Results 61 to 70 of about 8,038,825 (297)

Addressing State Representation in Deep Reinforcement Learning: a critical analysis of state-of-the-art” methods

, 2022
openDeep Reinforcement Learning models use a Deep Neural Network to compute the Q-function, avoiding some computational and memory issues related to the Q-table in classic Reinforcement Learning. However, DeepRL models suffer from high sample complexity;
CANNAVÒ, FIAMMETTA
core  

Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction

Frontiers in Genetics, 2021
It is a vital task to design an integrated machine learning model to discover cancer subtypes and understand the heterogeneity of cancer based on multiple omics data.
Jian Liu, Jian Liu, Shuguang Ge, Shuguang Ge, Yuhu Cheng, Yuhu Cheng, Xuesong Wang, Xuesong Wang   +7 more
doaj   +1 more source

Representation Learning for Dynamic Graphs: A Survey

J. Mach. Learn. Res., 2019
Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs.
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain 0001, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart   +6 more
openaire   +4 more sources

Emerging experimental and computational methods for studying redox‐regulated structural transitions

FEBS Letters, EarlyView.
Redox reactions can reshape proteins and alter how they behave in cells, with important consequences for health and disease. This review explores emerging experimental and computational approaches for discovering these redox‐sensitive protein switches, revealing their structural effects, and predicting their behavior, opening new opportunities to ...
Tasneem Rass, Dana Reichmann, Gábor Erdős   +2 more
wiley   +1 more source

Unsupervised Representation Learning with Adaptive Multi-Order Structural Graph Fusion

Applied Sciences
High-dimensional unlabeled data often contain complex latent structures that are easily obscured by redundant features, noise, and unreliable neighborhood relationships.
Canyu Zhang, Yunjing Zhang, Jiawen Sun, Chen Ding, Pengfei Wan, Shaojun Shi, Sisi Wang, Shenfei Pei, Yanping Chen   +8 more
doaj   +1 more source

gat2vec: representation learning for attributed graphs

Computing, 2018
Network representation learning (NRL) enables the application of machine learning tasks such as classification, prediction and recommendation to networks. Apart from their graph structure, networks are often associated with diverse information in the form of attributes.
Nasrullah Sheikh, Zekarias T. Kefato, Alberto Montresor   +2 more
openaire   +3 more sources

Prospecting the protein design landscape

FEBS Letters, EarlyView.
This review outlines the current state of various protein design approaches. We discuss the current possibilities enabled by recently released tools, highlight future avenues to pursue in protein design, and underscore the crucial role of key databases and resources for successful protein design workflows.
Jakob R. Riccabona, Katharina T. Stonig, Jens Meiler, Clara T. Schoeder, Monica L. Fernández‐Quintero   +4 more
wiley   +1 more source

GFF-CARVING: Graph Feature Fusion for the Recognition of Highly Varying and Complex Balinese Carving Motifs

IEEE Access, 2022
The recognition of Balinese carving motifs is challenging due to the highly varying and interrelated motifs of Balinese carvings and in addition to the scantiness of Balinese carving data.
I Wayan Agus Surya Darma, Nanik Suciati, Daniel Siahaan   +2 more
doaj   +1 more source

Developmental programmes drive cellular plasticity, disease progression and therapy resistance in lung adenocarcinoma

Molecular Oncology, EarlyView.
This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska, Stephany Gallardo, Nur S. Zainal, Leena Arora, Matthew Ellis, Maria‐Antoinette Lopez, Judith Austine, Sai Pittla, Serena J Chee, Aiman Alzetani, Emily C Shaw, Christian H Ottensmeier, Gareth J Thomas, Christopher J Hanley   +13 more
wiley   +1 more source

Boosting Graph Contrastive Learning via Adaptive Sampling

, 2023
Contrastive learning (CL) is a prominent technique for self-supervised representation learning, which aims to contrast semantically similar (i.e., positive) and dissimilar (i.e., negative) pairs of examples under different augmented views.
Chen Gong, Tao, Dacheng, Wan, Sheng, Yibing Zhan, Gong, Chen, Shuo Chen, Shirui Pan, Jian Yang, Dacheng Tao, Yang, Jian, Zhan, Yibing, Chen, Shuo, Pan, Shirui, Sheng Wan   +13 more
core   +1 more source