| Nome |
# |
|
RNA-Seq Analysis: Methods, Applications and Challenges, file e0feeaa9-5ac0-44d2-e053-6605fe0a8db0
|
535
|
|
Segmentation of Aorta 3D CT Images Based on 2D Convolutional Neural Networks, file e0feeaaa-96a2-44d2-e053-6605fe0a8db0
|
231
|
|
Editorial: RNA-Seq Analysis: Methods, Applications and Challenges, file e0feeaa8-c215-44d2-e053-6605fe0a8db0
|
211
|
|
Confidence measures for deep learning in domain adaptation, file e0feeaa8-3d54-44d2-e053-6605fe0a8db0
|
135
|
|
A Multi-Stage GAN for Multi-Organ Chest X-ray Image Generation and Segmentation, file e0feeaaa-8464-44d2-e053-6605fe0a8db0
|
130
|
|
A Two-Stage GAN for High-Resolution Retinal Image Generation and Segmentation, file e0feeaab-14e6-44d2-e053-6605fe0a8db0
|
128
|
|
GNNkeras: A Keras-based library for Graph Neural Networks and homogeneous and heterogeneous graph processing, file e0feeaab-8bd6-44d2-e053-6605fe0a8db0
|
77
|
|
A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data, file e0feeaab-02be-44d2-e053-6605fe0a8db0
|
74
|
|
Visual Sequential Search Test Analysis: An Algorithmic Approach, file e0feeaaa-bb73-44d2-e053-6605fe0a8db0
|
69
|
|
Advanced Computing and Intelligent Technologies: Proceedings of ICACIT 2022, file 9c9161fc-d95f-41c2-84f1-b297a03801c6
|
67
|
|
Preface to ”Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications”, file e0feeaab-bc04-44d2-e053-6605fe0a8db0
|
62
|
|
Deep Learning Approaches for the Segmentation of Glomeruli in Kidney Histopathological Images, file e0feeaab-d7fd-44d2-e053-6605fe0a8db0
|
55
|
|
Advanced Computing and Intelligent Technologies: Proceedings of ICACIT 2021, file f41f2906-5dad-45e9-a673-1c78889b2fd2
|
53
|
|
A Neural Network Approach for the Analysis of Reproducible Ribo–Seq Profiles, file ec7d15f0-c176-4c09-937c-8c82b2ae9625
|
50
|
|
MicroRNA signature for interpretable breast cancer classification with subtype clue, file e0feeaab-e3b5-44d2-e053-6605fe0a8db0
|
35
|
|
Machine Learning for Robotics Applications, file 6c44348e-f1c0-4371-94fe-606ff78f45d6
|
33
|
|
No PAIN no Gain: More Expressive GNNs with Paths, file 23d95b3a-bc23-4a21-bd34-25c75c8e195d
|
22
|
|
Graph-Based Integration of Histone Modification Profiles, file e0feeaab-c8b0-44d2-e053-6605fe0a8db0
|
19
|
|
A Bioinformatics Approach to Investigate Structural and Non-Structural Proteins in Human Coronaviruses, file 97aa3ae7-488c-4735-b709-f1867a00948c
|
15
|
|
Graph Neural Networks for the Prediction of Protein–Protein Interfaces, file e0feeaa9-34fe-44d2-e053-6605fe0a8db0
|
12
|
|
Modular multi-source prediction of drug side-effects with DruGNN, file 1708d768-a275-440d-b2a5-5e030e68bbed
|
11
|
|
Molecular generative Graph Neural Networks for Drug Discovery, file e0feeaaa-2b98-44d2-e053-6605fe0a8db0
|
11
|
|
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities, file 44f1d8aa-f7c9-405c-b1eb-8e1c08b92340
|
9
|
|
Web spam detection using transductive-inductive Graph Neural Networks, file e0feeaa5-5650-44d2-e053-6605fe0a8db0
|
7
|
|
Deep Learning Techniques for Text Generation to Support Augmentative and Alternative Communication, file f0a0769c-fa7c-44be-b4ce-b6b967e3ea93
|
7
|
|
A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs, file 1d99359f-86e9-4d1a-9472-a4afd5399c2c
|
6
|
|
Automatic image classification for the urinoculture screening, file e0feeaa5-5598-44d2-e053-6605fe0a8db0
|
6
|
|
Fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep Learning, file e0feeaa8-3d56-44d2-e053-6605fe0a8db0
|
6
|
|
Structural Bioinformatics to unveil weaknesses of coronavirus spike glycoprotein stability, file e0feeaa9-379b-44d2-e053-6605fe0a8db0
|
6
|
|
BioGNN: How Graph Neural Networks Can Solve Biological Problems, file 5ee96df8-b289-4119-8353-6cf4518d8e35
