Results 31 to 40 of about 2,611,781 (277)

Deep learning and geometric deep learning: An introduction for mathematicians and physicists

International Journal of Geometric Methods in Modern Physics, 2023
In this expository paper, we want to give a brief introduction, with few key references for further reading, to the inner functioning of the new and successful algorithms of Deep Learning and Geometric Deep Learning with a focus on Graph Neural Networks.
R. Fioresi, F. Zanchetta
openaire   +3 more sources

Quantum deep learning

Quantum Information and Computation, 2016
In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers.
Ashish Kapoor, Nathan Wiebe, Krysta M. Svore   +2 more
openaire   +3 more sources

From omics to AI—mapping the pathogenic pathways in type 2 diabetes

FEBS Letters, EarlyView.
Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan, Lu Qi, Pierre Zalloua   +2 more
wiley   +1 more source

Deep API learning

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, 2016
Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query, existing approaches utilize information retrieval models to search for matching API sequences.
Xiaodong Gu, Dongmei Zhang, Sunghun Kim, Hongyu Zhang   +3 more
openaire   +3 more sources

Data‐driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes

Molecular Oncology, EarlyView.
This study investigates gene expression differences between two major pediatric acute lymphoblastic leukemia (ALL) subtypes, B‐cell precursor ALL, and T‐cell ALL, using a data‐driven approach consisting of biostatistics and machine learning methods. Following analysis of a discovery dataset, we find a set of 14 expression markers differentiating the ...
Mona Nourbakhsh, Nikola Tom, Anna Schrøder Lassen, Helene Brasch Lind Petersen, Ulrik Kristoffer Stoltze, Karin Wadt, Kjeld Schmiegelow, Matteo Tiberti, Elena Papaleo   +8 more
wiley   +1 more source

Deep Learning At Scale and At Ease

, 2016
Recently, deep learning techniques have enjoyed success in various multimedia applications, such as image classification and multi-modal data analysis. Large deep learning models are developed for learning rich representations of complex data.
Beng Chin Ooi, Gang Chen, Haibo Chen, Jinyang Gao, Kian-Lee Tan, Meihui Zhang, Sheng Wang, Tien Tuan Anh Dinh, Wei Wang   +8 more
core   +1 more source

Systematic profiling of cancer‐fibroblast interactions reveals drug combinations in ovarian cancer

Molecular Oncology, EarlyView.
Fibroblasts, cells in the tumor environment, support ovarian cancer cell growth and alter morphology and drug response. We used fibroblast and cancer cell co‐culture models to test 528 drugs and discovered new drugs for combination treatment. We showed that adding Vorinostat or Birinapant to standard chemotherapy may improve drug response, suggesting ...
Greta Gudoityte, Osheen Sharma, Laura Leuenberger, Emelie Wallin, Josefin Fernebro, Päivi Östling, Rebecka Bergström, Johan Lindberg, Ulrika Joneborg, Olli Kallioniemi, Brinton Seashore‐Ludlow   +10 more
wiley   +1 more source

Deep Learning in the Wild

, 2018
Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains ...
Stefan Lörwald, Benjamin Bruno Meier, Marek Arnold, Lukas Tuggener, Katharina Rombach, Melanie Geiger, Gilbert François Duivesteijn, Thilo Stadelmann, Mohammadreza Amirian, Ismail Arabaci, Ismail Elezi   +10 more
openaire   +3 more sources

Deep learning in systems medicine [PDF]

Briefings in Bioinformatics, 2020
AbstractSystems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great ...
Wang, Haiying, PujosGuillot, Estelle, Comte, Blandine, de Miranda, Joao Luis, Spiwok, Vojtech, Chorbev, Ivan, Castiglione, Filippo, Tieri, Paolo, Watterson, Steven, McAllister, Roisin, de Melo Malaquias, Tiago, Zanin, Massimiliano, Rai, Taranjit Singh, Zheng, Huiru   +27 more
openaire   +6 more sources

Machine learning for identifying liver and pancreas cancers through comprehensive serum glycopeptide spectra analysis: a case‐control study

Molecular Oncology, EarlyView.
This study presents a novel AI‐based diagnostic approach—comprehensive serum glycopeptide spectra analysis (CSGSA)—that integrates tumor markers and enriched glycopeptides from serum. Using a neural network model, this method accurately distinguishes liver and pancreatic cancers from healthy individuals.
Motoyuki Kohjima, Yuko Takami, Ken Kawabe, Kazuhiro Tanabe, Chihiro Hayashi, Mikio Mikami, Tetsuya Kusumoto   +6 more
wiley   +1 more source