Results 51 to 60 of about 10,388,998 (324)

Not as simple as we thought: A rigorous examination of data aggregation in materials informatics

Digital Discovery
Recent Machine Learning (ML) developments have opened new perspectives on accelerating the discovery of new materials.
Federico Ottomano, G. de Felice, Vladimir V. Gusev, Taylor D. Sparks   +3 more
semanticscholar   +1 more source

Materials Discovery and Properties Prediction in Thermal Transport via Materials Informatics: A Mini Review. [PDF]

Nano letters (Print), 2019
There has been increasing demand for materials with functional thermal properties, but traditional experiments and simulations are high-cost and time-consuming.
Xiao Wan, Wentao Feng, Yunpeng Wang, Haidong Wang, Xing Zhang, Chengcheng Deng, Nuo Yang   +6 more
semanticscholar   +1 more source

Colorectal cancer‐derived FGF19 is a metabolically active serum biomarker that exerts enteroendocrine effects on mouse liver

Molecular Oncology, EarlyView.
Meta‐transcriptome analysis identified FGF19 as a peptide enteroendocrine hormone associated with colorectal cancer prognosis. In vivo xenograft models showed release of FGF19 into the blood at levels that correlated with tumor volumes. Tumoral‐FGF19 altered murine liver metabolism through FGFR4, thereby reducing bile acid synthesis and increasing ...
Jordan M. Beardsley, Michael W. Rohr, Joseph A. Goode, Sai Preethi Nakkina, Jignesh G. Parikh, Deborah A. Altomare   +5 more
wiley   +1 more source

Time-dependent density-functional theory for electronic excitations in materials: basics and perspectives [PDF]

, 2008
Time-dependent density-functional theory (TDDFT) is widely used to describe electronic excitations in complex finite systems with large numbers of atoms, such as biomolecules and nanocrystals.
Turkowski, V., Ullrich, C. A.
core   +1 more source

Creating an Understanding of Data Literacy for a Data-driven Society [PDF]

, 2016
Society has become increasingly reliant on data, making it necessary to ensure that all citizens are equipped with the skills needed to be data literate.
Cavero Montaner, Jose J., Gooch, Daniel, Kortuem, Gerd, Rashid, Umar, Wolff, Annika   +4 more
core   +2 more sources

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Molecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel, Peyman Zarrineh, Nathnael T. Tuffa, Jigar H. Sheth, Sumitra Mohan, Mudassar Iqbal, Sankari Nagarajan   +6 more
wiley   +1 more source

Genetic attenuation of ALDH1A1 increases metastatic potential and aggressiveness in colorectal cancer

Molecular Oncology, EarlyView.
Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova, Zuzana Kozovska, Ondrej Pos, Kristina Pavlov, Sachin Gulati, Peter Makovicky, Kristina Jakic, Monika Burikova, Eva Sedlackova, Barbora Svitkova, Silvia Tyciakova, Vojtech Bystry, Nicolas Blavet, Boris Tichy, Matej Hrnciar, Jaroslav Budis, Miroslav Tomas, Peter Dubovan, Georgina Kolnikova, Veronika Repaska, Nikoleta Mojzesova, Eva Zomborska, Daniel Pindak, Michal Mego, Tomas Szemes, Miroslava Matuskova   +25 more
wiley   +1 more source

Ionic species representations for materials informatics

APL Machine Learning
High-dimensional representations of the elements have become common within the field of materials informatics to build useful, structure-agnostic models for the chemistry of materials.
Anthony Onwuli, Keith T. Butler, Aron Walsh   +2 more
doaj   +1 more source

Reproducibility in materials informatics: lessons from ‘A general-purpose machine learning framework for predicting properties of inorganic materials’

Digital Discovery
Reproducing results from a foundational materials informatics tool (magpie) is difficult and in this study, a failure. This failure yields tangible suggestions to promote easy adoption and trust of materials informatics in the future.
D. Persaud, Logan Ward, J. Hattrick-Simpers   +2 more
semanticscholar   +1 more source

Network divergence analysis identifies adaptive gene modules and two orthogonal vulnerability axes in pancreatic cancer

Molecular Oncology, EarlyView.
Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson, Lyanne Delgado‐Coka, Natalia Marchenko, Luisa F. Escobar‐Hoyos, Kenneth R. Shroyer, Alisa Yurovsky, Trey Ideker, Gabor Balázsi, Thomas MacCarthy, Scott Powers   +9 more
wiley   +1 more source