an area under the receiver operating characteristic curve (AUC) of 0.82 in distinguishing expressed OGs from spurious OGs and an AUC of 0.93 in distinguishing expressed OGs from CGs. Interpretation based on SHAP (SHapley Additive exPlanations) revealed clear biological signals. Particularly, expressed orphans were present in more genomes than spurious ones, and expressed OGs were shorter than CGs. This work improves OG discovery and suggests that expressed OGs differ systematically from CGs and spurious OGs in sequence composition, structural constraints, and evolutionary signals.

Humans↗

PubMed1979

Gene localization by chromosome fractionation: globin genes are on at least two chromosomes and three estrogen-inducible genes are on three chromosomes.

Chicken metaphase chromosomes were partially purified by rate zonal centrifugation, and DNA was prepared from each of the fractions of the sucrose gradient. The DNA was digested with various restriction enzymes and subjected to electrophoresis in agarose gels. The DNA was transferred to nitrocellulose filters (as described by Southern), and the filters were hybridized with cDNA probes. Four globin genes alpha A, alpha D, beta, and rho or epsilon are located on at least two chromosomes, and three of the estrogen-inducible genes of the hen oviduct--ovalbumin, ovomucoid, and transferrin--are on three different chromosomes. These experiments also confirm our earlier assignment of the endogenous viral sequence related to Rous-associated virus-0 to a separate (and larger) chromosome than the cellular sequence related to the transforming gene of avian sarcoma virus (cellular sarc), although it now appears that cellular sarc is on a small macrochromosome, rather than on a microchromosome.

Animals↗

PubMed1977

Cloned MPC 11 myeloma cells express two kappa genes: a gene for a complete light chain and a gene for a constant region polypeptide.

Cloned MPC 11 mouse plasmacytoma cells synthesize a complete kappa light chain and also a kappa light chain constant region fragment. Partial amino terminal sequences of the in vitro forms of these two proteins have been determined. Both in vitro products contain typical light chain leaders; leaders are defined as the amino terminal sequences present on in vitro products but absent from the in vivo products found in living cells. The in vitro form of the MPC 11 complete light chain contains a leader sequence plus variable and constant region sequences. The in vitro form of the MPC 11 light chain constant region fragment contains a different leader sequence attached directly to a complete constant ragion sequence and has no variable region sequences. Thus the MPC 11 light chain fragment is not a degradation product of the MPC 11 complete light chain (or of any other complete light chain) and must be coded by a separate gene. The results reveal two unusual features of MPC 11 cells: first, expression of a unique variant light chain gene coding the light chain constant region fragment, and second, expression of two different kappa light chain genes (coding the complete light chain and the variant constant region fragment) in a single cell. In addition, evidence is provided that the in vitro forms of kappa light chains, three of which are presented here for the first time, include a minimum of three partially homologous but quite different leader sequences.

Amino Acid Sequence↗

PubMed1979

Assignment of the large oligonucleotides of vesicular stomatitis virus to the N, NS, M, G, and L genes and oligonucleotide gene ordering within the L gene.

Analyses of prototype vesicular stomatitis (VSV, Indiana serotype) mRNA-32P-labeled viral RNA duplexes have established the assignments of 65 of the 72 large oligonucleotides that are recovered by two-dimensional electrophoresis of RNase T1 digests of the viral RNA. Fifty of the oligonucleotides are recovered in the L RNA duplex, four each in the N, M, and NS duplexes, and three in the G RNA duplex. Studies of three small defective-particle RNA species indicate that they have only L gene oligonucleotides in addition to three of the seven unassigned oligonucleotides. Some L gene ordering of oligonucleotides can be postulated from the defective-particle RNA sequence analyses. Analyses of naturally occurring alternate isolates of VSV Indiana have established that by comparison to the prototype virus strain, the alternate isolates minimally have genome sequence differences in L, G, N, NS and/or unassigned regions of the genome. Changes in the genome have also been induced by vitro high-level mutagenesis of the prototype virus.

Genes, Viral↗

PubMed2025

Exploring prognostic genes in the immune microenvironment of acute myeloid leukemia via weighted gene co-expression network analysis.

BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous blood cancer that arises from transformed myeloid precursor cells in a compromised bone marrow microenvironment. This environment is essential for AML initiation, progression, and relapse. Alongside oncogenic changes in hematopoietic cells, immunological dysregulation also contributes to leukemogenesis. The present study is aimed to identify prognostic genes in stromal and immune cells associated with AML using the weighted gene co-expression network analysis (WGCNA). METHODS: Gene expression profiles were retrieved from The Cancer Genome Atlas database, and immune and stromal cell scores were calculated using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) method. These scores helped identify differentially expressed genes (DEGs), which were then used to create gene clusters through WGCNA. To explore the functions of genes linked to AML subtypes, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed. A protein-protein interaction network was developed to identify hub genes. The top 18 hub genes were identified using the cytoHubba plug-in in Cytoscape software, and survival analysis was conducted with the Gene Expression Profiling Interactive Analysis 2 online tool. RESULTS: A total of 1097 DEGs were identified, with 601 being upregulated and 496 downregulated. WGCNA analysis indicated that the gray module, comprising 165 genes, had the strongest association with AML subtypes (Cor > 0.3; P < .05). Gene Ontology enrichment analysis demonstrated that the 18 identified hub genes were predominantly associated with neutrophil activation, immune response, secretory granule membrane, and pattern recognition receptor activity. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that the DEGs were mainly involved in pathways related to phagosome, lysosome, tuberculosis, leishmaniasis, and neutrophil extracellular trap formation. Kaplan-Meier survival analysis of the top 18 hub genes indicated that ITGAM, IL10, and CD163 were significantly correlated with survival outcomes in AML. CONCLUSION: Key stromal and immune-related genes influencing AML patient outcomes were identified, highlighting their potential as therapeutic targets. These discoveries provide deeper insights into the molecular mechanisms driving AML pathogenesis and subtype differentiation.

Leukemia, Myeloid, Acute↗

PubMed2026

A sequence-based classifier distinguishes phenotype-associated genes from other gene models in plants.

Only a small fraction of annotated plant genes possess experimentally validated associations with specific phenotypes. Phenotype-associated genes have distinct structural, molecular, and evolutionary characteristics compared with nonvalidated gene models. Here, we develop a simple classifier that uses sequence and evolutionary features, which can be generated for any species with an annotated reference genome assembly, to accurately distinguish phenotype-associated genes from both the overall population of annotated gene models and a specific set of genes identified as being tolerant of premature stop mutations. A model trained solely on genes from maize (Zea mays) identifies and prioritizes rice (Oryza sativa) and Arabidopsis (Arabidopsis thaliana) genes that are highly enriched in genes with experimentally validated links to phenotypes in both of these evolutionarily distant species. Gene models predicted to have a higher probability of being linked to phenotypes display patterns consistent with known biological properties of phenotype-associated genes. Notably, the sets of genes predicted to have a high probability of being linked to phenotype variation do not consist exclusively of well-characterized gene families but included many uncharacterized gene families carrying domains of unknown function. The quantitative scores generated by this model offer a valuable resource for prioritizing and exploring the vast number of uncharacterized gene models in plants, reducing the risk of failure in future reverse genetic efforts and potentially accelerating gene discovery and functional annotation in crops.

Phenotype↗

PubMed1978

Gene expression and stability of mRNA affected by DNA-arrested synthesis in gene 59, 46, and 47 mutants of bacteriophage T4.

