The ability to accurately model and redesign ligand binding sites (pockets) on protein surfaces holds tremendous value across fields like drug discovery, enzyme engineering,...
OncoGPT is a customized language model created by Shenzhen Kanghua Juntai Biotech Co. Ltd. researchers in China that is intended to deliver precise medical...
Dynamic structural ensembles play a major role in the biological functions of proteins. For the purpose of learning and sampling the conformational landscapes of...
Reduced representation methylation sequencing for CpG islands or imprinted areas is made possible by Oxford Nanopore sequencing, which offers a distinct advantage over other...
Scientists from the University of Illinois, in collaboration with experts in the field, have unveiled a cutting-edge system named the "Team of AI-made Scientists."...
BioChatter, a new open-source framework developed by Heidelberg University researchers and collaborators, aims to harness the power of large language models (LLMs) for biomedical...
Deep learning-based virtual screening provides a more effective way to find molecules that resemble drugs, and virtual sources give chemists useful information. Leiden University...
Omics datasets generated by high throughput sequencing contain vast amounts of nucleotide data that must be analyzed to extract meaningful biological insights. A common...
Adverse drug-drug interactions (DDIs) pose a major health risk when using drug combinations to treat complex diseases. However, predicting harmful DDIs is extremely challenging...
Effective structure-based search tools are necessary to efficiently organize the massive volume of data in the database. Novel tools like FoldSeek and 3D-surfer have...
The digitization of health records has enabled powerful AI techniques to extract key medical insights from the vast trove of unstructured clinical notes. However,...
Compound-protein interaction (CPI) prediction accuracy is crucial for drug discovery. Creating deep learning models that can be applied broadly requires expanding the CPI data...
OpenFold, an artificial intelligence (AI) research consortium, has announced the release of two new tools that aim to improve protein structure prediction: SoloSeq and...
Cancer is a complex disease. It is brought on by the accumulation of several gene alterations. Understanding the patterns of these alterations, or mutational...
Gene Ontology is an axiomatic theory that describes the molecular roles, biological processes, and cellular placements of proteins using more than 100,000 axioms. Learning...
For scientists studying the functions of the genome, DNA embedding is a vital tool. Effective investigations like species categorization and metagenomics binning are made...
When analyzing omics data, regression analysis is an essential tool for identifying biomarkers. For the analysis of graph-structured data, graph neural networks (GNNs) are...
Researchers from the University of Michigan have reported the discovery of a new plant protein fold that catalyzes the formation of macrocyclic peptides through...
Proteins perform essential functions in living organisms and adopt specific three-dimensional structures dictated by their amino acid sequences. The ability to accurately design novel...
One must understand proteins' structural flexibility in-depth to comprehend their role in biological processes and functional systems. It is still challenging to predict various...
Patients with recently diagnosed non-differentiated multiple myeloma (NDMM) have a wide range of outcomes, with overall survival (OS) spanning from several months to more...
A team of researchers from the University of Tokyo developed GenerRNA, the first large-scale pre-trained AI model for automated RNA design that does not...
Recent advances in large language models (LLMs) and transformers have opened new possibilities for modeling protein sequences as language. Carnegie Mellon University researchers have...
The soldiers of our immune system, antibodies, bind to specific targets like viruses and toxins and neutralize them. Predicting antibody-antigen complex structures accurately has...
This article delves into a recent study undertaken by a team of researchers that examined multiple computer algorithms for transcriptional regulator prediction using Next-Generation...
Before understanding RNA biology, one must grasp the three-dimensional structure of RNA. Because experimental procedures require a lot of labor and money, computer approaches...
Recent breakthroughs in AI-based protein structure prediction have enormous potential to accelerate discoveries across the life sciences. However, major barriers persist in making these...
MarkerGeneBERT, a natural language processing (NLP) system to automatically extract cell type markers from single-cell sequencing literature by parsing full text, was recently introduced...
Deep learning models like RosettaFold and AlphaFold2 can predict protein structure with high accuracy; however, they still have difficulties when it comes to intricate...
Scientists from the University of Southern Denmark, NaturalAntibody and Alector Therapeutics unveil nanoBERT, a nanobody-specific transformer designed to predict amino acids in specific positions...
In a major scientific breakthrough, researchers at the University of Chicago have succeeded in creating a comprehensive model of the nuclear pore complex with...
Understanding biological processes and offering organized representations of intricate relationships are made possible by Knowledge Graphs, or KGs. However, the resources available today to...
Protein structure prediction and design may be accomplished using protein language models (pLMs). They may not completely comprehend the biophysics of protein structures, though....
Harvard University researchers introduce RNA3DB, a dataset crafted from the Protein Data Bank (PDB), addressing challenges in RNA structure prediction. In response to limitations...
Predicting protein structures has been transformed by artificial intelligence. However, rather than competence, accessibility, and ease of use are increasingly becoming limiting issues for...
Transcriptional enhancers control the spatiotemporal activation of the target genes they regulate by serving as docking stations for various transcription factor combinations. It has...
Transposable elements (TEs) supply genomes with both coding and non-coding sequences and have significant roles in evolution. However, the extent of TE annotations in...
Advances in high-throughput omics profiling have improved cancer patient classification significantly. However, insufficient data in multi-omics integration is a big problem because conventional techniques...
Molecular and phenotypic responses to numerous perturbations are revealed by the increasing number of single-cell perturbation studies. However, variations in format, naming conventions, and...
The laborious aspects of acquiring and curating data sets for machine learning, especially in proteomics-based systems, present unique difficulties because there is a significant...
In a landmark initiative, the Human Immunome Project (HIP) commenced at a summit in La Jolla, California, bringing immunology specialists together to address the...
The field of biological sequence analysis has lately benefited from the revolutionary changes brought about by the development of self-supervised deep language models for...
IntelliGenes is a new machine learning pipeline developed by researchers at The State University of New Jersey that integrates multi-genomics, clinical, and demographic data...
The drug development process involves a winding trail across the enormous chemical wilderness, looking for compounds that interact ideally with their target proteins. Traditional...
Protein design has seen significant progress, but a comprehensive deep-learning framework for protein design, including de novo binder design and higher-order symmetric architectures, remains...
Predicting the immunogenicity of peptide antigens attached to major histocompatibility complex (MHC) molecules is critical for developing new immunotherapies and better understanding human immune...
Although the field of protein engineering offers countless applications in chemistry, energy, and medicine, the process of designing new proteins with enhanced or unique...
Drug discovery is one of the many fields where recent developments in conversational large language models (LLMs), like ChatGPT, have shown incredible promise. However,...
Single-cell transcriptomics has been generating large-scale information, improving our knowledge of cellular processes across different tissues, and facilitating medication discovery, diagnosis, and prognosis. But...
Conventional representational models of protein functions have demonstrated notable performance in downstream tasks; nevertheless, they frequently do not incorporate explicit information on protein structure,...
Are you aware of the perception of a genomic language model? If not, then it's worth mentioning that researchers at Harvard, Cornell University, and...
Imagine a teeming city where proteins are the movers and shakers. Each protein intertwines with others in partnerships that are vital to every cellular...
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