Mutate and observe: utilizing deep neural networks to investigate the impact of mutations on translation initiation.

Motivation: The primary regulatory step for protein synthesis is translation initiation, which makes it one of the fundamental steps in the central dogma of molecular biology. In recent years, a number of approaches relying on deep neural networks (DNNs) have demonstrated superb results for predicting translation initiation sites. These state-of-the art results indicate that DNNs are indeed capable of learning complex features that are relevant to the process of translation.

Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit.

While transcriptome- and proteome-wide technologies to assess processes in protein biogenesis are now widely available, we still lack global approaches to assay post-ribosomal biogenesis events, in particular those occurring in the eukaryotic secretory system. We here develop a method, SECRiFY, to simultaneously assess the secretability of >10 protein fragments by two yeast species, S. cerevisiae and P.

ToxDL: deep learning using primary structure and domain embeddings for assessing protein toxicity.

Motivation: Genetically engineering food crops involves introducing proteins from other species into crop plant species or modifying already existing proteins with gene editing techniques. In addition, newly synthesized proteins can be used as therapeutic protein drugs against diseases. For both research and safety regulation purposes, being able to assess the potential toxicity of newly introduced/synthesized proteins is of high importance.

Electrochromic Photodetectors: Toward Smarter Glasses and Nano Reflective Displays via an Electrolytic Mechanism.

Electrochromic devices, serving as smart glasses, have not yet been intelligent enough to regulate lighting conditions independent of external photosensing devices. On the other hand, their bulky sandwich structures have been suffering setbacks utilized for reflective displays in an effort to compete with mature emissive displays. The key to resolve both problems lies in incorporating the photosensing function into electrochromic devices while simplifying their configuration via replacing ionic electrolytes.

SpliceRover: interpretable convolutional neural networks for improved splice site prediction.

Motivation: During the last decade, improvements in high-throughput sequencing have generated a wealth of genomic data. Functionally interpreting these sequences and finding the biological signals that are hallmarks of gene function and regulation is currently mostly done using automated genome annotation platforms, which mainly rely on integrated machine learning frameworks to identify different functional sites of interest, including splice sites. Splicing is an essential step in the gene regulation process, and the correct identification of splice sites is a major cornerstone in a genome annotation system.