Matching RNA Sequencing Alignment Data to Genotypes Made Easy with RNA...
Researchers from the Jackson Laboratory for Mammalian Genetics, USA, have built RNA Strain-Match, a quality control tool helpful in matching RNA data to their...
Exploring Uncharted Territory of Protein Folds Through De Novo Protein Design
Japanese scientists from the National Institute of Natural Sciences (NINS) and Osaka University have discovered novel protein folds. They created a well-defined set of...
ChatGPT meets DNA: DNAGPT, The Ultimate Tool for Multitasking DNA Sequence...
Motivated by the success of the GPT (Generative Pre-trained Transformer) model, researchers from the Southern University of Science and Technology, Tencent AI Lab, Shenzhen,...
Microsoft’s AI2BMD: Transforming Protein Dynamics Simulation with ab initio Accuracy
Microsoft Researchers from Beijing, China, have introduced an AI-Based ab initio Biomolecular Dynamics System (AI2BMD) for large biomolecule simulation by strategizing protein fragmentation scheme...
Treating Rare Cancers with CancerGPT: Cracking the Code on Drug Pair...
CancerGPT, an advanced machine learning model introduced by scientists from the University of Texas and the University of Massachusetts, USA, harnesses the power of...
The Shape Shifters of DNA: Exploring Dynamic Conformational Switching in TFIIH...
Scientists from Georgia State University, The University of Texas, and Lawrence Berkeley National Laboratory, USA, built models of transcription factor IIH (TFIIH) using cryo-electron...
Meet Uni-RNA: Universal Pre-trained Models Transforming the RNA Research Landscape
Uni-RNA, a context-aware deep learning model developed by DP Technology, Beijing, China researchers, delivers state-of-the-art performance in unwrapping structural, functional, and evolutionary information intercalated...
Meet Mapinsights: A New Toolkit for Impeccable Quality Control of High-Throughput...
High-Throughput Sequencing (HTS) has drastically transformed the world of genomics by allowing rapid and accurate detection of genomic variations at the level of individual...
Predicting Immunotherapy Response: Construction and Validation of a Machine Learning-Based Gene...
A recent publication in Nature's Communications Medicine has presented the TNBC (Triple Negative Breast Cancer) ICI (Immune Checkpoint Inhibitors) response predictive classifier (TNBC-ICI) integrating...
HyenaDNA Powers Long-range Genomic Sequence Modeling at Single Nucleotide Resolution
Scientists from Stanford and Harvard Universities have developed a groundbreaking genomic foundation model called HyenaDNA, which promises to transform the analysis of DNA sequences....
MIT’s Data-Efficient Molecular Property Predictor: Leveraging Hierarchical Grammar-Induced Geometry
Researchers at the MIT-IBM Watson AI Lab have achieved a significant breakthrough in the field of molecular property prediction. By introducing the Grammar-Induced Geometry...
Decoding the Epigenomic Puzzle: Evaluating Computational Pipelines for Single-cell Histone Modification...
Single-cell histone post-translational modification (scHPTM) assays, such as scCUT&Tag or scChIP-seq, have emerged as powerful tools for mapping epigenomic landscapes within complex tissues. These...
Meet ColabDock: A New Framework Integrating AlphaFold and Experimental Restraints for...
Scientists from Peking University, China, presented a new protein-protein docking framework, ColabDock, that incorporates experimental restraints into protein complex structure modeling with an aim...
AI Transforms CRISPR Technology: Accurately Predicting On- and Off-Target Activity of...
Artificial intelligence (AI) and CRISPR gene editing technological advancements have transformed the study of genetics. While CRISPR enables scientists to precisely change genes, AI...
Multiscale Pangenome Analysis: A New Tool “PGR-TK” Improves Representation of Repetitive...
An article published recently in Nature Journal introduced the PanGenome Research Tool Kit (PGR-TK) with the purpose of facilitating analysis of structural and haplotype...
GPT-powered Drug Discovery: Meet DrugGPT, the Game-Changing “ChatGPT” for Ligand Design
ChatGPT has become a rage all over the globe ever since its launch. From brainstorming business ideas to explaining complex code in the most clear-cut...
