| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A deep learning based multi-model approach for predicting drug-like chemical compound’s toxicity
|
Saravanan, Konda Mani
|
|
|
226 |
C |
p. 164-175
|
artikel
|
| 2 |
Chromatin image-driven modelling
|
Kadlof, Michał
|
|
|
226 |
C |
p. 54-60
|
artikel
|
| 3 |
Current methodologies in studying chromatin and gene expression
|
Bhaumik, Sukesh R.
|
|
|
226 |
C |
p. 19-20
|
artikel
|
| 4 |
DEEP-EP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery
|
Ali, Farman
|
|
|
226 |
C |
p. 49-53
|
artikel
|
| 5 |
Deep learning methods in biomedical informatics
|
Zhang, Jinli
|
|
|
226 |
C |
p. 162-163
|
artikel
|
| 6 |
Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues
|
Huang, Guohua
|
|
|
226 |
C |
p. 1-8
|
artikel
|
| 7 |
DNA shape features improve prediction of CRISPR/Cas9 activity
|
Vora, Dhvani Sandip
|
|
|
226 |
C |
p. 120-126
|
artikel
|
| 8 |
DTKGIN: Predicting drug-target interactions based on knowledge graph and intent graph
|
Luo, Yi
|
|
|
226 |
C |
p. 21-27
|
artikel
|
| 9 |
Editorial: Artificial intelligence in drug discovery and development
|
Wei, Leyi
|
|
|
226 |
C |
p. 133-137
|
artikel
|
| 10 |
Editorial Board
|
|
|
|
226 |
C |
p. ii
|
artikel
|
| 11 |
Experimental and computational approaches for membrane protein insertion and topology determination
|
Duart, Gerard
|
|
|
226 |
C |
p. 102-119
|
artikel
|
| 12 |
Exploring GPCR conformational dynamics using single-molecule fluorescence
|
Agyemang, Eugene
|
|
|
226 |
C |
p. 35-48
|
artikel
|
| 13 |
Image 1 AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs
|
Ghosh, Madhusudan
|
|
|
226 |
C |
p. 78-88
|
artikel
|
| 14 |
Language model based on deep learning network for biomedical named entity recognition
|
Hou, Guan
|
|
|
226 |
C |
p. 71-77
|
artikel
|
| 15 |
m6Aexpress-enet: Predicting the regulatory expression m6A sites by an enet-regularization negative binomial regression model
|
Zhang, Teng
|
|
|
226 |
C |
p. 61-70
|
artikel
|
| 16 |
MRUNet-3D: A multi-stride residual 3D UNet for lung nodule segmentation
|
Bbosa, Ronald
|
|
|
226 |
C |
p. 89-101
|
artikel
|
| 17 |
Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network
|
Afroz, Sabrin
|
|
|
226 |
C |
p. 138-150
|
artikel
|
| 18 |
Non-canonical amino acids and protein dynamics
|
Antony, Edwin
|
|
|
226 |
C |
p. 161
|
artikel
|
| 19 |
Pipelined biomedical event extraction rivaling joint learning
|
Wu, Pengchao
|
|
|
226 |
C |
p. 9-18
|
artikel
|
| 20 |
Predicting lysine methylation sites using a convolutional neural network
|
Spadaro, Austin
|
|
|
226 |
C |
p. 127-132
|
artikel
|
| 21 |
Prediction of cell-type-specific cohesin-mediated chromatin loops based on chromatin state
|
Liu, Li
|
|
|
226 |
C |
p. 151-160
|
artikel
|
| 22 |
The bioaccessibility and tolerability of marine-derived sources of magnesium and calcium
|
Dowley, Alison
|
|
|
226 |
C |
p. 28-34
|
artikel
|
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