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"Learning Graph Convolutional Networks for Multi-Label Recognition and " by Zhaomin Chen, Xiu Shen Wei et al.

The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model label dependencies to improve recognition performance. To capture and explore such important information, we propose Graph Convolutional Networks based models for multi-label recognition, where directed graphs are constructed over classes and information is propagated between classes to learn inter-dependent class-level representations. Following this idea, we design two particular models that approach multi-label classification from different views. In our first model, the prior knowledge about the class dependencies is integrated into classifier learning. Specifically, we propose Classifier-Learning-GCN to map class-level semantic representations (\eg, word embedding) into classifiers that maintain the inter-class topology. In our second model, we decompose the visual representation of an image into a set of label- ....

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Scientists Identify 165 New Cancer Genes Using Machine Learning

Cipla Doubles Remdesivir Production In India For Unprecedented Demand
The ability of artificial intelligence (AI) and machine learning (ML) in furthering medical research has become indisputable. Adding credence to their application, a new ML algorithm has helped researchers identify over 160 new genes that are responsible for the development of numerous cancers.
Developed by scientists from the Max Planck Institute for Molecular Genetics (MPIMG) and Institute of Computational Biology (ICB), the algorithm helped identify 165 unknown cancer genes. The new genes are said to closely interact with previously known cancer-causing genes and found to be vital in the survival of cancer cells in cell culture experiments. ....

Annalisa Marsico , Roman Schulte Sasse , Institute Of Computational Biology , Max Planck Institute For Molecular Genetics , Max Planck Institute , Molecular Genetics , Computational Biology , New Cancer Genes , Representational Picture , Explainable Multi Omics Graph Integration , Cancer Treatment , Machine Learning , Artificial Intelligence , 165 New Cancer Genes Discovered , New Cancer Genes Identified , Ml Identifies New Cancer Genes , Ai Cancer , Machine Learning New Cancer , Machine Learning Cancer Genes , New Cancer Genes Machine Learning , Artificial Intelligence Medicine , Graph Convolutional Networks , Emogi New Cancer Genes , நிறுவனம் ஆஃப் கணக்கீட்டு உயிரியல் , மூலக்கூறு ஜெநெடிக்ஸ் , கணக்கீட்டு உயிரியல் ,

More than the sum of mutations

165 new cancer genes identified with the help of machine learning. ....

Stefan Budach , Denes Hnisz , Annalisa Marsico , Roman Schulte Sasse , Institute Of Computational Biology Helmholtz Zentrum , Max Planck Institute For Molecular Genetics , Max Planck Institute , Molecular Genetics , Computational Biology , Explainable Multi Omics Graph Integration , Nature Machine Intelligence , Artificial Intelligence , Multi Omics Data , Graph Convolutional Networks , Identify New Cancer Genes , Associated Molecular , Medicine Health , மூலக்கூறு ஜெநெடிக்ஸ் , கணக்கீட்டு உயிரியல் , இயற்கை இயந்திரம் உளவுத்துறை , செயற்கை உளவுத்துறை , தொடர்புடையது மூலக்கூறு , மருந்து ஆரோக்கியம் ,