The effect of bacteriophage T4 gene 59 mutations (DNA-arrested synthesis) on kinetics of DNA synthesis, gene expression, and stability of mRNA has been studied. When Escherichia coli B was infected by a T4 gene 59 mutant, DNA synthesis proceeded to increase linearly after initiation, but started to decrease at 8 min and was completely arrested at 12 min at 37 degrees C. At various incubation temperatures (20 to 42 degrees C), the initial rates and times of arrest of DNA synthesis were different, but the total amount of DNA synthesized was constant. This result supports the hypothesis that function of gene 59 is required for the conversion of 63S DNA molecules to other replicative intermediates (39). The abnormality in protein synthesis caused by gene 59 mutation is manifested by (i) a delayed shutoff in the expression of early proteins (gene 43, 46, 39, 52, 63, 42-45, and some unidentified proteins), (ii) a reduced rate of late gene expression (gene 34, 37, 18, 20, 23, wac, 24, 22, 38, and 19), and (iii) an absence of cleavage of certain late proteins (23, 24, IPIII and 22 to 23(*), 24(*), IPIII(*), and small fragments). It appears that there was no effect on the expression of gene 33, 55, and 32 by a mutation in gene 59. Results obtained from an addition of rifampin at the prereplicative cycle after infection indicated that mRNA from genes 43, rIIA, 46, 39, 52, and 63 are more stable in T4amC5 (gene 59) than in wild-type-infected cells. mRNA remained functional longer in mutant-infected cells, and this may explain the prolonged synthesis of certain early proteins. The gene expression of other DNA arrested mutants-those in genes 46 and 47-showed a pattern of abnormal protein synthesis similar to that found in gene 59 mutant-infected cells, except more late proteins are synthesized. The gene expression in terms of phage DNA structure is discussed.

Coliphages↗

PubMed1978

Analysis of the beta-delta-globin gene loci in normal and Hb Lepore DNA: direct determination of gene linkage and intergene distance.

Total human DNA was cleaved with a variety of restriction enzymes, and the fragments were fractionated by gel electrophoresis and transferred to nitrocellulose filter strips. The restricted DNA was then hybridized to nick-translated radioactive recombinant plasmid DNA containing sequences derived from human beta-globin messenger RNA. Under suitable conditions, this probe hybridizes with both the beta--and delta-globin genes. Using this probe, a restriction map of the human beta--and delta-globin genes and the surrounding genomic DNA regions has been constructed. The beta-globin gene contains a nonglobin DNA insert approximately 899-1000 base pairs in length, present within the sequence coding for amino acids 101-120 of the 146 amino acid long globin polypeptide. A similar sequence may be present within the same sequence of the delta-globin gene. The distance between the beta--and delta-globin genes is approximately 7000 nucleotide pairs, and the delta-globin gene is to the 5' side of the beta-globin gene, as predicted by genetic evidence. Both genes are transcribed from the same DNA strand. The structure of the Hb Lepore gene is shown to be a fused delta--and beta-globin gene, and to be completely consistent with the derived map of normal beta--and delta-globin genes. [Restriction enzyme nomenclature follows that of Smith and Nathans (1973) and Roberts (1976). A genomic DNA restriction fragment containing part or all of one globin gene will be designated by that globin chain--for instance, the Pst I fragment containing the beta-globin gene sequence will be designated Pst I beta. A similar convention will be used for double digests. Throughout this paper, when reference is made to the 5' or 3' side or fragment of a gene, this refers to the 5' or 3' side of the mRNA coded by that sequence. Thus the 5' side (N terminal) of the beta-globulin gene is the sequence to the 5' side of the anti-sense strand.].

Chromosome Mapping↗

PubMed2023

Diverse evolutionary rates and gene duplication patterns among families of functional olfactory receptor genes in humans.

In humans, odors are detected by ~400 functional olfactory receptor (OR) genes. The superfamily of functional OR genes can be further divided into tens of families. In large part, the OR genes have experienced extensive tandem duplications, which have led to gene gains and losses. However, whether different OR gene families have experienced distinct modes of gene duplication has yet to be reported. We conducted comparative genomic and evolutionary analyses for human functional OR genes. Based on analysis of human-mouse 1-1 orthologs, we found that human functional OR genes show higher-than-average evolutionary rates, and there are significant differences among families of functional OR genes. Via comparison with seven vertebrate outgroups, families of human functional OR genes show different extents of gene synteny conservation. Although the superfamily of human functional OR genes is enriched in tandem and proximal duplications, there are particular families which are enriched in segmental duplications. These findings suggest that human functional OR genes may be governed by different evolutionary mechanisms and that large-scale gene duplications have contributed to the early evolution of human functional OR genes.

