Machine Learning

Machine learning in bioinformatics: models, methods and applications for analyzing biological and clinical data.

AI | Bioinformatics | Drug Discovery | Machine Learning | News | Paper Summary | Structural Biology | Tools | Trending
ETH Zurich Chemists Unveil DRAGONFLY: New AI Approach for Rapid Drug Design from Protein Structures
ByAnchal Negi April 30, 2024October 22, 2026
Centuries of scientific invention have been driven by the search for new drugs to fight against diseases. Since medicinal plants became valuable sources of medicine from synthetic chemistry, we have consistently improved our capacity to design molecules with therapeutic potential. However, the traditional drug discovery process is time-consuming, costly, and fraught with challenges. It is…
Read More ETH Zurich Chemists Unveil DRAGONFLY: New AI Approach for Rapid Drug Design from Protein Structures
AI | Bioinformatics | Machine Learning | News | Paper Summary | RNA-Seq | Structural Biology | Tools | Trending
Cracking the RNA Inverse Folding Puzzle with RiboDiffusion’s Generative Diffusion Approach
ByAnshika Gupta April 28, 2024October 22, 2026
Molecular biology scientists have been captivated by the complex way RNA folds. A recent study by Beihang and Nanjing University researchers in China introduces the RiboDiffusion model. This new method looks at predicting protein structure at the atomic level and is more advanced than existing methods like StructGNN and GVP-GNN. RiboDiffusion: A Game Changer Designing…
Read More Cracking the RNA Inverse Folding Puzzle with RiboDiffusion’s Generative Diffusion Approach
AI | Big Data | Bioinformatics | Computational Models | Gene Expression | Genomics | Machine Learning | News | OMICS | Paper Summary | RNA-Seq | Tools | Trending
Decoding the Regulatory Landscape with LINGER: A New Era in Gene Regulatory Networks Inference
ByDeotima Chakraborty April 27, 2024October 22, 2026
Despite the difficulties presented by the current methods depending solely on gene expression data, RNA sequencing data has been incorporated into Gene regulatory networks (GRN) inference methods, improving the capacity to learn complicated pathways from a few independent data points. Here, scientists offer LINGER (Lifelong neural network for gene regulation), a machine-learning technique that uses…
Read More Decoding the Regulatory Landscape with LINGER: A New Era in Gene Regulatory Networks Inference
AI | Bioinformatics | Machine Learning | Molecular Dynamics | News | Paper Summary | Tools | Trending
GEMS: DeepMind’s Novel Approach to Accurate Biomolecular Dynamics through Machine-Learned Quantum-Mechanical Force Fields
ByAnchal Negi April 24, 2024October 22, 2026
Think about peering into the intricate world of biomolecules, those complex assemblies that direct the symphony of life. Scientists have long relied on molecular dynamics (MD) simulations to watch them dance, but these often require costly quantum-mechanical calculations to get accurate results. However, a groundbreaking new technique called GEMS (general approach for constructing accurate machine-learned…
Read More GEMS: DeepMind’s Novel Approach to Accurate Biomolecular Dynamics through Machine-Learned Quantum-Mechanical Force Fields
AI | Bioinformatics | Database | Machine Learning | News | Paper Summary | PPI | Structural Biology | Trending
A New Era in Interactomics: Exploring Predictomes, the AlphaFold-Modeled Protein Interaction Database
ByAnchal Negi April 20, 2024October 22, 2026
Imagine a teeming city where proteins are the movers and shakers. Each protein intertwines with others in partnerships that are vital to every cellular process. Nevertheless, unraveling these interactions, which are called protein-protein interactions (PPIs), forms the basis for both biology and drug development. Detecting PPIs has been akin to overhearing hushed conversations in a…
Read More A New Era in Interactomics: Exploring Predictomes, the AlphaFold-Modeled Protein Interaction Database
AI | Bioinformatics | Drug Discovery | Machine Learning | Neural Network | News | Paper Summary | Structural Biology | Trending
Smart Sampling for Smarter Drugs: How Active Learning Boosts Drug Discovery
ByDeotima Chakraborty April 16, 2024October 22, 2026
