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Found 10 papers, 6 papers with code
PADME-SoSci: A Platform for Analytics and Distributed Machine Learning for the Social Sciences
PADME uses a federated approach where the model is implemented and deployed by all parties and visits each data location incrementally for training.
Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective
Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning.
A Biomedical Knowledge Graph for Biomarker Discovery in Cancer
However, exploration and querying large-scale KGs is tedious for certain groups of users due to a lack of knowledge about underlying data assets or semantic technologies.
Explainable AI for Bioinformatics: Methods, Tools, and Applications
Explainable artificial intelligence (XAI) aims to overcome the opaqueness of black-box models and provide transparency in how AI systems make decisions.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Towards General Deep Leakage in Federated Learning
We find that image restoration fails even if there is only one incorrectly inferred label in the batch; we also find that when batch images have the same label, the corresponding image is restored as a fusion of that class of images.
Biases in Data Science Lifecycle
In recent years, data science has become an indispensable part of our society.
Computers and Society
DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images
Amid the coronavirus disease(COVID-19) pandemic, humanity experiences a rapid increase in infection numbers across the world.
OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data
In this paper, we propose a new approach called OncoNetExplainer to make explainable predictions of cancer types based on GE data.
Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network
Existing data-driven prediction approaches for DDIs typically rely on a single source of information, while using information from multiple sources would help improve predictions.
Convolutional Embedded Networks for Population Scale Clustering and Bio-ancestry Inferencing
The study of genetic variants can help find correlating population groups to identify cohorts that are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs.
