Applications of support vector machine (SVM) learning in cancer genomics
S Huang, N Cai, PP Pacheco… - Cancer genomics & …, 2018 - cgp.iiarjournals.org
Machine learning with maximization (support) of separating margin (vector), called support
vector machine (SVM) learning, is a powerful classification tool that has been used for …
vector machine (SVM) learning, is a powerful classification tool that has been used for …
Challenges in the introduction of next-generation sequencing (NGS) for diagnostics of myeloid malignancies into clinical routine use
U Bacher, E Shumilov, J Flach, N Porret… - Blood cancer …, 2018 - nature.com
Given the vast phenotypic and genetic heterogeneity of acute and chronic myeloid
malignancies, hematologists have eagerly awaited the introduction of next-generation …
malignancies, hematologists have eagerly awaited the introduction of next-generation …
Genome-wide mutational spectra analysis reveals significant cancer-specific heterogeneity
Cancer is widely recognized as a genetic disease in which somatic mutations are
sequentially accumulated to drive tumor progression. Although genomic landscape studies …
sequentially accumulated to drive tumor progression. Although genomic landscape studies …
Inferring subgroup-specific driver genes from heterogeneous cancer samples via subspace learning with subgroup indication
Motivation Detecting driver genes from gene mutation data is a fundamental task for
tumorigenesis research. Due to the fact that cancer is a heterogeneous disease with various …
tumorigenesis research. Due to the fact that cancer is a heterogeneous disease with various …
Oncofuse: a computational framework for the prediction of the oncogenic potential of gene fusions
Motivation: Gene fusions resulting from chromosomal aberrations are an important cause of
cancer. The complexity of genomic changes in certain cancer types has hampered the …
cancer. The complexity of genomic changes in certain cancer types has hampered the …
Machine learning methods for prediction of cancer driver genes: a survey paper
R Andrades… - Briefings in …, 2022 - academic.oup.com
Identifying the genes and mutations that drive the emergence of tumors is a critical step to
improving our understanding of cancer and identifying new directions for disease diagnosis …
improving our understanding of cancer and identifying new directions for disease diagnosis …
Pan-cancer analysis on microRNA-associated gene activation
Abstract Background While microRNAs (miRNAs) were widely considered to repress target
genes at mRNA and/or protein levels, emerging evidence from in vitro experiments has …
genes at mRNA and/or protein levels, emerging evidence from in vitro experiments has …
[HTML][HTML] The TP53 gene rs1042522 C> G polymorphism and neuroblastoma risk in Chinese children
TP53, a tumor suppressor gene, plays a critical role in cell cycle control, apoptosis, and DNA
damage repair. Previous studies have indicated that the TP53 gene Arg72Pro (rs1042522 …
damage repair. Previous studies have indicated that the TP53 gene Arg72Pro (rs1042522 …
Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation
Purpose Pseudoprogression (PsP) can mimic true tumor progression (TTP) on magnetic
resonance imaging in patients with glioblastoma multiform (GBM). The phenotypical …
resonance imaging in patients with glioblastoma multiform (GBM). The phenotypical …
[HTML][HTML] Identification of biomarkers for pseudo and true progression of GBM based on radiogenomics study
The diagnosis for pseudoprogression (PsP) and true tumor progression (TTP) of GBM is a
challenging task in clinical practices. The purpose of this study is to identify potential genetic …
challenging task in clinical practices. The purpose of this study is to identify potential genetic …