In computational drug discovery, active learning (AL) is a potent approach that makes it possible to identify the best binders from large chemical libraries. To get the best results, designing a strong AL methodology necessitates an understanding of AL parameters and data set properties. In order to systematically assess the efficacy of machine learning models…
Read More Smart Sampling for Smarter Drugs: How Active Learning Boosts Drug Discovery
AI | Bioinformatics | Covid-19 News | Drug Discovery | Machine Learning | News | Paper Summary | Structural Biology | Trending
Creating Antibodies from Scratch: Scientists Design Single-Domain Antibodies with Atomic Precision Using AI
ByDeotima Chakraborty March 21, 2024October 22, 2026
The inability to logically design new antibodies that bind a particular epitope on a target persists despite the crucial role that antibodies play in contemporary medicine. Instead, time-consuming animal immunization procedures or library screening techniques are currently used in antibody development. Here, scientists from the University of Washington show that user-specified epitopes can be bound…
Read More Creating Antibodies from Scratch: Scientists Design Single-Domain Antibodies with Atomic Precision Using AI
AI | Bioinformatics | LLM | Machine Learning | News | Paper Summary | Proteomics | Trending
Unveiling DeepGO-SE: Advancing Protein Function Prediction Leveraging Language Models and GO Knowledge
ByDeotima Chakraborty February 19, 2024October 22, 2026
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 and reasoning skills are necessary for predicting the actions of proteins in order to preserve consistency and take advantage of prior knowledge. It is still difficult to apply all of the…
Read More Unveiling DeepGO-SE: Advancing Protein Function Prediction Leveraging Language Models and GO Knowledge
AI | Bioinformatics | Education | Machine Learning | News | Paper Summary | Proteomics | Trending
Empowering Researchers with ProteomicsML: An Online Oasis of Data Sets and Tutorials for Machine Learning in Proteomics
ByDeotima Chakraborty January 30, 2024October 22, 2026
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 amount of data reduction from raw data to machine learning-ready data. In spite of the difficulties with accessibility and repeatability, laboratories frequently employ distinctive and intricate data processing pipelines to maximize…
Read More Empowering Researchers with ProteomicsML: An Online Oasis of Data Sets and Tutorials for Machine Learning in Proteomics
AI | Bioinformatics | Biomarker Discovery | Disease Diagnosis | Genomics | Machine Learning | News | OMICS | Tools | Trending
IntelliGenes Unveiled: A New Paradigm for AI-Driven Biomarker Discovery from Multi-genomic Data
ByDr. Tamanna Anwar January 27, 2024October 22, 2026
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 to discover biomarkers for disease prediction with high accuracy. It implements Intelligent Gene (I-Gene) scoring to assess the significance of biomarkers and create individual profiles to understand disease prognosis. IntelliGenes is…
Read More IntelliGenes Unveiled: A New Paradigm for AI-Driven Biomarker Discovery from Multi-genomic Data
AI | Bioinformatics | Machine Learning | News | Paper Summary | Proteomics | Structural Biology | Tools | Trending
Unveiling the Future of Protein Design: RFdiffusion’s De Novo Revolution
ByDeotima Chakraborty January 26, 2024October 22, 2026
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 unexplored. Diffusion models have shown success in image and language generative modeling but have limited success in protein modeling due to the complexity of protein backbone geometry and sequence-structure relationships. The…
Read More Unveiling the Future of Protein Design: RFdiffusion’s De Novo Revolution
AI | Bioinformatics | Computational Models | Drug Discovery | LLM | Machine Learning | News | Paper Summary | Structural Biology | Trending
Meet ChatDrug: A New ChatGPT-powered Conversational Drug Editing Framework
ByDeotima Chakraborty January 23, 2024October 22, 2026
Drug discovery is one of the many fields where recent developments in conversational large language models (LLMs), like ChatGPT, have shown incredible promise. However, the majority of the work that has already been done is on examining conversational LLMs’ skills with regard to chemical reactions and retrosynthesis. However, there is still much to learn about…
Read More Meet ChatDrug: A New ChatGPT-powered Conversational Drug Editing Framework
