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Found 14 papers, 8 papers with code
Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification
Considering the relevance score, we then generate explanations by visualizing relevant words for the predicted patent class.
From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer
Domain experts often rely on most recent knowledge for apprehending and disseminating specific biological processes that help them design strategies for developing prevention and therapeutic decision-making in various disease scenarios.
Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Laundering
From a given graph of money transfers between accounts of a bank, existing approaches attempted to detect money laundering.
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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Question Answering Over Biological Knowledge Graph via Amazon Alexa
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.
Interpreting Black-box Machine Learning Models for High Dimensional Datasets
Many real-life datasets, however, are of increasingly high dimensionality, where a large number of features may be irrelevant for both supervised and unsupervised learning tasks.
Multimodal Hate Speech Detection from Bengali Memes and Texts
This paper is about hate speech detection from multimodal Bengali memes and texts.
DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali Language
The exponential growths of social media and micro-blogging sites not only provide platforms for empowering freedom of expressions and individual voices, but also enables people to express anti-social behaviour like online harassment, cyberbullying, and hate speech.
Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network
Evaluations against several baseline embedding models, e. g., Word2Vec and GloVe yield up to 92. 30%, 82. 25%, and 90. 45% F1-scores in case of document classification, sentiment analysis, and hate speech detection, respectively during 5-fold cross-validation tests.
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.