Humans↗

PubMed2025

Large-scale analysis of MYB genes in Cucurbitaceae identifies a novel gene regulating plant height.

The MYB transcription factor (TF) family, which is involved in plant growth and development, is large and diverse. Previous studies on MYB family in Cucurbitaceae were mostly based on a single genome or focused on the R2R3 subfamily. Here, we analyzed 91 genomes of 11 Cucurbitaceae species and identified a total of 15 858 MYB genes. According to phylogenetic relationships, these genes were divided into 27 subgroups. The identified MYB genes were further classified into 121 MYB orthologous gene groups (OGGs), including 25 core, 57 softcore, 19 shell and 20 line-specific/cloud groups. Whole-genome duplication was the most common mechanism of MYB genes expansion. In core group, the higher proportions of MYB genes were found to be in the coexpression network constructed by the RNA-seq data. Through the comprehensive analysis including phylogeny and gene expression profile of cucumber MYB genes, as well as genetic variations in 103 cucumber germplasms, we identified a MYB gene CsRAX5, which may be related to cucumber plant height. We used gene editing technology to knockout and overexpress CsRAX5. In the knockout lines, Csrax5, the height was significantly increased compared with wild type (WT), whereas after overexpression the height of CsRAX5-OE plants was significantly decreased compared with WT. These results indicated that MYB gene CsRAX5 negatively regulated cucumber plant height. The large-scale analysis of MYB genes in Cucurbitaceae in this study provides insights for further investigating the evolution and function of MYB genes in Cucurbitaceae crops.

Journal Article↗

PubMed2025

Integration of multi-source gene interaction networks and omics data with graph attention networks to identify novel disease genes.

MOTIVATION: The pathogenesis of diseases is closely associated with genes, and the discovery of disease genes holds significant importance for understanding disease mechanisms and designing targeted therapeutics. However, biological validation of all genes for diseases is expensive and challenging. RESULTS: In this study, we propose DGP-AMIO, a computational method based on graph attention networks, to rank all unknown genes and identify potential novel disease genes by integrating multi-omics and gene interaction networks from multiple data sources. DGP-AMIO outperforms other methods significantly on 20 disease datasets, with an average AUROC and AUPR exceeding 0.9. The superior performance of DGP-AMIO is attributed to the integration of multiomics and gene interaction networks from multiple databases, as well as triGAT, a proposed GAT-based method that enables precise identification of disease genes in directed gene networks. Enrichment analysis conducted on the top 100 genes predicted by DGP-AMIO and literature research revealed that a majority of enriched GO terms, KEGG pathways and top genes were associated with diseases supported by relevant studies. We believe that our method can serve as an effective tool for identifying disease genes and guiding subsequent experimental validation efforts. AVAILABILITY AND IMPLEMENTATION: DGP-AMIO is publicly available at https://github.com/yangkaiyuan1027/DGP-AMIO.

Gene Regulatory Networks↗

PubMed2025

scPOEM: robust co-embedding of peaks and genes revealing peak-gene regulation.

MOTIVATION: Identifying regulatory elements in various chromosomal regions that influence gene expression is a fundamental challenge in epigenomics, with profound implications for understanding gene regulation and disease mechanisms. The advent of paired single-cell RNA sequencing and single-cell ATAC sequencing has created unprecedented opportunities to address this challenge by enabling simultaneous profiling of gene expression and chromatin accessibility at single-cell resolution. However, the inherent signals between them are weak due to the highly sparse and noisy nature of data. RESULTS: This article proposes single-cell meta-Path based Omics Embedding (scPOEM), a novel embedding method that jointly projects chromatin accessibility peaks and expressed genes into a shared low-dimensional space. By integrating the relationships among peak-peak, peak-gene, and gene-gene interactions, scPOEM assigns closer representations in the embedding space to related peak-gene pairs. Our experiments demonstrate that scPOEM generates stable representations of peaks and genes, outperforms existing methods in recovering biologically meaningful peak-gene regulatory relationships and enables new insights in subgroup and differential analysis of gene regulation. These results highlight its potential to uncover gene regulatory mechanisms and enhance the understanding of transcriptional regulation at single-cell resolution. AVAILABILITY AND IMPLEMENTATION: The source code of scPOEM is available at https://github.com/Houyt23/scPOEM. The datasets can be obtained from the 10× Genomics (https://www.10xgenomics.com/datasets/pbmc-from-a-healthy-donor-granulocytes-removed-through-cell-sorting-10-k-1-standard-1-0-0) and GEO database under access codes GSE194122 and GSE239916.

