Meet DRfold: A Novel Method to Predict RNA Tertiary Structures with...
RNAs are essential components of physiological activities, however, accurately modeling their structures has long posed a challenge due to their complex structures. The National...
Meet AutoBA: A New Tool Making Bioinformatics Analysis Easy
Recent advancements in technology have now made it feasible for scientific researchers to be able to collect large amounts of biological data using a...
CodonBERT: A New Large Language Model Could Help Design Optimized mRNA...
A team of researchers at Digital R&D, Sanofi, USA, developed CodonBERT, a transformer-based large language model that is used for performing tasks related to...
Large Language Models Meet Single Cell Transcriptomics: Unlocking Biological Insights with...
Large language models, such as GPT (Generative Pre-trained Transformer), have showcased remarkable proficiency in various natural language processing tasks. Their ability to understand and...
Machine Learning Predicts Mood in Chronic Stroke Patients and Identifies the...
Stanford's scientists used Machine Learning to elucidate the relationship between stroke and depression by identifying a biomarker in stroke survivors. The findings may provide...
Virtual Drug Screening with Less Data: Boosting Predictions with Transfer Learning...
A team of researchers at the Eric and Wendy Schmidt Centre at the Broad Institute of MIT and Harvard developed a model that uses...
3Dmapper: A New Tool for Mapping Genetic Variants to Protein Structures...
Centuries after the first proteins were identified and characterized, their complexity proves a challenge for scientists all over the world. Their creation, evolution, and...
PeptideBERT: A Transformer-based Language Model for Predicting Peptide Properties using Amino...
Scientists at Carnegie Mellon University, USA, introduced PeptideBERT, a language model that is capable of predicting the properties of proteins and peptides solely based...
Deep Learning Powers New Methods for Protein Remote Homology Detection and...
Comprehending sequence–structure–function relationships is challenging for proteins with low similarity to existing proteins. New and improved alignment approaches are required for such proteins to...
Meet BARNACLE: A New Tool to Predict RNA Contacts using Data...
In the pursuit of understanding how RNA structure and function are related, predicting RNA structures becomes a tool alongside experimental approaches. However, the limited...
Meet HighFold: An AlphaFold-based Algorithm to Predict Cyclic Peptide Monomers and...
Cyclic peptides represent an emerging class of drugs that combine the advantageous attributes of small molecules with those of antibodies or protein-based therapeutics. Scientists...
Meet XVir: A Promising Transformer-based Approach to Spot Viral Reads in...
A team of researchers at The University of Texas at Austin developed a novel algorithm, XVir, that combines deep learning methods and transformers to...
Large Language Models Offering Solutions to Tackle Biological Complexity
Large Language Models (LLMs) have emerged as dynamic tools with the ability to transform the landscape of biological research in a world driven by...
Meet Flow: An Ultimate Bioinformatics Data Analysis and Management Solution
Scientists at King's College London, UK, and collaborators have introduced 'Flow,' an open-access web platform to perform bioinformatics analysis. Flow provides the capability to...
ChatGPT Empowers Clinical Decision Making with Impressive Accuracy
The large-language model (LLM)-based model, ChatGPT, had its clinical decision-making abilities put to the test in a joint study by researchers from Harvard Medical...
Revolutionizing Parkinson’s Care: Harnessing Artificial Intelligence to Detect Disease Severity at...
A team of researchers has recently developed an Artificial Intelligence (AI) based model for detecting the severity of the disease's progression and its symptoms...
Oxford Researchers Develop a Machine Learning Model to Predict 10-Year Breast...
A group of researchers affiliated with Oxford University, UK, have developed a prognostic model backed by the most extensive information set spanning a national,...
Meet FateMap: A Barcoding Approach to Decode Why Certain Cells Resist...
A collaborative research effort led by scientists and engineers at the University of Pennsylvania and Northwestern University led to the development of FateMap, a...
Decoding Thoughts into Words: A Breakthrough Brain-Computer Interface Lets Paralyzed Woman...
In a groundbreaking advancement, researchers from Stanford University have developed a speech brain-computer interface (BCI) that holds significant promise for individuals with paralysis. By...
From Algorithms to Antibiotics: Harnessing Artificial Intelligence in the Search for...
Faculties of the Mathematics Department at the University of Warsaw, Poland, have presented a review of the past two years progress of Artificial Intelligence...
Scientists Assemble the First Complete Human Y Chromosome Sequence: A Milestone...