A Synergistic Approach: AlphaFold and Symmetrical Docking for Precise Protein Assembly...
Mads Jeppesen and Ingemar André from Lund University, Sweden, have demonstrated a protein complex assembly structure prediction pipeline conjugating Alphafold and symmetrical docking method....
Predicting Heart Attack Risk Faster and More Accurately than Current Methods:...
Occlusion myocardial infarction (OMI) impacts a large number of people, yet fewer get diagnosed. This is because their ECG fails to show an elevation in...
Guardians of Immunity: How T Cells Fight Diseases and Keep Us...
T cells are a category of immune cells. They play an important role in maintaining normal bodily functions and battling diseases. When faced with...
Innovating Molecular Simulations: Unleashing the Power of Artificial Intelligence for Revolutionary...
In a recently published paper by Peking University, China, researchers have outlined an analogy between the advancement of Artificial Intelligence (AI) technologies and the...
Predicting Childhood ADHD Symptoms: The Promising Role of Neurocognitive Assessments and...
A recent paper published in the Nature Journal has outlined a machine learning approach that predicts childhood attention-deficit/hyperactivity disorder (ADHD) symptoms by deploying information...
Deep Encoder-Decoder Network: A Tool for Fragment Linker Prediction in PROTACs...
PROTACs (PROteolysis TArgeting Chimeras) are small molecules capable of degrading target proteins. They have wide applications in designing drugs with therapeutic effects, especially in...
Transforming Healthcare: Empowering Clinicians with AutoPrognosis 2.0’s Democratized Machine Learning Models
A recent paper authored by researchers from the University of California, USA, and the University of Cambridge, UK, introduced an open-source Machine Learning framework,...
Uncovering the Genetic Blueprint of Cotton: Scientists Sequence the Cotton Genome
Cotton, the primary source of natural fiber worldwide, has played a significant role in the textile industry. While several cotton species exist, only four...
Meet BioAutoMATED: Empowering Life Scientists with Automated Machine Learning for Analyzing...
BioAutoMATED, a novel, automated machine-learning tool, has eliminated the biggest barrier preventing life scientists from effectively utilizing machine learning (ML) in the analysis and...
AI-Powered Protein-Protein Interaction Drug Discovery Pipeline Yields a Promising SARS-CoV-2 Inhibitor
The multi-adaptive Support Vector Machine Learning Algorithm (maSVM) can improve comparisons between binary Protein-Protein interaction datasets gathered in different laboratories and can be utilized...
Discovering Genome-Wide Chromatin Contacts: Multiplex-GAM vs. Hi-C Showdown!
Chromatin conformation capture techniques have revolutionized our understanding of genome organization and its impact on gene regulation. Hi-C, the gold standard method for investigating...
Drug-Microbiome Interactions: Machine Learning Model Predicts How Drugs Affect Gut Microbiome
In a recent Nature publication by Algavi and Elhanan, faculties from Tel Aviv University, Israel, have tackled the challenge of fully understanding the adverse...
Exploring Molecular Alterations in Cancer with cProSite: A Web-based Tool for...
Large-scale proteome and genomic investigations play a crucial role in understanding the molecular causes of cancer. The National Cancer Institute's Clinical Proteomic Tumor Analysis...
Unveiling the Spatial Dynamics of Malaria Transmission: A Patch-based Modeling Framework
The researchers managed the challenge of quantification, simulation, and weight appointment to counterfactuals and diversely heterogeneous sets of factors accountable for the dynamism of...
Unlocking New Insights in Alzheimer’s Disease: UC Irvine Neuroscientists Develop the...
Transcriptomics has revolutionized our understanding of gene expression and its impact on biological processes. High-dimensional transcriptomics data provides researchers with much information, but analyzing...
Mapping the Cellular Landscape of the Lung: A Comprehensive Human Lung...
The Human Lung Cell Atlas (HLCA) represents comprehensive single-cell transcriptomics of the human respiratory system, which aims to resolve the limitations in previous atlases....
UCSC Scientists Introduce “DeepSea”: A Novel Deep Learning-based Software for Cell...
The field of cell biology is undergoing rapid advancement, necessitating a comprehensive comprehension of the intricate dynamics and heterogeneity exhibited by individual cells in...
