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Found 14 papers, 5 papers with code
Attention-based Interpretable Regression of Gene Expression in Histology
Interpretability of deep learning is widely used to evaluate the reliability of medical imaging models and reduce the risks of inaccurate patient recommendations.
TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks
Scarcity of data and a large sequence space make this task challenging, and to date only models limited to a small set of epitopes have achieved good performance.
On the Importance of Looking at the Manifold
We explore a spectrum of models, ranging from uniquely learning representations based on the isolated features of the nodes (focusing on Variational Autoencoders), to uniquely learning representations based on the topology (using node2vec) passing through models that integrate both node features and topological information in a hybrid fashion.
Learning Invariances for Interpretability using Supervised VAE
If we understand a problem, we may introduce inductive biases in our model in the form of invariances.
On quantitative aspects of model interpretability
Despite the growing body of work in interpretable machine learning, it remains unclear how to evaluate different explainability methods without resorting to qualitative assessment and user-studies.
PaccMann$^{RL}$ on SARS-CoV-2: Designing antiviral candidates with conditional generative models
With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents.
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data
We explore and compare three methods in order to perform unsupervised stress detection: 1) traditional K-Means clustering with engineered time and frequency domain features 2) convolutional autoencoders and 3) long short-term memory (LSTM) autoencoders, both trained on the raw RRI measurements combined with DBSCAN clustering and K-Nearest-Neighbors classification.
MonoNet: Towards Interpretable Models by Learning Monotonic Features
Being able to interpret, or explain, the predictions made by a machine learning model is of fundamental importance.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning
The generative process is optimized through PaccMann, a previously developed drug sensitivity prediction model to obtain effective anticancer compounds for the given context (i. e., transcriptomic profile).
Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders
In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder.
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to building discriminative node and graph embeddings.
Ranked #27 on
Graph Classification
on MUTAG
Inference of the three-dimensional chromatin structure and its temporal behavior
In this work, we explore the idea of manifold learning for the 3D chromatin structure inference and present a novel method, REcurrent Autoencoders for CHromatin 3D structure prediction (REACH-3D).
PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks
Our models ingest a drug-cell pair consisting of SMILES encoding of a compound and the gene expression profile of a cancer cell and predicts an IC50 sensitivity value.
PIMKL: Pathway Induced Multiple Kernel Learning
Reliable identification of molecular biomarkers is essential for accurate patient stratification.
