Related papers: PhaGO: Protein function annotation for bacteriopha…
Across all kingdoms of biological life, protein-coding genes exhibit unequal usage of synonmous codons. Although alternative theories abound, translational selection has been accepted as an important mechanism that shapes the patterns of…
We introduce a pioneering methodology for boosting large language models in the domain of protein representation learning. Our primary contribution lies in the refinement process for correlating the over-reliance on co-evolution knowledge,…
Accurately annotating and controlling protein function from sequence data remains a major challenge, particularly within homologous families where annotated sequences are scarce and structural variation is minimal. We present a two-stage…
Recent advances in genomic sequencing technology have resulted in an abundance of genome sequence data. Despite the progress in interpreting those data, there remains a broad scope for their translation into clinical and societal benefits.…
The marine environment is one of the most important sources for microbial biodiversity on the planet. These microbes are drivers for many biogeochemical processes, and their enormous genetic potential is still not fully explored or…
Cells of diatoms and related algae with complex plastids of red algal origin are highly compartmentalized. These plastids are surrounded by four envelope membranes, which also define the periplastidic compartment (PPC), the space between…
Deep learning models have driven significant progress in predicting protein function and interactions at the protein level. While these advancements have been invaluable for many biological applications such as enzyme engineering and…
Proteomics will celebrate its 20th year in 2014. In this relatively short period of time, it has invaded most areas of biology and its use will probably continue to spread in the future. These two decades have seen a considerable increase…
Proteins play a vital role in biological processes and are indispensable for living organisms. Accurate representation of proteins is crucial, especially in drug development. Recently, there has been a notable increase in interest in…
Machine- and deep-learning approaches for biological sequences depend critically on transforming raw DNA, RNA, and protein FASTA files into informative numerical representations. However, this process is often fragmented across multiple…
Recent advances in protein function prediction exploit graph-based deep learning approaches to correlate the structural and topological features of proteins with their molecular functions. However, proteins in vivo are not static but…
We introduce a new model of proteins, which extends and enhances the traditional graphical representation by associating a combinatorial object called a fatgraph to any protein based upon its intrinsic geometry. Fatgraphs can easily be…
Proteins are the workhorses of life and gaining insight on their functions is of paramount importance for applications such as drug design. However, the experimental validation of functions of proteins is highly-resource consuming.…
Feature selection is a combinatorial optimization problem that is NP-hard. Conventional approaches often employ heuristic or greedy strategies, which are prone to premature convergence and may fail to capture subtle yet informative…
The remarkable growth and significant success of machine learning have expanded its applications into programming languages and program analysis. However, a key challenge in adopting the latest machine learning methods is the representation…
Pangenome variation graphs (PVGs) allow for the representation of genetic diversity in a more nuanced way than traditional reference-based approaches. Here we focus on how PVGs are a powerful tool for studying genetic variation in viruses,…
Protein quantification and analysis are well-accepted approaches for biomarker discovery but are limited to identification without structural information. High-throughput omics data (i.e., genomics, transcriptomics, and proteomics) have…
The ability to identify time periods when individuals are most susceptible to exposures, as well as the biological mechanisms through which these exposures act, is of great public health interest. Growing evidence supports an association…
Phosphorylation site prediction based on kinase-substrate interaction plays a vital role in understanding cellular signaling pathways and disease mechanisms. Computational methods for this task can be categorized into kinase-family-focused…
We introduce a unified Python package for the prediction of protein biophysical properties, streamlining previous tools developed by the Bio2Byte research group. This suite facilitates comprehensive assessments of protein characteristics,…