Results 31 to 40 of about 370,246 (216)

Molecular and functional profiling unravels targetable vulnerabilities in colorectal cancer

Molecular Oncology, EarlyView.
We used whole exome and RNA‐sequencing to profile divergent genomic and transcriptomic landscapes of microsatellite stable (MSS) and microsatellite instable (MSI) colorectal cancer. Alterations were classified using a computational score for integrative cancer variant annotation and prioritization.
Efstathios‐Iason Vlachavas, Konstantinos Voutetakis, Vivian Kosmidou, Spyridon Tsikalakis, Spyridon Roditis, Konstantinos Pateas, Ryangguk Kim, Kymberleigh Pagel, Stephan Wolf, Gregor Warsow, Antonia Dimitrakopoulou‐Strauss, Georgios N. Zografos, Alexander Pintzas, Johannes Betge, Olga Papadodima, Stefan Wiemann   +15 more
wiley   +1 more source

Deep learning for computational biology

Molecular Systems Biology, 2016
Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data
Tanel Pärnamaa, Tanel Pärnamaa, Oliver Stegle, Leopold Parts, Leopold Parts, Christof Angermueller   +5 more
openaire   +4 more sources

Classification of acute myeloid leukemia based on multi‐omics and prognosis prediction value

Molecular Oncology, EarlyView.
The Unsupervised AML Multi‐Omics Classification System (UAMOCS) integrates genomic, methylation, and transcriptomic data to categorize AML patients into three subtypes (UAMOCS1‐3). This classification reveals clinical relevance, highlighting immune and chromosomal characteristics, prognosis, and therapeutic vulnerabilities.
Yang Song, Zhe Wang, Guangji Zhang, Jiangxue Hou, Kaiqi Liu, Shuning Wei, Yan Li, Chunlin Zhou, Dong Lin, Min Wang, Hui Wei, Jianxiang Wang, Tao Cheng, Yingchang Mi   +13 more
wiley   +1 more source

Transcriptome‐wide analysis of circRNA and RBP profiles and their molecular relevance for GBM

Molecular Oncology, EarlyView.
CircRNAs are differentially expressed in glioblastoma primary tumors and might serve as therapeutic targets and diagnostic markers. The investigation of circRNA and RNA‐binding proteins (RBPs) interactions shows that distinct RBPs play a role in circRNA biogenesis and function.
Julia Latowska‐Łysiak, Żaneta Zarębska, Marcin P. Sajek, Adriana Grabowska, Alessia Buratin, Paweł Głodowicz, Julia O. Misiorek, Konrad Kuczyński, Stefania Bortoluzzi, Marek Żywicki, Jan G. Kosiński, Agnieszka Rybak‐Wolf, Rafał Piestrzeniewicz, Anna M. Barciszewska, Katarzyna Rolle   +14 more
wiley   +1 more source

Exploration of heterogeneity and recurrence signatures in hepatocellular carcinoma

Molecular Oncology, EarlyView.
This study leveraged public datasets and integrative bioinformatic analysis to dissect malignant cell heterogeneity between relapsed and primary HCC, focusing on intercellular communication, differentiation status, metabolic activity, and transcriptomic profiles.
Wen‐Jing Wu, Jianchao Wang, Fuqing Chen, Xuefeng Wang, Bin Lan, Ruyi Fu, Hong Wen, Fangfang Chen, Wei Hong, Tian‐Yu Tang, Ying He, Gang Chen, Jianyin Zhou, Hai‐Long Piao, Di Chen, Shu‐Yong Lin   +15 more
wiley   +1 more source

Computational system biology [PDF]

Nature Precedings, 2012
AbstractSystems biology is a multi-disciplinary field that deals with mechanisms involved in complex biological processes by considering them as integrated systems of multiple interacting components. The huge amount of data involved in this study necessitates the use of computational tools.
openaire   +2 more sources

Methods in Computational Biology [PDF]

, 2019
Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data.
Ross P. Carlson, Herbert M. Sauro
openaire   +2 more sources

Comparing self‐reported race and genetic ancestry for identifying potential differentially methylated sites in endometrial cancer: insights from African ancestry proportions using machine learning models

Molecular Oncology, EarlyView.
Integrating ancestry, differential methylation analysis, and machine learning, we identified robust epigenetic signature genes (ESGs) and Core‐ESGs in Black and White women with endometrial cancer. Core‐ESGs (namely APOBEC1 and PLEKHG5) methylation levels were significantly associated with survival, with tumors from high African ancestry (THA) showing ...
Huma Asif, J. Julie Kim
wiley   +1 more source

Escape from TGF‐β‐induced senescence promotes aggressive hallmarks in epithelial hepatocellular carcinoma cells

Molecular Oncology, EarlyView.
Chronic TGF‐β exposure drives epithelial HCC cells from a senescent state to a TGF‐β resistant mesenchymal phenotype. This transition is characterized by the loss of Smad3‐mediated signaling, escape from senescence, enhanced invasiveness and metastatic potential, and upregulation of key resistance modulators such as MARK1 and GRM8, ultimately promoting
Minenur Kalyoncu, Dilara Demirci, Sude Eris, Bengisu Dayanc, Ece Cakiroglu, Merve Basol, Merve Uysal, Gulcin Cakan‐Akdogan, Fang Liu, Mehmet Ozturk, Gökhan Karakülah, Serif Senturk   +11 more
wiley   +1 more source

Time, the final frontier

Molecular Oncology, EarlyView.
This article advocates integrating temporal dynamics into cancer research. Rather than relying on static snapshots, researchers should increasingly consider adopting dynamic methods—such as live imaging, temporal omics, and liquid biopsies—to track how tumors evolve over time.
Gautier Follain, Michal Dibus, Omkar Joshi, Guillaume Jacquemet   +3 more
wiley   +1 more source