AI | Bioinformatics | CRISPR | Machine Learning | News | Paper Summary | Trending
Fighting Pathogens Smarter: How Machine Learning and Data Integration Improves Bacterial CRISPRi Guide Efficiency Prediction
ByAnchal Negi January 19, 2024January 19, 2024
CRISPR interference (CRISPRi) is one of the most widely used CRISPR technologies in bacteria. In this, a catalytically dead Cas protein incapable of DNA cleavage (dCas) is targeted to interfere with the transcription of a gene of choice. Due to complex interactions between guide sequences predicting it accurately remains a challenge. To tackle this problem,…
Read More Fighting Pathogens Smarter: How Machine Learning and Data Integration Improves Bacterial CRISPRi Guide Efficiency Prediction
AI | Bioinformatics | Machine Learning | Neural Network | Neuroscience | News | Paper Summary | Trending
New Principles Underlying Neural Activity Discovered With Startling Implications For Machine Learning
BySonal Keni January 12, 2024January 11, 2024
One of the biggest challenges in the machine learning space today is the problem of credit assignment: the identification of the components within the information processing pipeline and pinpointing which are responsible for any errors that crop up in the output generated by the algorithm. The common assumption is that the problem is best resolved…
Read More New Principles Underlying Neural Activity Discovered With Startling Implications For Machine Learning
Bioinformatics | Drug Discovery | Machine Learning | News | Paper Summary | Structural Biology | Trending
Unleashing MACE-OFF23: Machine Learning Empowers Transferable Force Field for Organic Molecules
ByDeotima Chakraborty January 4, 2024October 22, 2026
For more than fifty years, biomolecular simulation has been dominated by classical empirical force fields. Despite being extensively employed in biomolecular dynamics, crystal structure prediction, and drug discovery, they typically lack the precision and transferability necessary for predictive modeling. In this paper, researchers from the University of Cambridge introduced MACE-OFF23. A high degree of quantum…
Read More Unleashing MACE-OFF23: Machine Learning Empowers Transferable Force Field for Organic Molecules
Big Data | Bioinformatics | Cell | Genomics | Machine Learning | News | Paper Summary | Tools | Trending
CellHint Revolutionizes Human Cell Atlas: Automatic Cell Type Harmonization Paves the Way for New Discoveries
BySonal Keni December 29, 2023January 4, 2024
Over the past few years, a large number of single-cell genomics datasets that profile a range of tissues in different stages of development have been created. These resources have been incorporated in the creation of a standard reference map for human bodies by consortia like the Human Cell Atlas. However, different labs may have different…
Read More CellHint Revolutionizes Human Cell Atlas: Automatic Cell Type Harmonization Paves the Way for New Discoveries
Bioinformatics | Disease Diagnosis | Health | Machine Learning | Medical Informatics | News | Paper Summary | Precision Medicine | Proteomics | Trending
Stanford Medicine Scientists Inch Closer to Predicting Which of Our Organs Will Fail First
ByDeotima Chakraborty December 14, 2023December 14, 2023
In a recently published study, Stanford Medicine researchers highlighted age-related problems in humans and showed how aging varies from person to person and between organs within an individual. Researchers used human blood plasma protein levels from particular organs to examine aging in 11 key organs in real people. The approach suggested by the authors offers…
Read More Stanford Medicine Scientists Inch Closer to Predicting Which of Our Organs Will Fail First
AI | Bioinformatics | Machine Learning | News | Paper Summary | Structural Biology | Tools | Trending
Decoding the Dance of Proteins and Nucleic Acids: Machine Learning Empowers Protein-Nucleic Acid Complexes Prediction with RoseTTAFoldNA
ByDeotima Chakraborty December 3, 2023January 26, 2024
Researchers at the University of Washington, USA, have developed a new AI system called RoseTTAFoldNA that can rapidly and accurately predict the 3D structures of proteins bound to DNA and RNA. The ability to model these protein-nucleic acid complexes has important implications across fields from biomedicine to biotechnology. RoseTTAFoldNA expands on the award-winning protein structure…
