TransFun: A Tool of Integrating Large Language Models, Transformers, and Equivariant Graph Neural Networks to Predict Protein Function.

Experimentally determining the functions of proteins is a complex and time-consuming process. This challenge contributes to a gap, where many proteins have known sequences, predicted structures, and other crucial information, yet lack functional annotations. This gap underscores the critical importance of automated function prediction (AFP) methods, which aim to develop computational techniques dedicated to predicting protein functions.

Global modulation of gene expression and transcriptome size in aneuploid combinations of maize.

Genomic imbalance refers to the more severe phenotypic consequences of changing a single chromosome compared to changing the whole genomic set. Previous genomic imbalance studies in maize have identified gene expression modulation in aneuploids of single chromosome arms. Here, the modulation of gene expression in more complex aneuploids, e.

Multi-omics analyses and machine learning prediction of oviductal responses in the presence of gametes and embryos.

The oviduct is the site of fertilization and preimplantation embryo development in mammals. Evidence suggests that gametes alter oviductal gene expression. To delineate the adaptive interactions between the oviduct and gamete/embryo, we performed a multi-omics characterization of oviductal tissues utilizing bulk RNA-sequencing (RNA-seq), single-cell RNA-sequencing (scRNA-seq), and proteomics collected from distal and proximal at various stages after mating in mice.

Improving protein function prediction by learning and integrating representations of protein sequences and function labels.

Motivation: As fewer than 1% of proteins have protein function information determined experimentally, computationally predicting the function of proteins is critical for obtaining functional information for most proteins and has been a major challenge in protein bioinformatics. Despite the significant progress made in protein function prediction by the community in the last decade, the general accuracy of protein function prediction is still not high, particularly for rare function terms associated with few proteins in the protein function annotation database such as the UniProt.

Results: We introduce TransFew, a new transformer model, to learn the representations of both protein sequences and function labels [Gene Ontology (GO) terms] to predict the function of proteins.

Deep learning methods for protein function prediction.

Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics. Numerous computational methods had been developed to advance protein function prediction gradually in the last two decades. Particularly, in the recent years, leveraging the revolutionary advances in artificial intelligence (AI), more and more deep learning methods have been developed to improve protein function prediction at a faster pace.

Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function.

Motivation: Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap.

Dissection of a Down syndrome-associated trisomy to separate the gene dosage-dependent and -independent effects of an extra chromosome.

As an aneuploidy, trisomy is associated with mammalian embryonic and postnatal abnormalities. Understanding the underlying mechanisms involved in mutant phenotypes is broadly important and may lead to new strategies to treat clinical manifestations in individuals with trisomies, such as trisomy 21 [Down syndrome (DS)]. Although increased gene dosage effects because of a trisomy may account for the mutant phenotypes, there is also the possibility that phenotypic consequences of a trisomy can arise because of the presence of a freely segregating extra chromosome with its own centromere, i.

Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function.

Motivation: Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap.

Deep Learning Prediction of Severe Health Risks for Pediatric COVID-19 Patients with a Large Feature Set in 2021 BARDA Data Challenge.

Most children infected with COVID-19 have no or mild symptoms and can recover automatically by themselves, but some pediatric COVID-19 patients need to be hospitalized or even to receive intensive medical care (e.g., invasive mechanical ventilation or cardiovascular support) to recover from the illnesses.

Allele-specific aberration of imprinted domain chromosome architecture associates with large offspring syndrome.

Large offspring syndrome (LOS) and Beckwith-Wiedemann syndrome are similar epigenetic congenital overgrowth conditions in ruminants and humans, respectively. We have reported global loss-of-imprinting, methylome epimutations, and gene misregulation in LOS. However, less than 4% of gene misregulation can be explained with short range (<20kb) alterations in DNA methylation.