Systematic assessment of prognostic molecular features across cancers

[1]  W. Hahn,et al.  Biologically informed deep neural network for prostate cancer discovery , 2021, Nature.

[2]  Neel S. Madhukar,et al.  Artificial Intelligence in Cancer Research and Precision Medicine. , 2021, Cancer discovery.

[3]  Jingya Fang,et al.  Revealing Prognosis-Related Pathways at the Individual Level by a Comprehensive Analysis of Different Cancer Transcription Data , 2020, Genes.

[4]  C. Amos,et al.  Comparison of pathway and gene-level models for cancer prognosis prediction , 2020, BMC Bioinformatics.

[5]  T. Davoli,et al.  Not all cancers are created equal: Tissue specificity in cancer genes and pathways. , 2020, Current opinion in cell biology.

[6]  Mary Goldman,et al.  Genomic basis for RNA alterations in cancer , 2020, Nature.

[7]  R. Germain,et al.  Cancer prognosis with shallow tumor RNA sequencing , 2020, Nature Medicine.

[8]  Anil K. Kesarwani,et al.  Differential Functions of Splicing Factors in Mammary Transformation and Breast Cancer Metastasis. , 2019, Cell reports.

[9]  A. Yoshizawa,et al.  Problems in the reproducibility of classification of small lung adenocarcinoma: an international interobserver study , 2019, Histopathology.

[10]  H. Rui,et al.  Individualized multi-omic pathway deviation scores using multiple factor analysis , 2019, bioRxiv.

[11]  Sijia Huang,et al.  DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data , 2021, Genome Medicine.

[12]  A. Maitra,et al.  The Status and Impact of Clinical Tumor Genome Sequencing. , 2019, Annual review of genomics and human genetics.

[13]  Robert B. Russell,et al.  Illuminating the Onco-GPCRome: Novel G protein–coupled receptor-driven oncocrine networks and targets for cancer immunotherapy , 2019, The Journal of Biological Chemistry.

[14]  Jason M. Sheltzer,et al.  Systematic identification of mutations and copy number alterations associated with cancer patient prognosis , 2018, eLife.

[15]  Pietro Lio',et al.  Pathway-based subnetworks enable cross-disease biomarker discovery , 2018, Nature Communications.

[16]  Zena Werb,et al.  Roles of the immune system in cancer: from tumor initiation to metastatic progression , 2018, Genes & development.

[17]  Adrian V. Lee,et al.  An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics , 2018, Cell.

[18]  Paul T. Spellman,et al.  The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma , 2018, Cell reports.

[19]  Steven J. M. Jones,et al.  The Immune Landscape of Cancer , 2018, Immunity.

[20]  A. Chinnaiyan,et al.  Precision oncology in the age of integrative genomics , 2018, Nature Biotechnology.

[21]  Mark D. M. Leiserson,et al.  Precision Oncology: The Road Ahead. , 2017, Trends in molecular medicine.

[22]  C. Lindskog,et al.  A pathology atlas of the human cancer transcriptome , 2017, Science.

[23]  Moriah H Nissan,et al.  OncoKB: A Precision Oncology Knowledge Base. , 2017, JCO precision oncology.

[24]  R. Young,et al.  Transcriptional Addiction in Cancer , 2017, Cell.

[25]  G. Hoser,et al.  Decreased Expression of SRSF2 Splicing Factor Inhibits Apoptotic Pathways in Renal Cancer , 2016, International journal of molecular sciences.

[26]  A. Krainer,et al.  Splicing-factor alterations in cancers , 2016, RNA.

[27]  M. Garnett,et al.  Perturbation-response genes reveal signaling footprints in cancer gene expression , 2016, bioRxiv.

[28]  Xiaokun Shen,et al.  microRNA-149 targets caspase-2 in glioma progression , 2016, Oncotarget.

[29]  J. Mesirov,et al.  The Molecular Signatures Database Hallmark Gene Set Collection , 2015 .

[30]  Jason G. Jin,et al.  Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes , 2015, Nature Medicine.

[31]  Trevor J Pugh,et al.  Oncotator: Cancer Variant Annotation Tool , 2015, Human mutation.

[32]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[33]  Eytan Domany,et al.  Using high-throughput transcriptomic data for prognosis: a critical overview and perspectives. , 2014, Cancer research.