Drug Off-target Discovery: Integrating Machine Learning, Metabolomics, and Structural Analysis in...
Drugs often impact intracellular off-targets, i.e., targets that are unintended. Identifying drug off-targets is extremely important for understanding the mechanism of drug action and...
Enhancing Protein Complex Structure Modeling: Deep Learning Meets Crosslinking Mass Spectrometry
Protein complex structures form the foundation for understanding the complexities of molecular biology. While deep learning-based methods have significantly enhanced the prediction of single...
MIT’s Language Model “ConPLex” Applied to Protein-Drug Interactions Speeds up Screening...
Scientists at the Massachusetts Institute of Technology and Tufts University introduced a deep-learning model named ConPLex that executes sequence-based predictions of Drug Target Interaction...
Serverless Deep Learning Models for Peptide Property Prediction: Achieving State-of-the-Art Results without...
Researchers at the Chemical Engineering Department of the University of Rochester, New York, introduce three deep learning sequence-based models for peptide properties prediction implementing...
Advancing Drug Discovery with DrugEx: An Open-source Software Package for Exploring...
The discovery of novel molecules with desired properties is a long-standing challenge in medicinal chemistry. On the other hand, de novo drug design tools...
Integrating Transcriptomics, Metabolomics, and In-silico Drug Predictions to Analyze Burn-induced Liver...
Burn injuries often incite substantial morbidity and lethality aftermath. Severe burn injuries due to hot liquids or solids and fire induce pathophysiological and inflammatory...
Mount Sinai’s Vision-based Transformer Model “HeartBEiT” Interprets Electrocardiograms as Language
Mount Sinai researchers have revealed a groundbreaking artificial intelligence (AI) model for electrocardiogram (ECG) analysis. This pioneering approach allows ECGs to be interpreted as...
Advancing Protein Subcellular and Suborganellar Localization Prediction and Visualization with MULocDeep
Researchers at the University of Missouri upgraded the MULocDeep web application by integrating animal, fungi, and plant species-specific models that meet competitive performance at...
Meet Snekmer: A Powerful Machine Learning Based Protein Annotation Tool Utilizing...
Snekmer, an innovative software developed to better understand protein function in microbes, emerged from the collaborative efforts of researchers from the Pacific Northwest National...
AI-Driven Retrosynthesis Prediction with G2Retro: Prospects in Drug Development
Recent research has demonstrated that generative AI has the ability to greatly speed up drug development in the pharmaceutical industry. Currently, it can take...
Accelerating Genomic Workflows: Harnessing the Power of NVIDIA Parabricks
As the integration of genome sequencing in scientific research, government policy, and personalized medicine continues to grow, the primary challenge for researchers has shifted...
Exploring Protein Modifications in Metabolic Diseases: Molecular Mechanisms and Therapeutic Strategies
Comprehensive introspection of cellular and molecular level aberrations that prompted the worldwide prevalence of life-threatening Non Communicable Diseases like Diabetes, Obesity, Hypertension, Nonalcoholic Fatty...
Meet MDASAE: A Novel Microbe-Drug Association Prediction Model
Scientists from the Hengyang Normal University, China, designed a stacked autoencoder (SAE) with a multi-head attention mechanism-based microbe drug association prediction model named MDASAE....
Repurposing ‘Failed’ Antibiotics: A Promising Approach for Herbicide Development
Resilient weeds, impervious to herbicides, present a grave danger to both the environment and the agricultural sector. The pressing demand for fresh herbicides, armed...
Transforming Natural Product Identification through Deep Self-Supervised Learning Language Models
Scientists from the Microsoft Research Lab, Cambridge, have designed a Self-Supervised Neural Network Masked Language Model named BiGCARP to accelerate the identification and classification...
Accelerating Genomic Analysis with GPUs: Meet the Genomics-GPU Benchmark Suite
Genomic analysis plays a vital role in various areas, such as disease detection, drug development, and genetic disease identification. With the exponential growth of...
Harnessing Machine Learning to Expedite the Search for Plants with Antimalarial...
Malaria, a parasitic disease transmitted by mosquitoes, poses a grave global health menace. With countless annual cases and the escalating defiance of traditional antimalarial...
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...