Results 61 to 70 of about 977,116 (306)

Machine-Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes

The Journal of Physical Chemistry A, 2023
A primary challenge in organic molecular crystal structure prediction (CSP) is accurately ranking the energies of potential structures. While high-level solid-state density functional theory (DFT) methods allow for mostly reliable discrimination of the low energy structures, their high computational cost is problematic because of the need to evaluate ...
Patrick W. V. Butler, Roohollah Hafizi, Graeme Day   +2 more
openaire   +4 more sources

Improving PARP inhibitor efficacy in bladder cancer without genetic BRCAness by combination with PLX51107

Molecular Oncology, EarlyView.
Clinical trials on PARP inhibitors in urothelial carcinoma (UC) showed limited efficacy and a lack of predictive biomarkers. We propose SLFN5, SLFN11, and OAS1 as UC‐specific response predictors. We suggest Talazoparib as the better PARP inhibitor for UC than Olaparib.
Jutta Schmitz, Anna L. Bartkowiak, Michael Rose, Nora Kolks, Patrick Petzsch, Vandana Solanki, Anne Stoffel, Bianca Faßbender, Leandra Lepping, Julka Volkamer, Karl Köhrer, Marc Seifert, Tokameh Mahmoudi, Tahlita C. M. Zuiverloon, Günter Niegisch, Michèle J. Hoffmann   +15 more
wiley   +1 more source

Kernel learning for ligand-based virtual screening: discovery of a new PPARgamma agonist [PDF]

, 2010
Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual ...
Hansen, Katja, Müller, Klaus-Robert, Proschak, Ewgenij, Rau, Oliver, Rupp, Matthias, Schneider, Gisbert (Prof. Dr.), Schroeter, Timon, Schubert-Zsilavecz, Manfred (Prof. Dr.), Steri, Ramona, Zettl, Heiko   +9 more
core  

A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

, 2011
The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable ...
Hassanzadeh, Hamed, Keyvanpour, MohammadReza   +1 more
core   +1 more source

Machine learning interatomic potential for silicon-nitride (Si3N4) by active learning

The Journal of Chemical Physics, 2023
Silicon nitride (Si3N4) is an extensively used material in the automotive, aerospace, and semiconductor industries. However, its widespread use is in contrast to the scarce availability of reliable interatomic potentials that can be employed to study various aspects of this material on an atomistic scale, particularly its amorphous phase. In this work,
Diego Milardovich, Christoph Wilhelmer, Dominic Waldhoer, Lukas Cvitkovich, Ganesh Sivaraman, Tibor Grasser   +5 more
openaire   +2 more sources

Perspectives in educating molecular pathologists on liquid biopsy: Toward integrative, equitable, and decentralized precision oncology

Molecular Oncology, EarlyView.
Liquid biopsy enables minimally invasive, real‐time molecular profiling through analysis of circulating biomarkers in biological fluids. This Perspective highlights the importance of training pathologists through integrative educational programs, such as the European Masters in Molecular Pathology, to ensure effective and equitable implementation of ...
Marius Ilié, Umberto Malapelle, Catherine Alix‐Panabières, Claus Lindbjerg Andersen, Sandra Chlebowski, Vivien Lake, Caroline Lacoux, Virginie Lespinet‐Fabre, Olivier Bordone, Simon Heeke, Christophe Bontoux, Ellen Heitzer, Klaus Pantel, Paul Hofman   +13 more
wiley   +1 more source

Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea

Life, 2022
This study was a multicenter retrospective cohort study of term nulliparous women who underwent labor, and was conducted to develop an automated machine learning model for prediction of emergent cesarean section (CS) before onset of labor.
Jeong Ha Wie, Se Jin Lee, Sae Kyung Choi, Yun Sung Jo, Han Sung Hwang, Mi Hye Park, Yeon Hee Kim, Jae Eun Shin, Ki Cheol Kil, Su Mi Kim, Bong Suk Choi, Hanul Hong, Hyun-Joo Seol, Hye-Sung Won, Hyun Sun Ko, Sunghun Na   +15 more
doaj   +1 more source

A Cognitive Science Based Machine Learning Architecture [PDF]

, 2006
In an attempt to illustrate the application of cognitive science principles to hard AI problems in machine learning we propose the LIDA technology, a cognitive science based architecture capable of more human-like learning. A LIDA based software agent or
Baars, Bernard J., D'Mello, S.K., Franklin, Stan, Ramamurthy, Uma   +3 more
core   +1 more source

An Active Instance-based Machine Learning method for Stellar Population Studies

, 2005
We have developed a method for fast and accurate stellar population parameters determination in order to apply it to high resolution galaxy spectra. The method is based on an optimization technique that combines active learning with an instance-based ...
Fuentes, Olac, Solorio, Thamar, Terlevich, Elena, Terlevich, Roberto   +3 more
core   +1 more source

Supervised machine learning and active learning in classification of radiology reports [PDF]

Journal of the American Medical Informatics Association, 2014
This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry.In addition to ...
Dung H M, Nguyen, Jon D, Patrick
openaire   +2 more sources