[34]  Kate B. Cook,et al.  Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity , 2014, Cell.

[35]  Ming Yan,et al.  Transcriptional control of PAX4-regulated miR-144/451 modulates metastasis by suppressing ADAMs expression , 2014, Oncogene.

[36]  Saeed Tavazoie,et al.  Metastasis-suppressor transcript destabilization through TARBP2 binding of mRNA hairpins , 2014, Nature.

[37]  Brendan J. Frey,et al.  A compendium of RNA-binding motifs for decoding gene regulation , 2013, Nature.

[38]  C Blake Gilks,et al.  Poor Interobserver Reproducibility in the Diagnosis of High-grade Endometrial Carcinoma , 2013, The American journal of surgical pathology.

[39]  Michal Sheffer,et al.  Pathway-based personalized analysis of cancer , 2013, Proceedings of the National Academy of Sciences.

[40]  L. Stein,et al.  A network module-based method for identifying cancer prognostic signatures , 2012, Genome Biology.

[41]  Julian Downward,et al.  The GATA2 Transcriptional Network Is Requisite for RAS Oncogene-Driven Non-Small Cell Lung Cancer , 2012, Cell.

[42]  Reza Salavati,et al.  Systematic Discovery of Structural Elements Governing Mammalian mRNA Stability , 2012, Nature.

[43]  Ulrich Bodenhofer,et al.  APCluster: an R package for affinity propagation clustering , 2011, Bioinform..

[44]  Q. Cui,et al.  Identification of high-quality cancer prognostic markers and metastasis network modules , 2010, Nature communications.

[45]  D. Wolf,et al.  Faculty Opinions recommendation of Transcription factor Nrf1 mediates the proteasome recovery pathway after proteasome inhibition in mammalian cells. , 2010 .

[46]  O. Elemento,et al.  Revealing global regulatory perturbations across human cancers. , 2009, Molecular cell.

[47]  Michael J. Emanuele,et al.  A Genome-wide RNAi Screen Identifies Multiple Synthetic Lethal Interactions with the Ras Oncogene , 2009, Cell.

[48]  Mikael Bodén,et al.  MEME Suite: tools for motif discovery and searching , 2009, Nucleic Acids Res..

[49]  Eytan Domany,et al.  Association of survival and disease progression with chromosomal instability: A genomic exploration of colorectal cancer , 2009, Proceedings of the National Academy of Sciences.

[50]  Hemant Ishwaran,et al.  Random Survival Forests , 2008, Wiley StatsRef: Statistics Reference Online.

[51]  Ole Winther,et al.  JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update , 2007, Nucleic Acids Res..

[52]  N. Slonim,et al.  A universal framework for regulatory element discovery across all genomes and data types. , 2007, Molecular cell.

[53]  William Stafford Noble,et al.  Quantifying similarity between motifs , 2007, Genome Biology.

[54]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[55]  Jeffrey T. Chang,et al.  Oncogenic pathway signatures in human cancers as a guide to targeted therapies , 2006, Nature.

[56]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[57]  H. Lodish,et al.  Synergism between Transcription Factors TFE3 and Smad3 in Transforming Growth Factor-β-induced Transcription of theSmad7 Gene* , 2000, The Journal of Biological Chemistry.

[58]  Damian Smedley,et al.  Fusion of splicing factor genes PSF and NonO (p54nrb) to the TFE3 gene in papillary renal cell carcinoma , 1997, Oncogene.

[59]  Ash A. Alizadeh,et al.  SUPPLEMENTARY NOTE , 1879, Botanical Gazette.

[60]  OUP accepted manuscript , 2022, Bioinformatics.

[61]  Yihua Zhu,et al.  Signaling Pathway Analysis Combined With the Strength Variations of Interactions Between Genes Under Different Conditions , 2020, IEEE Access.

[62]  E. Vakiani,et al.  Logarithmic expansion of LGR5+ cells in human colorectal cancer. , 2018, Cellular signalling.

[63]  Stefanie Seiler,et al.  Finding Groups In Data , 2016 .

[64]  Jian Huang,et al.  Loss of FOXF2 Expression Predicts Poor Prognosis in Hepatocellular Carcinoma Patients , 2015, Annals of Surgical Oncology.

[65]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[66]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[67]  Jason M. Sheltzer,et al.  Genome-wide identification and analysis of prognostic features in human cancers , 2022 .