The sequencing and assembly of the human Y chromosome have been challenging due to its intricate repeat structure, which encompasses lengthy palindromes, tandem repeats,...
Unveiling Cellular Secrets: Visualizing Dynamic Biomolecular Machinery with CryoDRGN-ET
The advancement of cryo-electron tomography (cryo-ET) has opened novel avenues for visualizing the intricate structures of dynamic macromolecular assemblies within their natural cellular surroundings....
AI’s Quantum Leap into Bioengineering: Unleashing Language Models in Atom-by-Atom Protein...
The University of Toronto researchers have explored the potential of Chemical Language Models (CLMs) to thrive as Biological Learning Models. In contrast to popular...
Stanford’s GEARS: An Innovative Approach to Predicting Transcriptional Outcomes of Multigene...
Understanding cellular responses to genetic changes holds vital importance in various biomedical contexts, such as uncovering cancer-related genetic interactions and advancing regenerative medicine. However,...
Meet DeepTracer-Refine: An Automated Refinement Approach for AlphaFold2 Predicted Protein Structures
Scientists of the DeepTracer project have formulated DeepTracer-refine, an automated pipeline that consolidates the advantages of sequence-to-model and map-to-model strategies, complements their shortcomings, and...
Meet OpenFold: Reimplementing AlphaFold2 to Illuminate Its Learning Mechanisms and Generalization
Columbia University and Harvard University researchers have developed OpenFold—a swift, memory-efficient, trainable AlphaFold2 implementation. They aimed to address the shortcomings of the revolutionary AlphaFold2,...
Tuft’s Researchers Unveil MELISSA: Revolutionizing Protein Function Prediction Through Semi-Supervised Embedding
Tufts University researchers have developed MELISSA, a groundbreaking approach for predicting protein functions through protein-protein association networks. Existing methods such as Mashup and deepNF...
Astrocyte-Powered Transformers: A Biological Blueprint for High-Performance Neural Architectures
Researchers affiliated with the MIT-IBM Watson AI Lab and Harvard Medical School have proposed a hypothesis of Transformer’s core computational schemes in order to...
CHOP Researchers Unveil TEQUILA-seq: A Versatile and Affordable Tool for Targeted Long-Read...
Researchers from the Children’s Hospital of Philadelphia, Philadelphia, USA, have created a versatile, easy-to-implement, and low-cost novel technique, TEQUILA-seq, for synthesizing large quantities of...
Functional Unknomics: Exploring the Untapped Potential of Unknown Genes Leveraging Unknome...
Unstudied areas of life sciences, including uncharacterized or less characterized genes and proteins, have now got an exclusively dedicated repository through the recent works...
Empowering Medical Research with Drugst.One: A Seamless Plug-and-Play Approach for Online...
Researchers from the University of Hamburg, Germany, in collaboration with researchers from Denmark, Canada, the USA, Spain, Slovakia, and Israel, have designed Drugst.One, a...
Answering Genomics Questions with Precision: GeneGPT Bridges the Gap Between LLMs...
Scientists from the National Library of Medicine (NLM), National Institutes of Health (NIH), and University of Maryland, College Park, US, have presented GeneGPT, a...
Artificial Intelligence: The New Weapon in the Fight Against Infectious Diseases
Infectious diseases have been a persistent challenge to public health despite significant advancements in various scientific disciplines. The emergence of viruses resistant to drugs,...
Advancing Spatial Transcriptomic Cell-Type Deconvolution with SONAR: A Spatially Weighted Poisson-Gamma...
Scientists from the Chinese Academy of Sciences, China, introduce SONAR, a novel model for cell-type deconvolution from spatial transcriptomics data. This method, termed Spatially...
Meet inClinico: Transforming Clinical Trial Outcome Prediction Leveraging Artificial Intelligence
Researchers from Insilico Medicine designed and devised inClinico to predict Phase-II clinical trial outcomes. The transformer-based artificial intelligence software platform, inClinico, conjugates an ensemble...
Exploring Deep Learning Methods for Binding Affinity Prediction Across Proteins and...
To filter promising drug candidates early in the drug discovery process, it is imperative to accurately predict how well a protein and a potential...
Ameliorating the Chromatin Landscape: ChromaFold’s Accurate 3D Contact Map Prediction from...
USA-based researchers have formulated a supervised deep learning model, ChromaFold, that has achieved state-of-the-art performance in cell-type-specific prediction of 3D contact maps and regulatory...