Read More Decoding the Dance of Proteins and Nucleic Acids: Machine Learning Empowers Protein-Nucleic Acid Complexes Prediction with RoseTTAFoldNA
AI | Bioinformatics | Drug Discovery | Machine Learning | News | Paper Summary | Structural Biology | Trending
Transforming Fragment-based Drug Design: Stanford’s FRAME Employs Machine Learning to Expand Fragments into Ideal Ligands
BySwasti Tyagi December 1, 2023October 22, 2026
Stanford University researchers developed FRAME (Fragment-Based Molecular Expansion), a machine learning framework that uses three-dimensional structures of interactions between proteins and ligands to facilitate drug design based on ‘fragments.’ Fragments are singular chemical groups that are added to ligands for the purpose of performing lead optimization tasks and improving their properties. Ligands are small molecules…
Read More Transforming Fragment-based Drug Design: Stanford’s FRAME Employs Machine Learning to Expand Fragments into Ideal Ligands
AI | Bioinformatics | Health | Machine Learning | Medical Informatics | News | Trending
New Machine Learning Technique Predicts Cancer Patient Cure Rates More Accurately
ByDr. Tamanna Anwar November 24, 2023October 22, 2026
As computing power has exponentially grown, machine learning (ML) techniques have gained immense popularity in healthcare – including predicting outcomes for patients with devastating illnesses like cancer. By estimating survival rates and life expectancy, statistical models help patients and doctors make difficult treatment decisions, weighing benefits against risks. Now, researchers at The University of Texas…
Read More New Machine Learning Technique Predicts Cancer Patient Cure Rates More Accurately
Bioinformatics | Genomics | Machine Learning | News | Paper Summary | Sequencing | Trending
Mosaic Variant Calling: Benchmarking Approaches and Unveiling Best Practices
BySwasti Tyagi October 23, 2023October 22, 2023
Scientists from Yonsei University College of Medicine, Republic of Korea, have developed a new benchmark for evaluating mosaic variant calling methods, which are used to detect genetic mutations in individuals. The benchmark will help researchers select the most suitable calling algorithms for their studies and guide developers in improving the accuracy of these methods. The…
Read More Mosaic Variant Calling: Benchmarking Approaches and Unveiling Best Practices
AI | Bioinformatics | Machine Learning | News | Paper Summary | Trending
Machine Learning Modeling Helps Uncover Links Between Gut Microbiome and Heart Failure
BySonal Keni October 22, 2023October 21, 2023
Heart failure is widely recognized as a leading cause of mortality worldwide. Though there have been great strides made in medical technology over the past few decades, heart failure remains widespread, with 64 million people globally suffering from it. Though early symptoms do show up, heart failure is often undetected until it reaches an acute…
Read More Machine Learning Modeling Helps Uncover Links Between Gut Microbiome and Heart Failure
Bioinformatics | Genomics | Machine Learning | Neural Network | News | Trending
Researchers Develop Neural Network for Genomics that can Explain its Prediction Accuracy
BySwasti Tyagi October 11, 2023October 11, 2023
A breakthrough by a group of researchers at New York University resulted in the development of an interpretable neural network that can explain the process by which it reached its predictions. This is revolutionary for both genomics as well as for artificial intelligence (AI) and machine learning (ML) because up until now, we did not…
Read More Researchers Develop Neural Network for Genomics that can Explain its Prediction Accuracy
Bioinformatics | Cell | Machine Learning | News | Paper Summary | Trending
A New Active Learning Model Makes Cellular Reprogramming and Genetic Intervention Easier than Ever
BySonal Keni October 8, 2023December 20, 2023
Millennia of evolution has allowed life to flourish in a million different ways, each suited to the niches it fills. The advent of CRISPR and other genetic engineering methods has allowed science to re-engineer cells to make them even more efficient, robust and fulfill more varied functions. One method for cellular engineering requires the use…
Read More A New Active Learning Model Makes Cellular Reprogramming and Genetic Intervention Easier than Ever