|
5
|
|
On the complexity of shallow and deep neural network classifiers, file e0feeaa4-bc64-44d2-e053-6605fe0a8db0
|
5
|
|
Generating Bounding Box Supervision for Semantic Segmentation with Deep Learning, file e0feeaa7-bb32-44d2-e053-6605fe0a8db0
|
5
|
|
Deep Neural Networks for Structured Data, file e0feeaa7-d4af-44d2-e053-6605fe0a8db0
|
5
|
|
Analysis of brain NMR images for age estimation with deep learning, file e0feeaa8-7d9f-44d2-e053-6605fe0a8db0
|
5
|
|
Deep learning techniques for biomedical data processing, file 73d3004b-824a-4097-ad0a-c796c7c74164
|
4
|
|
A neural network approach to similarity learning, file e0feeaa4-dacd-44d2-e053-6605fe0a8db0
|
4
|
|
Automatic image classification for the urinoculture screening, file e0feeaa5-94ab-44d2-e053-6605fe0a8db0
|
4
|
|
Inductive–Transductive Learning with Graph Neural Networks, file e0feeaa7-ca77-44d2-e053-6605fe0a8db0
|
4
|
|
Weak supervision for generating pixel–level annotations in scene text segmentation, file e0feeaa9-4cad-44d2-e053-6605fe0a8db0
|
4
|
|
Structural bioinformatics survey on disease-inducing missense mutations, file e0feeaaa-2b97-44d2-e053-6605fe0a8db0
|
4
|
|
Point-Wise Ribosome Translation Speed Prediction with Recurrent Neural Networks, file 2f781f34-d15e-4df9-869d-2c6797181c0f
|
3
|
|
Multi-stage Synthetic Image Generation for the Semantic Segmentation of Medical Images, file 3c082cbb-5c1e-4d7d-9331-602d2fbdfbfe
|
3
|
|
Visual Sequencing Search Strategy in Parkinson's Disease, file 580f9cd9-1c72-410e-96f6-6d1e0e9f6fc9
|
3
|
|
On the complexity of neural network classifiers: a comparison between shallow and deep architectures, file e0feeaa5-0383-44d2-e053-6605fe0a8db0
|
3
|
|
A comparative study of inductive and transductive learning with feedforward neural networks, file e0feeaa6-20ef-44d2-e053-6605fe0a8db0
|
3
|
|
Extraction of high level visual features for the automatic recognition of UTIs, file e0feeaa6-21a0-44d2-e053-6605fe0a8db0
|
3
|
|
A possible strategy to fight COVID-19: Interfering with spike glycoprotein trimerization, file e0feeaa9-11ed-44d2-e053-6605fe0a8db0
|
3
|
|
A deep attention network for predicting amino acid signals in the formation of α-helices, file e0feeaa9-34fc-44d2-e053-6605fe0a8db0
|
3
|
|
Robust Prostate Cancer Classification with Siamese Neural Networks, file e0feeaa9-5634-44d2-e053-6605fe0a8db0
|
3
|
|
Modelling taxi drivers' behaviour for the next destination prediction, file e0feeaa9-f007-44d2-e053-6605fe0a8db0
|
3
|
|
Smart gravimetric system based on Deep Learning for enhanced safety of accesses to public places, file e0feeaaa-0758-44d2-e053-6605fe0a8db0
|
3
|
|
Smart Gravimetric System for Enhanced Security of Accesses to Public Places Embedding a MobileNet Neural Network Classifier, file e0feeaab-b08d-44d2-e053-6605fe0a8db0
|
3
|
|
A Mobile App for Detecting Potato Crop Diseases, file fd5c5af4-088c-40ef-bd96-bedc7f7c2416
|
3
|
|
A Discrete Geometry Method for Atom Depth Computation in Complex Molecular Systems, file 5352a6dd-b8a9-4d4d-ab6c-542648709a59
|
2
|
|
Learning long-term dependencies using layered graph neural networks, file e0feeaa4-dd91-44d2-e053-6605fe0a8db0
|
2
|
|
Does Terminal Attractor Guarantee Global Convergence?, file e0feeaa4-de1d-44d2-e053-6605fe0a8db0
|
2
|
|
Inside PageRank, file e0feeaa4-e7d2-44d2-e053-6605fe0a8db0
|
2
|
|
An unobtrusive sleep monitoring system for the human sleep behaviour understanding, file e0feeaa6-2e28-44d2-e053-6605fe0a8db0
|
2
|
|
A new integrated and interactive tool applicable to inborn errors of metabolism: Application to alkaptonuria, file e0feeaa7-b326-44d2-e053-6605fe0a8db0
|
2
|
|
A deep learning approach to bacterial colony segmentation, file e0feeaa7-c0d2-44d2-e053-6605fe0a8db0
