Existing annotation paradigms rely on controlled vocabularies, where each data instance is classified into one term from a predefined set of controlled vocabularies. This paradigm restricts the analysis to concepts that are known and well-characterized. Here, we present the novel multilingual translation method BioTranslator to address this problem. BioTranslator takes a user-written textual description of a new concept and then translates this description to a non-text biological data instance. The key idea of BioTranslator is to develop a multilingual translation framework, where multiple modalities of biological data are all translated to text. We demonstrate how BioTranslator enables the identification of novel cell types using only a textual description and how BioTranslator can be further generalized to protein function prediction and drug target identification. Our tool frees scientists from limiting their analyses within predefined controlled vocabularies, enabling them to interact with biological data using free text.

Understanding the temporal dynamics of gene expression is crucial for developmental biology, tumor biology, and biogerontology. However, some timepoints remain challenging to measure in the lab, particularly during very early or very late stages of a biological process. Here we propose Sagittarius, a transformer-based model that can accurately simulate gene expression profiles at timepoints outside of the range of times measured in the lab. The key idea behind Sagittarius is to learn a shared reference space for time series measurements, thereby explicitly modeling unaligned timepoints and conditional batch effects between time series, and making the model widely applicable to diverse biological settings. We show Sagittarius's promising performance when extrapolating mammalian developmental gene expression, simulating druginduced expression at unmeasured dose and treatment times, and augmenting datasets to accurately predict drug sensitivity. We also used Sagittarius to extrapolate mutation profiles for early-stage cancer patients, which enabled us to discover a gene set connected to the Hedgehog signaling pathway that may be related to tumorigenesis in sarcoma patients, including PTCH1, ARID2, and MYCBP2. By augmenting experimental temporal datasets with crucial but difficultto-measure extrapolated datapoints, Sagittarius enables deeper insights into the temporal dynamics of heterogeneous transcriptomic processes and can be broadly applied to biological time series extrapolation..

Motivation The exponential growth of genomic sequencing data has created ever-expanding repositories of gene networks. Unsupervised network integration methods are critical to learn informative representations for each gene, which are later used as features for downstream applications. However, these network integration methods must be scalable to account for the increasing number of networks and robust to an uneven distribution of network types within hundreds of gene networks. Results To address these needs, we present Gemini, a novel network integration method that uses memory-efficient high-order pooling to represent and weight each network according to its uniqueness. Gemini then mitigates the uneven network distribution through mixing up existing networks to create many new networks. We find that Gemini leads to more than a 10% improvement in F1 score, 15% improvement in micro-AUPRC, and 63% improvement in macro-AUPRC for human protein function prediction by integrating hundreds of networks from BioGRID, and that Gemini’s performance significantly improves when more networks are added to the input network collection, while Mashup and BIONIC embeddings’ performance deteriorates. Gemini thereby enables memory-efficient and informative network integration for large gene networks and can be used to massively integrate and analyze networks in other domains. Availability and implementation Gemini can be accessed at: https://github.com/MinxZ/Gemini.