AI | Bioinformatics | Drug Discovery | LLM | Machine Learning | News | Paper Summary | Structural Biology | Trending
The Rise of Scientific Language Models: Revolutionizing Molecular Discovery
BySwasti Tyagi October 6, 2023October 22, 2026
In a groundbreaking collaboration spanning IBM Research in Switzerland and the USA, scientists have illuminated the pivotal role of language models (LMs) in molecular discovery. It has been determined that transformer-based architectures are the most popular forms of language models, enabling their widespread use in various domains of science. Due to this, the term ‘scientific…
Read More The Rise of Scientific Language Models: Revolutionizing Molecular Discovery
AI | Bioinformatics | Biomedical Engineering | Health | Machine Learning | News | Paper Summary | Trending
Advancing Continuous Blood Pressure Monitoring: Accurate Measurements with Multichannel Sensing Signals in Wearable Devices
BySonal Keni October 5, 2023October 5, 2023
Hypertension has long been known to be a causative factor for a multitude of medical ailments. Hypertension has further been noticed to have significantly increased in recent years, posing a public health concern. While medical costs and inconvenience may deter potential patients from seeking frequent blood pressure monitoring, new advances in wearable medical devices have…
Read More Advancing Continuous Blood Pressure Monitoring: Accurate Measurements with Multichannel Sensing Signals in Wearable Devices
AI | Bioinformatics | Drug Discovery | Machine Learning | News | Paper Summary | RNA-Seq | Tools | Trending
BigRNA: A Foundation Model for RNA Biology that Revolutionizes Personalized RNA Therapeutics
BySwasti Tyagi October 2, 2023October 2, 2023
A team of researchers at Deep Genomics Inc. developed BigRNA, a foundation model for understanding the biological functions and mechanisms of RNA molecules. Two challenges faced by researchers over the years have been modeling RNA correctly and predicting its features, especially when considering interpreting genetic variations and attempting to formulate personalized therapeutics. By feeding information…
Read More BigRNA: A Foundation Model for RNA Biology that Revolutionizes Personalized RNA Therapeutics
AI | Bioinformatics | Drug Discovery | Machine Learning | News | Paper Summary | Structural Biology | Tools | Trending
Machine Learning Model ‘HASTEN’ Enables Giga-Scale Virtual Screening of Enumerated Chemical Libraries
BySwasti Tyagi September 30, 2023October 22, 2026
A team of researchers utilized HASTEN, a machine-learning (ML) model, to perform virtual screening on ultra-large chemical compound libraries. Several challenges are faced when screening libraries of such large scale, and conventional brute-force docking methods that are usually used have drawbacks, especially for libraries that span nearly a billion compounds. HASTEN is a quick and…
Read More Machine Learning Model ‘HASTEN’ Enables Giga-Scale Virtual Screening of Enumerated Chemical Libraries
AI | Bioinformatics | Machine Learning | News | Paper Summary | Proteomics | Structural Biology | Trending
Protein Engineering Tournament: A Competition Aiming to Revolutionize Computational Protein Design
BySonal Keni September 29, 2023October 22, 2026
Protein engineering is an emerging field in biological research – the improvement of existing proteins or the creation of new ones in order to improve efficiency or perform new functions has proved to be a field with much potential. Despite advancements in the field making protein engineering a sought-after field for industrial applications, it is…
Read More Protein Engineering Tournament: A Competition Aiming to Revolutionize Computational Protein Design
AI | Disease Diagnosis | Health | Machine Learning | News | Paper Summary | Proteomics | Structural Biology | Tools | Trending
Google’s DeepMind Unveils AlphaMissense: A Breakthrough AI Tool for Decoding Genetic Enigmas
ByPrachi Khare September 22, 2023September 23, 2023
Google’s DeepMind scientists have made a monumental stride with the inception of AlphaMissense, an AI tool that can pinpoint genes responsible for diseases. This move signals substantial progress in gene research and potentially transforms how rare genetic disorders are detected. The state-of-the-art capacities of AlphaMissense gear towards improving our grasp on intricate genetic changes, thus…
