191 related articles for article (PubMed ID: 38468344)
61. Development of computational models using omics data for the identification of effective cancer metabolic biomarkers.
Lee SM; Kim HU
Mol Omics; 2021 Dec; 17(6):881-893. PubMed ID: 34608924
[TBL] [Abstract][Full Text] [Related]
62. Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers.
Li CI; Yeh YM; Tsai YS; Huang TH; Shen MR; Lin PC
Hum Genomics; 2023 Mar; 17(1):18. PubMed ID: 36879264
[TBL] [Abstract][Full Text] [Related]
63. Seten: a tool for systematic identification and comparison of processes, phenotypes, and diseases associated with RNA-binding proteins from condition-specific CLIP-seq profiles.
Budak G; Srivastava R; Janga SC
RNA; 2017 Jun; 23(6):836-846. PubMed ID: 28336542
[TBL] [Abstract][Full Text] [Related]
64. Gene expression analysis in clear cell renal cell carcinoma using gene set enrichment analysis for biostatistical management.
Maruschke M; Reuter D; Koczan D; Hakenberg OW; Thiesen HJ
BJU Int; 2011 Jul; 108(2 Pt 2):E29-35. PubMed ID: 21435154
[TBL] [Abstract][Full Text] [Related]
65. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT.
Agren R; Bordel S; Mardinoglu A; Pornputtapong N; Nookaew I; Nielsen J
PLoS Comput Biol; 2012; 8(5):e1002518. PubMed ID: 22615553
[TBL] [Abstract][Full Text] [Related]
66. Association between bivariate expression of key oncogenes and metabolic phenotypes of patients with prostate cancer.
Khodayari Moez E; Pyne S; Dinu I
Comput Biol Med; 2018 Dec; 103():55-63. PubMed ID: 30340213
[TBL] [Abstract][Full Text] [Related]
67. Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features.
Xie G; Dong C; Kong Y; Zhong JF; Li M; Wang K
Genes (Basel); 2019 Mar; 10(3):. PubMed ID: 30901858
[TBL] [Abstract][Full Text] [Related]
68. De novo discovery of mutated driver pathways in cancer.
Vandin F; Upfal E; Raphael BJ
Genome Res; 2012 Feb; 22(2):375-85. PubMed ID: 21653252
[TBL] [Abstract][Full Text] [Related]
69. The panoramic picture of pepsinogen gene family with pan-cancer.
Shen S; Li H; Liu J; Sun L; Yuan Y
Cancer Med; 2020 Dec; 9(23):9064-9080. PubMed ID: 33067881
[TBL] [Abstract][Full Text] [Related]
70. Metabolic source isotopic pair labeling and genome-wide association are complementary tools for the identification of metabolite-gene associations in plants.
Simpson JP; Wunderlich C; Li X; Svedin E; Dilkes B; Chapple C
Plant Cell; 2021 May; 33(3):492-510. PubMed ID: 33955498
[TBL] [Abstract][Full Text] [Related]
71. Extending metabolome coverage for untargeted metabolite profiling of adherent cultured hepatic cells.
García-Cañaveras JC; López S; Castell JV; Donato MT; Lahoz A
Anal Bioanal Chem; 2016 Feb; 408(4):1217-30. PubMed ID: 26769129
[TBL] [Abstract][Full Text] [Related]
72. Comparing somatic mutation-callers: beyond Venn diagrams.
Kim SY; Speed TP
BMC Bioinformatics; 2013 Jun; 14():189. PubMed ID: 23758877
[TBL] [Abstract][Full Text] [Related]
73. A multimodal atlas of tumour metabolism reveals the architecture of gene-metabolite covariation.
Benedetti E; Liu EM; Tang C; Kuo F; Buyukozkan M; Park T; Park J; Correa F; Hakimi AA; Intlekofer AM; Krumsiek J; Reznik E
Nat Metab; 2023 Jun; 5(6):1029-1044. PubMed ID: 37337120
[TBL] [Abstract][Full Text] [Related]
74. Metabolic Footprinting of Microbial Systems Based on Comprehensive In Silico Predictions of MS/MS Relevant Data.
Reiter A; Asgari J; Wiechert W; Oldiges M
Metabolites; 2022 Mar; 12(3):. PubMed ID: 35323700
[TBL] [Abstract][Full Text] [Related]
75. Common and mutation specific phenotypes of KRAS and BRAF mutations in colorectal cancer cells revealed by integrative -omics analysis.
Kundu S; Ali MA; Handin N; Conway LP; Rendo V; Artursson P; He L; Globisch D; Sjöblom T
J Exp Clin Cancer Res; 2021 Jul; 40(1):225. PubMed ID: 34233735
[TBL] [Abstract][Full Text] [Related]
76. Deep Pathway Analysis V2.0: A Pathway Analysis Framework Incorporating Multi-Dimensional Omics Data.
Zhao Y; Shin DG
IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(1):373-385. PubMed ID: 31603796
[TBL] [Abstract][Full Text] [Related]
77. MiMeNet: Exploring microbiome-metabolome relationships using neural networks.
Reiman D; Layden BT; Dai Y
PLoS Comput Biol; 2021 May; 17(5):e1009021. PubMed ID: 33999922
[TBL] [Abstract][Full Text] [Related]
78. Gene characteristics predicting missense, nonsense and frameshift mutations in tumor samples.
Gorlov IP; Pikielny CW; Frost HR; Her SC; Cole MD; Strohbehn SD; Wallace-Bradley D; Kimmel M; Gorlova OY; Amos CI
BMC Bioinformatics; 2018 Nov; 19(1):430. PubMed ID: 30453881
[TBL] [Abstract][Full Text] [Related]
79. The Potential Transcriptomic and Metabolomic Mechanisms of ATO and ATRA in Treatment of FLT3-ITD Acute Myeloid Leukemia.
Peng CJ; Fan Z; Luo JS; Wang LN; Li Y; Liang C; Zhang XL; Luo XQ; Huang LB; Tang YL
Technol Cancer Res Treat; 2024; 23():15330338231223080. PubMed ID: 38179723
[TBL] [Abstract][Full Text] [Related]
80. A new strategy for dynamic metabolic flux estimation by integrating transient metabolome data into genome-scale metabolic models.
Liu P; Hua Y; Zhang W; Xie T; Zhuang Y; Xia J; Noorman H
Bioprocess Biosyst Eng; 2021 Dec; 44(12):2553-2565. PubMed ID: 34459987
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]