Gene Expression Regulation↗

PubMed1978

Nucleotide sequence of gene VII and of a hypothetical gene (IX) in bacteriophage M13.

A DNA fragment containing gene VII of bacteriophage M13 has been transcribed and the nucleotide sequence of this 169-nucleotides long transcript was determined by RNA sequencing methods. Additionally, the nucleotide sequence of this gene and parts of its neighbouring genes V and VIII has been determined by the dimethylsulphate-hydrazine technique. The reading frame of gene VII has been established by determining the nucleotide changes occurring in the transcripts of two amber mutants of this gene. From these combined data it is apparent that gene VII is only 99 nucleotides long and is immediately followed by the termination codon UGA. Its initiation codon AUG is separated from gene V by only a single nucleotide. It was noted that between the UGA termination codon of gene VII and the initiation codon of the next gene (gene VIII) there is space for another, hitherto unknown gene. This gene (IX) most probably codes for the small polypeptide ("C-protein") present in mature M13 phage particles.

Base Sequence↗

PubMed1979

Regulation of expression of the flagellin gene (hag) in Escherichia coli K-12: analysis of hag-lac gene fusions.

Previous studies have defined 28 genes necessary for the synthesis of the flagellar apparatus of Escherichia coli K-12. This study analyzed the influence of the flagellar genes on the expression of the hag gene (structural gene for flagellin). To this end, a hag::Mu d(Apr lac) mutant which had the lac genes fused to the promoter of the hag gene was constructed. This allowed the measurement of hag gene expression by detection of beta-galactosidase activity. The following observations were made. (i) The hag gene was expressed constitutively in Fla+ cells. (ii) hag gene expression was positively regulated by flaA, FLAB, flaC, flaD, flaE, flaG, flaH, flaI, flaK, flaL, flaM, flaN, flaO, flaP, flaQ, flaR, flaV, flaW, flaX, flaY, flaZ, flbA, and flbB genes.hag-lac expression was not observed in strains with these fla mutations. (iii) The hag gene was expressed in mutants with flaS, flaT, flaU, and flbC defects. Therefore, these genes were not involved in regulation of hag gene transcription.

Bacterial Proteins↗

PubMed2025

DyNDG: Identifying Leukemia-related Genes Based on Time-series Dynamic Network by Integrating Differential Genes.

Leukemia is a malignant disease characterized by progressive accumulation with high morbidity and mortality rates, and investigating its disease genes is crucial for understanding its etiology and pathogenesis. Network propagation methods have emerged and been widely employed in disease gene prediction, but most of them focus on static biological networks, which hinders their applicability and effectiveness in the study of progressive diseases. Moreover, there is currently a lack of special algorithms for the identification of leukemia disease genes. Here, we proposed a novel Dynamic Network-based model integrating Differentially expressed Genes (DyNDG) to identify leukemia-related genes. Initially, we constructed a time-series dynamic network to model the development trajectory of leukemia. Then, we built a background-temporal multilayer network by integrating both the dynamic network and the static background network, which was initialized with differentially expressed genes at each stage. To quantify the associations between genes and leukemia, we extended a random walk process to the background-temporal multilayer network. The results demonstrate that DyNDG achieves superior accuracy compared to several state-of-the-art methods. Moreover, after excluding housekeeping genes, DyNDG yields a set of promising candidate genes associated with leukemia progression or potential biomarkers, indicating the value of dynamic network information in identifying leukemia-related genes. The implementation of DyNDG is available at both https://ngdc.cncb.ac.cn/biocode/tool/BT7617 and https://github.com/CSUBioGroup/DyNDG.

Leukemia↗