Read More Google’s DeepMind Unveils AlphaMissense: A Breakthrough AI Tool for Decoding Genetic Enigmas
AI | Antibiotic Resistance | Bioinformatics | Infectious Diseases | Machine Learning | News | Paper Summary | Phylogenetic Analysis | Trending
Machine Learning and Phylogenetic Analysis Join Forces to Predict Antibiotic Resistance in Pathogenic Bacteria
BySonal Keni September 22, 2023January 19, 2024
Tuberculosis has been known to be a significant threat for many years now. Despite the development of a number of preventative strategies and medications, it remains widespread, causing millions of deaths globally every year. Part of the difficulty of adequately responding to and treating tuberculosis is due to the emergence of strains that are resistant…
Read More Machine Learning and Phylogenetic Analysis Join Forces to Predict Antibiotic Resistance in Pathogenic Bacteria
AI | Bioinformatics | Computational Models | Drug Discovery | Machine Learning | News | Paper Summary | Structural Biology | Trending
Meet DRfold: A Novel Method to Predict RNA Tertiary Structures with Unprecedented Accuracy
ByPrachi Khare September 21, 2023October 22, 2026
RNAs are essential components of physiological activities, however, accurately modeling their structures has long posed a challenge due to their complex structures. The National University of Singapore researchers introduced DRfold, an innovative technique to predict RNA complex 3D structures. By simultaneously learning local frame rotations and geometric constraints from experimentally solved RNA structures, DRfold addresses…
Read More Meet DRfold: A Novel Method to Predict RNA Tertiary Structures with Unprecedented Accuracy
AI | Bioinformatics | Disease Diagnosis | Health | Machine Learning | Medical Informatics | News | Trending
Machine Learning Predicts Mood in Chronic Stroke Patients and Identifies the Link Between Stroke and Depression
ByDr. Tamanna Anwar September 17, 2023September 17, 2023
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 new hope for post-stroke patients struggling with mood disorders. Stroke, a major cause of disability globally, often accompanies a complex web of challenges, including the common and persistent complication of depression….
Read More Machine Learning Predicts Mood in Chronic Stroke Patients and Identifies the Link Between Stroke and Depression
Bioinformatics | Chemoinformatics | Drug Discovery | Machine Learning | News | Paper Summary | Trending
Virtual Drug Screening with Less Data: Boosting Predictions with Transfer Learning and Kernel Methods
BySwasti Tyagi September 16, 2023October 22, 2026
A team of researchers at the Eric and Wendy Schmidt Centre at the Broad Institute of MIT and Harvard developed a model that uses kernel methods to facilitate transfer learning. Kernel methods are simple machine learning algorithms that are used to train models on data that has been transformed beforehand. Transfer learning is a technique…
Read More Virtual Drug Screening with Less Data: Boosting Predictions with Transfer Learning and Kernel Methods
AI | Bioinformatics | Cancer | Cancer Research | Disease Diagnosis | Health | Machine Learning | News | Paper Summary | Trending
Oxford Researchers Develop a Machine Learning Model to Predict 10-Year Breast Cancer Mortality Risk
ByAditi Sarkar August 30, 2023September 2, 2023
A group of researchers affiliated with Oxford University, UK, have developed a prognostic model backed by the most extensive information set spanning a national, population-representative, and linked electronic healthcare record data set of over 11·6 million female individuals of 20–90 years of age for clinical prediction of females who are at high risk of developing…
Read More Oxford Researchers Develop a Machine Learning Model to Predict 10-Year Breast Cancer Mortality Risk
AI | Bioinformatics | Machine Learning | News | Paper Summary | Proteomics | Tools | Trending
Tuft’s Researchers Unveil MELISSA: Revolutionizing Protein Function Prediction Through Semi-Supervised Embedding
ByNeegar Naushaba Iqbal August 20, 2023August 29, 2023
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 employ network diffusion analysis and structure embedding of proteins, respectively, but they neglect to utilize known protein functions directly. Contrarily, MELISSA, a novel approach, incorporates functional labels during the embedding stage….