Brock Researchers Develop AI-Powered Tool ‘DAPTEV’ to Design New Aptamer Drugs...
Traditional drug discovery and development methods are quite expensive, cumbersome, and prone to the biases of experts. A revolution in the field of drug...
Large Language Models and Foundation Models in Healthcare: Unveiling the Hype...
Standford researchers have discoursed the critical aspect for the integration of Foundation Models (FM) in the Healthcare system, that is evaluation of the reliability...
UCSD’s uDance: A Scalable Approach for Generating and Updating Phylogenies with...
uDance, a novel method that builds highly accurate and scalable phylogenetic trees using the divide and conquer approach, has been devised jointly by scientists...
Drug-GPT™ vs. ChatGPT: Comparative Analysis of GPT Language Models in Healthcare...
Scientists from Talking Medicine present a comparative analysis of three leading Generative Pre-trained Transformer (GPT) solutions - Drug-GPT™ 3, Drug-GPT™ 4, and ChatGPT -...
Connecting the Cellular Dots with treeArches: How Single-Cell Reference Mapping Constructs...
To deeply understand how tissues work in healthy and diseased conditions, it is imperative to collect vast amounts of data on individual cells. Such...
Unlock Cellular Mysteries with IndepthPathway: An Innovative Tool for Single-cell Sequencing...
Researchers from the University of Pittsburgh, USA, have made a momentous breakthrough in the realm of single-cell sequencing with the creation of IndepthPathway, an innovative...
Beyond Biopsies: Single-Molecule Cell-Free DNA Profiling Fuels Non-Invasive Cancer Screening and...
A new ray of hope for non-invasive cancer detection has emerged. A machine learning algorithm called GEMINI, created by John Hopkins University and Boston...
Decoding the Microbial Orchestra: A Breakthrough in Metaproteomic Data Analysis and...
In groundbreaking research, scientists from Brigham Young University, United States, have developed a cutting-edge deep learning computer model, Kaiko, with the unprecedented capability to...
Machine Learning Magic: DNABERT-2 Empowers Enhanced Multi-Species Genomic Analysis
The key to unlocking genomic secrets is here. Researchers from Northwestern University, in collaboration with Stony Brook University, have developed DNABERT-2, which is a...
Machine Learning Meets Virology: Scripps Scientists Unveil Tracking and Early Warning...
Imagine possessing the power to foresee the appearance of hazardous viral strains well in advance of their impact. The scientists at Scripps Research...
Predicting Binding Modes with Precision: CHARMM-GUI Empowers Reliable Ligand Binding Predictions
Researchers from Leigh University, United States, have developed a novel technique referred to as CHARMM-GUI protein-ligand docking (CGUI-IFD) to overcome the drawbacks associated with...
Automating CHARMM to GROMACS Force Field Porting with charmm2gmx: A Seamless...
Molecular mechanics (MM) calculations and molecular dynamics (MD) simulations are powerful tools used in biomolecular research, relying on accurate force fields (FFs) for reliable...
Meet TALKIEN: A New Tool to Decipher Molecular Crosstalk Through Ligand-Receptor...
Researchers from Bellvitge Biomedical Research Institute in Spain have designed TALKIEN (crossTALK IntEraction Network), a user-friendly online tool that helps to visualize molecular crosstalk...
Must Read
How Does NeuralPLexer3 Combine Physics and Machine Learning for Superior Biomolecular Predictions?
The quest to understand biomolecular interactions is central to advancing drug discovery, molecular biology, and bioengineering. A significant step in this journey is accurately...
FedPyDESeq2: Advancing Differential Expression Analysis with Federated Learning for Bulk RNA-Seq
In genomics, the conventional approach for explaining any gene expression is to cross-analyze bulk RNA sequencing (RNA-seq) data. However, as researchers, more often than...
Decoding Protein Roles with ProCyon: A Unified Framework for Multiscale Phenotypes
About 20% of human proteins lack recognized functionalities, and over 40% lack context-specific functionals, underscoring the difficulties of comprehending these proteins and their varied...
Streamlining Copy Number Variation Discovery: How CNV-Finder is Changing Genomics
A prevailing challenge in genomics still revolves around the comprehension of structural variants, one of which is copy number variation (CNV). To undertake the...
Unlocking Protein Design with PLAID: A Sequence-Centric Generative Model for All-Atom Structures
The potential influence of generative models for protein design is drawing the attention of the scientific world. However, there are numerous modalities that mediate...