High-dimensional molecular measurements are transforming the field of pathology into a data-driven discipline. While hematoxylin and eosin (H&E) stainings are still the gold standard to diagnose diseases, the integration of microscopic and molecular information is becoming crucial to advance our understanding of tissue heterogeneity. To this end, we propose a data fusion method that integrates spatial omics and microscopic data obtained from the same tissue slide. Through correspondence-aware manifold learning, we can visualize the biological trends observed in the high-dimensional omics data at microscopic resolution. While data fusion enables the detection of elements that would not be detected taking into account the separate data modalities individually, out-of-sample prediction makes it possible to predict molecular trends outside of the measured tissue area. The proposed dimensionality reduction-based data fusion paradigm will therefore be helpful in deciphering molecular heterogeneity by bringing molecular measurements such as mass spectrometry imaging (MSI) to the cellular resolution.

High-dimensional molecular measurements are transforming the field of pathology into a data-driven discipline. While H&E stainings are still the gold standard to diagnose disease, the integration of microscopic and molecular information is becoming crucial to advance our understanding of tissue heterogeneity. To this end, we propose a data fusion method that integrates spatial omics and microscopic data obtained from the same tissue slide. Through correspondence-aware manifold learning, we can visualise the biological trends observed in the high-dimensional omics data at microscopic resolution. While data fusion enables the detection of elements that would not be detected taking into account the separate data modalities individually, out-of-sample prediction makes it possible to predict molecular trends outside of the measured tissue area. The proposed dimensionality reduction-based data fusion paradigm will therefore be helpful in deciphering molecular heterogeneity by bringing molecular measurements such as MSI to the cellular resolution.

This paper explores how Dutch diary fragments, written by family coaches in the social sector, can be analysed automatically using machine learning techniques to quantitatively measure the impact of social coaching. The focus lays on two tasks: determining which sentiment a fragment contains (sentiment analysis) and investigating which fundamental social rights (education, employment, legal aid, etc.) are addressed in the fragment. To train and test the new algorithms, a dataset consisting of 1715 Dutch diary fragments is used. These fragments are manually labelled on sentiment and on the applicable fundamental social rights. The sentiment analysis models were trained to classify the fragments into three classes: negative, neutral or positive. Fine-tuning the Dutch pre-trained Bidirectional Encoder Representations from Transformers (BERTje) (de Vries et al., 2019) language model surpassed the more classic algorithms by correctly classifying 79.6% of the fragments on the sentiment analysis, which is considered as a good result. This technique also achieved the best results in the identification of the fundamental rights, where for every fragment the three most likely fundamental rights were given as output. In this way, 93% of the present fundamental rights were correctly recognised. To our knowledge, we are the first to try to extract social rights from written text with the help of Natural Language Processing techniques.