Read More Tuft’s Researchers Unveil MELISSA: Revolutionizing Protein Function Prediction Through Semi-Supervised Embedding
AI | Bioinformatics | Education | Machine Learning | Neural Network | News | Paper Summary | Trending
Astrocyte-Powered Transformers: A Biological Blueprint for High-Performance Neural Architectures
ByAditi Sarkar August 19, 2023August 29, 2023
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 decrypt the enigma of neuron-astrocyte networks and their interaction in our brain for cognitive functions and information processing through the fabrication of a computational neuron-astrocyte model that is functionally equivalent to…
Read More Astrocyte-Powered Transformers: A Biological Blueprint for High-Performance Neural Architectures
AI | AMR Alert | Antibiotic Resistance | Bioinformatics | Drug Discovery | Health | Infectious Diseases | Machine Learning | News | Trending
Artificial Intelligence: The New Weapon in the Fight Against Infectious Diseases
ByDr. Tamanna Anwar August 12, 2023October 22, 2026
Infectious diseases have been a persistent challenge to public health despite significant advancements in various scientific disciplines. The emergence of viruses resistant to drugs, the frequency of pathogen outbreaks, and the ongoing danger of pandemics underline the urgent need for new and effective treatments. Addressing these challenges requires the combined efforts of multiple disciplines, and…
Read More Artificial Intelligence: The New Weapon in the Fight Against Infectious Diseases
AI | Bioinformatics | Health | Machine Learning | Medical Informatics | News | Paper Summary | Trending
Large Language Models and Foundation Models in Healthcare: Unveiling the Hype and Addressing the Gaps
ByAditi Sarkar August 4, 2023August 4, 2023
Standford researchers have discoursed the critical aspect for the integration of Foundation Models (FM) in the Healthcare system, that is evaluation of the reliability and extent of applicability of FM models, and advocated metrics, tasks, and datasets for improving the assessment framework that would facilitate effective determination of the value of clinical FMs. The research…
Read More Large Language Models and Foundation Models in Healthcare: Unveiling the Hype and Addressing the Gaps
AI | Bioinformatics | Cancer | GWAS | Machine Learning | News | Paper Summary | Sequencing | Trending
Beyond Biopsies: Single-Molecule Cell-Free DNA Profiling Fuels Non-Invasive Cancer Screening and Detection
ByNeegar Naushaba Iqbal July 30, 2023July 30, 2023
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 University researchers, can effectively diagnose cancer patients using CT scans, even in the early stages (stages I and II), without intrusive treatments like biopsies. This model focuses on the DNA changes…
Read More Beyond Biopsies: Single-Molecule Cell-Free DNA Profiling Fuels Non-Invasive Cancer Screening and Detection
AI | Bioinformatics | Genomics | Machine Learning | News | OMICS | Paper Summary | Proteomics | Tools | Trending
Machine Learning Magic: DNABERT-2 Empowers Enhanced Multi-Species Genomic Analysis
ByNeegar Naushaba Iqbal July 28, 2023October 22, 2026
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 better alternative to the previously used foundation models, DNABERT and Nucleotide Transformer. These models worked by breaking genomic sequences into fixed-size k-mers, which takes a toll on computing efficiency. However, DNABERT-2…
Read More Machine Learning Magic: DNABERT-2 Empowers Enhanced Multi-Species Genomic Analysis
AI | Bioinformatics | Corona Virus | COVID-19 | Covid-19 News | Machine Learning | News | Paper Summary | Trending
Machine Learning Meets Virology: Scripps Scientists Unveil Tracking and Early Warning System for Viral Pandemics
ByNeegar Naushaba Iqbal July 26, 2023July 27, 2023
Imagine possessing the power to foresee the appearance of hazardous ⁠ viral strains well in advance of their impact. The scientists at Scripps Research in the USA invented an ML algorithm that employs spatial covariance, ⁠ built upon Gaussian process (GP) principles, to monitor how genetic changes influence host-pathogen balance in biological contexts. Utilizing GP-based…
Read More Machine Learning Meets Virology: Scripps Scientists Unveil Tracking and Early Warning System for Viral Pandemics
AI | Bioinformatics | Computational Models | Machine Learning | News | Paper Summary | Structural Biology | Trending
Microsoft’s AI²BMD: Transforming Protein Dynamics Simulation with ab initio Accuracy
ByAditi Sarkar July 17, 2023February 9, 2024
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 and machine learning force fields, which accomplishes a generalizable ab initio accuracy for energy and force calculations of proteins comprising over 10,000 atoms with a faster pace, reducing computational time. Being…
Read More Microsoft’s AI2BMD: Transforming Protein Dynamics Simulation with ab initio Accuracy
