Is it a good idea to combine fine tuning and feature extraction techniques?
The concept of multimodal learning is relevant: in this case, combining data from two modalities: 1) image signal using ResNet50 and 2) genomic features extracted from genes. Multimodal learning for extending state-of-the-art performance of pre-trained unimodal models is currently an area of active research in the literature. In the NLP domain, the paper on Multimodal Bert describes combining a pre-trained language model with additional signals from visual and auditory inputs for improved classification performance.
In the specific scenario mentioned here, combining the image and non-image signals into a single deep neural network graph would allow for additional fine-tuning. It is possible to first train the layer combining signals from both modalities, then later fine-tune all weights in the graph (with a lower learning rate) for improved performance. This procedure discussed a bit in the Keras Guide to Transfer Learning.