Machine learning with in silico analysis markedly improves survival prediction modeling in colon cancer patients

Plasma Hsp90 levels in patients with systemic sclerosis and relation to lung and skin involvement: a cross-sectional and longitudinal study

(2021) Hana Štorkánová et al.   Scientific Reports

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

(2021) Christina Y. Yu et al.   Frontiers in Genetics

Integrative analysis from multi-center studies identities a consensus machine learning-derived lncRNA signature for stage II/III colorectal cancer

(2021) Zaoqu Liu et al.   EBioMedicine

Lipid raft‐disrupting miltefosine preferentially induces the death of colorectal cancer stem‐like cells

(2021) So‐Yeon Park et al.   Clinical and Translational Medicine

Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification

(2020) S. Rauschert et al.   Clinical Epigenetics

Predicting stage-specific cancer related genes and their dynamic modules by integrating multiple datasets

(2019) Chaima Aouiche et al.   BMC BIOINFORMATICS

A Novel Multi-Target Small Molecule, LCC-09, Inhibits Stemness and Therapy-Resistant Phenotypes of Glioblastoma Cells by Increasing miR-34a and Deregulating the DRD4/Akt/mTOR Signaling Axis

(2019) Wen et al.   Cancers

Copy number variation is highly correlated with differential gene expression: a pan-cancer study

(2019) Xin Shao et al.   BMC Medical Genetics

Interobserver variability in breast carcinoma grading results in prognostic stage differences

(2019) Kimmie Rabe et al.   HUMAN PATHOLOGY

Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach

(2019) Pushpanjali Gupta et al.   Cancers

Feature selection in machine learning: A new perspective

(2018) Jie Cai et al.   NEUROCOMPUTING

Applications of Bayesian network models in predicting types of hematological malignancies

(2018) Rupesh Agrahari et al.   Scientific Reports

Colorectal cancer susceptibility loci as predictive markers of rectal cancer prognosis after surgery

(2017) Yue Hu et al.   GENES CHROMOSOMES & CANCER

Prediction of lung cancer patient survival via supervised machine learning classification techniques

(2017) Chip M. Lynch et al.   INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS

Application of Machine Learning Approaches for Protein-protein Interactions Prediction

(2017) Mengying Zhang et al.   Medicinal Chemistry

DEOD: uncovering dominant effects of cancer-driver genes based on a partial covariance selection method

(2015) Bayarbaatar Amgalan et al.   BIOINFORMATICS

Machine learning applications in cancer prognosis and prediction

(2015) Konstantina Kourou et al.   Computational and Structural Biotechnology Journal

Comprehensive molecular characterization of human colon and rectal cancer

(2012)   NATURE

GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers

(2011) Craig H Mermel et al.   GENOME BIOLOGY

An Integrated Approach to Uncover Drivers of Cancer

(2010) Uri David Akavia et al.   CELL

Modeling Oncogenic Signaling in Colon Tumors by Multidirectional Analyses of Microarray Data Directed for Maximization of Analytical Reliability

(2010) Magdalena Skrzypczak et al.   PLoS One

Copy number variation influences gene expression and metabolic traits in mice

(2009) Luz D. Orozco et al.   HUMAN MOLECULAR GENETICS

Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm

(2009) Prateek Kumar et al.   Nature Protocols

Cross-Species Comparison of Human and Mouse Intestinal Polyps Reveals Conserved Mechanisms in Adenomatous Polyposis Coli (APC)-Driven Tumorigenesis

(2008) Claudia Gaspar et al.   AMERICAN JOURNAL OF PATHOLOGY

Chromosome copy number analysis in screening for prognosis-related genomic regions in colorectal carcinoma

(2008) Kentaro Kurashina et al.   CANCER SCIENCE