AI | Big Data | Bioinformatics | Cancer | Health | Machine Learning | News | Paper Summary | Precision Medicine | Tools | Trending
Treating Rare Cancers with CancerGPT: Cracking the Code on Drug Pair Synergy Prediction Leveraging Large Language Models
ByDr. Tamanna Anwar July 16, 2023July 18, 2023
CancerGPT, an advanced machine learning model introduced by scientists from the University of Texas and the University of Massachusetts, USA, harnesses the power of large pre-trained language models (LLMs) to predict the outcomes of drug combination therapy on rare human tissues found in cancer patients. The significance of this innovative approach becomes even more apparent…
Read More Treating Rare Cancers with CancerGPT: Cracking the Code on Drug Pair Synergy Prediction Leveraging Large Language Models
AI | Bioinformatics | Cancer Research | Drug Discovery | Machine Learning | News | Paper Summary | Precision Medicine | Trending
Predicting Immunotherapy Response: Construction and Validation of a Machine Learning-Based Gene Expression Classifier for Primary Triple-Negative Breast Cancer
ByAditi Sarkar July 13, 2023July 13, 2023
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 gene expression profiles of TNBC specimens and artificial intelligence, which has demonstrated a significant performance in identifying TNBC patients who would potentially achieve pCR (Pathological Complete Response) to the chemotherapy treatment…
Read More Predicting Immunotherapy Response: Construction and Validation of a Machine Learning-Based Gene Expression Classifier for Primary Triple-Negative Breast Cancer
AI | Big Data | Bioinformatics | Drug Discovery | Machine Learning | Neural Network | News | Paper Summary | Trending
MIT’s Data-Efficient Molecular Property Predictor: Leveraging Hierarchical Grammar-Induced Geometry
ByAditi Sarkar July 11, 2023October 22, 2026
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 Framework, the researchers address the challenge of curating labeled data for developing accurate deep-learning models. This framework leverages a learnable hierarchical molecular grammar to capture structure-level similarity, paving the way for…
Read More MIT’s Data-Efficient Molecular Property Predictor: Leveraging Hierarchical Grammar-Induced Geometry
AI | Bioinformatics | Disease Diagnosis | Health | Machine Learning | Medical Informatics | News | Paper Summary | Trending
Predicting Heart Attack Risk Faster and More Accurately than Current Methods: The Role of Machine Learning in ECG Diagnosis
ByNeegar Naushaba Iqbal July 3, 2023July 3, 2023
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 the ST region. An elevated ST region indicates the presence of OMI. Such patients can receive great help from immediate reperfusion therapy, but the problem is that no tool can accurately…
Read More Predicting Heart Attack Risk Faster and More Accurately than Current Methods: The Role of Machine Learning in ECG Diagnosis
AI | Bioinformatics | Disease Diagnosis | Health | Machine Learning | News | Paper Summary | Trending
Predicting Childhood ADHD Symptoms: The Promising Role of Neurocognitive Assessments and Machine Learning
ByAditi Sarkar June 30, 2023June 30, 2023
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 about neurocognition, impulsivity, screen time, family & social contexts, and other relevant factors. The futuristic study executed by researchers from the University of Michigan and Emory University, USA, provides a significant…
Read More Predicting Childhood ADHD Symptoms: The Promising Role of Neurocognitive Assessments and Machine Learning
AI | Bioinformatics | Health | Machine Learning | News | Paper Summary | Tools | Trending
Transforming Healthcare: Empowering Clinicians with AutoPrognosis 2.0’s Democratized Machine Learning Models
ByAditi Sarkar June 27, 2023June 27, 2023
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, Autoprognosis 2.0, to encounter the technical and practical challenges that are obstructing the large-scale adoption of Machine Learning (ML) models in clinical settings and to empower non-ML expert medical professional, facilitating…
Read More Transforming Healthcare: Empowering Clinicians with AutoPrognosis 2.0’s Democratized Machine Learning Models
AI | Bioinformatics | Machine Learning | News | Paper Summary | Trending
Meet BioAutoMATED: Empowering Life Scientists with Automated Machine Learning for Analyzing and Designing Biological Sequences
ByNeegar Naushaba Iqbal June 25, 2023December 25, 2023
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 design of biological sequences. This platform, developed by a group of scientists from Harvard University and MIT, brings together multiple automated machine learning (Auto ML) techniques into a unified framework, making…
Read More Meet BioAutoMATED: Empowering Life Scientists with Automated Machine Learning for Analyzing and Designing Biological Sequences

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