|
2
|
|
AKUImg: A database of cartilage images of Alkaptonuria patients, file e0feeaa9-34fb-44d2-e053-6605fe0a8db0
|
2
|
|
On inductive-transductive learning with Graph Neural Networks, file e0feeaa9-cbc2-44d2-e053-6605fe0a8db0
|
2
|
|
Complex Data: Learning Trustworthily, Automatically, and with Guarantees, file e0feeaaa-b103-44d2-e053-6605fe0a8db0
|
2
|
|
Preface to Machine Learning for Robotics Applications, file e0feeaaa-d8ef-44d2-e053-6605fe0a8db0
|
2
|
|
Towards learning trustworthily, automatically, and with guarantees on graphs: An overview, file e0feeaab-7d81-44d2-e053-6605fe0a8db0
|
2
|
|
Structural bioinformatic survey of protein-small molecule interfaces delineates the role of glycine in surface pocket formation, file e0feeaab-d5af-44d2-e053-6605fe0a8db0
|
2
|
|
Blinking Rate Comparison Between Patients with Chronic Pain and Parkinson's Disease, file ee224ea5-3f0b-45f1-b950-9bdb686078c1
|
2
|
|
From Pixels to Diagnosis: AI-Driven Skin Lesion Recognition, file dc9ce171-587f-4d76-9ff9-93e157bd7679
|
1
|
|
Identification of parameters in polymer crystallization, file e0feeaa4-ca3a-44d2-e053-6605fe0a8db0
|
1
|
|
Supervised Neural Network Learning: from Vectors to Graphs, file e0feeaa4-cbed-44d2-e053-6605fe0a8db0
|
1
|
|
Standardizzazione isogravità di un case-mix ospedaliero mediante Charlson index, file e0feeaa4-ced4-44d2-e053-6605fe0a8db0
|
1
|
|
Optimal Learning in Artificial Neural Networks, file e0feeaa4-d0e9-44d2-e053-6605fe0a8db0
|
1
|
|
Advances in Neural Information Processing Paradigms, file e0feeaa4-d0ec-44d2-e053-6605fe0a8db0
|
1
|
|
Recursive neural networks and graphs: dealing with cycles, file e0feeaa4-dac4-44d2-e053-6605fe0a8db0
|
1
|
|
Recursive Neural Networks and Their Applications to Image Processing, file e0feeaa4-dc08-44d2-e053-6605fe0a8db0
|
1
|
|
A cyclostationary neural network model for the prediction of the NO2 concentration, file e0feeaa4-dd1d-44d2-e053-6605fe0a8db0
|
1
|
|
Recursive Neural Networks for Processing Graphs with Labelled Edges, file e0feeaa4-e24d-44d2-e053-6605fe0a8db0
|
1
|
|
Recursive Neural Networks for Processing Graphs with Labelled Edges: Theory and Applications, file e0feeaa4-e4a9-44d2-e053-6605fe0a8db0
|
1
|
|
Automatic Image Analysis and Classification for Urinary Bacteria Infection Screening, file e0feeaa5-6b96-44d2-e053-6605fe0a8db0
|
1
|
|
Discovering potential clinical profiles of multiple sclerosis from clinical and pathological free text data with constrained non-negative matrix factorization, file e0feeaa6-2bcb-44d2-e053-6605fe0a8db0
|
1
|
|
COCO_TS Dataset: Pixel–Level Annotations Based on Weak Supervision for Scene Text Segmentation, file e0feeaa8-40c9-44d2-e053-6605fe0a8db0
|
1
|
|
Understanding Human Sleep Behaviour by Machine Learning, file e0feeaa8-4455-44d2-e053-6605fe0a8db0
|
1
|
|
A transcriptional study of oncogenes and tumor suppressors altered by copy number variations in ovarian cancer, file e0feeaa8-fc19-44d2-e053-6605fe0a8db0
|
1
|
|
Image generation by GAN and style transfer for agar plate image segmentation, file e0feeaa9-77ae-44d2-e053-6605fe0a8db0
|
1
|
|
Multi-Modal Siamese Network for Diagnostically Similar Lesion Retrieval in Prostate MRI, file e0feeaa9-febf-44d2-e053-6605fe0a8db0
|
1
|
|
A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi, file e0feeaaa-497b-44d2-e053-6605fe0a8db0
|
1
|
|
Structural Bioinformatics to unveil weaknesses of coronavirus spike glycoprotein stability, file e0feeaaa-e386-44d2-e053-6605fe0a8db0
|
1
|
|
A Deep Learning Approach to Analyze NMR Spectra of SH-SY5Y Cells for Alzheimer’s Disease Diagnosis, file f54421b2-f416-4fcf-85d2-713adb09fc52
|
1
|
| Totale |
2.219 |