Scientists from BGI-Research developed a new version of the Cultivated Genome Reference (CGR), a repository of high-quality draft genomes of the human gut microbiome. The current version of CGR, which is CGR2, has been further expanded to incorporate numerous high-quality draft genomes generated from cultivated bacteria. CGR2 classifies previously unidentified species and uncovers the functional…
Neuropsychiatric disorders are a serious public health concern, given that they are the leading cause of disability and account for millions of deaths worldwide. Despite thorough research efforts during the past few years, our understanding of the complex biological mechanistic processes underlying these conditions remains mainly unknown. Recently, a study by researchers at RIKEN BSI…
Researchers at the Abdul Jameel Clinic for Machine Learning in Health, MIT, have developed a novel method, DiffDock, an accelerated drug discovery pipeline using a diffusion generative model (DGM)-based approach for molecular docking. One of the crucial processes involved in a drug design pipeline involves molecular docking, which predicts the binding structure of a ligand…
In the past few years, there has been a significant increase in multi-omics data generation for biomedical research. However, the analysis of these large-scale, complex, and heterogeneous datasets represents an enormous challenge for researchers. Recently, researchers from the Allen Institute of Immunology have developed a ground-breaking PALMO platform, which contains five analytical modules allowing the…
An innovative artificial intelligence program called CLEAN (contrastive learning–enabled enzyme annotation) has the ability to predict enzyme activities based on their amino acid sequences, even if the enzymes are unfamiliar or inadequately understood. The researchers have reported that CLEAN has surpassed the most advanced tools in terms of precision, consistency, and sensitivity. However, a deeper…
Scientists from Shanghai have developed a de novo drug design method, QADD (quality assessment-based drug design approach). The multi-objective reinforcement learning-based method is capable of designing chemical molecules with desired properties using a novel Quality Assessment (QA) module. The novel drug design framework is able to cover a large territory of the chemical space that was previously…
The identification of ligand-binding sites on the surface of a protein is a vital aspect of structure-based drug design. SiteRadar is a new algorithm for identifying ligand-binding sites on protein surfaces, which is important for structure-based drug design. It outperforms existing techniques, including machine learning algorithms, and can detect up to 74% of true ligand-binding…
Recent findings by the Smidt Heart Institute based on deep learning analysis of medical images suggest, that one’s likelihood of developing atrial fibrillation and heart muscle disease can be predicted by the shape of their heart as well as certain genetic indicators. What Determines Heart Shape? It’s crucial to first understand what affects how your…
Conventionally, cancer diagnostic procedures have been known for their invasive nature and enormous cost. However, researchers from KAIST and Korea University have developed a novel diagnostic approach that combines AI and innovative technology for an affordable, accessible, and non-invasive diagnosis of multiple types of cancer at an early stage under high resolution. In the study,…
Lawrence Livermore National Laboratory, California’s scientists have developed a theoretical model based on Dynamic Density Functional Theory (DDFT) for modeling multicomponent cellular membranes as a continuum. The cellular membranes considered are composed of an arbitrary number of lipid types, and the continuum model for the cellular membranes is trained on molecular dynamics (MD) simulations. Within…
Cancer therapies often become ineffective due to the development of resistance in the tumor cells against the treatment. One potential strategy to overcome this resistance was treating the patients with a combination of drugs. Scientists conducted a study to improve cancer treatment by developing deep-learning models for drug synergy prediction. The models were designed to…
Scientists from the University of Tübingen, Germany, have developed a novel workflow for predicting robust druggable targets against emerging viral infections such as the SARS-CoV-2. The workflow implementation comprises pymCADRE and PREDICATE, which incorporate metabolic changes in infected cells for accelerated prediction of drug targets. The authors generated tissue-specific metabolic models, constructed viral biomass functions,…
A recent study published in Nature Communications comprehensively evaluated computational methods used for spatial transcriptomics data analysis. The scientists investigated how well various advanced computer methods performed on real and simulated datasets and recommended which method to use depending on the type of analysis required. The guidelines may be helpful to researchers who want to improve the…
The discovery of cryptic pockets has widened the scope of drug development by permitting the targeting of proteins previously deemed undruggable due to the absence of pockets in their ground-state structures. However, detecting cryptic pockets takes a significant amount of time and effort. PocketMiner is a machine learning tool built by researchers from the University…
Scientists from Kyoto University, Japan, have developed an integrated gut-liver-on-a-chip platform as an in-vitro human model for the study of non-alcoholic fatty liver disease (NAFLD). The authors co-cultured human gut and liver cells and connected them via microfluidics to mimic the human gut-liver axis (GLA). The gut and liver mimicking co-cultured cells exhibit protective effects…
Healthcare providers have been using routine hospital tests to evaluate patients’ health for decades. Electrocardiogram (ECG) tests are frequently a part of these tests. This standard procedure involves attaching small electrodes to the patient’s chest to examine the heart’s electrical activity and rhythm. Predicting the risk of death for patients has proven to be a…
Scientists from The Hospital for Sick Children, Toronto, have developed an atlas for childhood cancer diagnostic classification using machine learning approaches. Pediatric cancers are one of the leading causes of mortality in children worldwide. However, there is no comprehensive molecular assay for pediatric cancer diagnosis and classification. Currently available transcriptome-based diagnostic tools are based on…
Due to the absence of adequate theoretical models and the enormous protein sequence space, the data-driven design of proteins with particular functions is difficult. The Protein Transformer Variational AutoEncoder (ProT-VAE) is a novel deep generative model for protein engineering that combines the capabilities of variational autoencoders and transformers to provide an accurate, quick, and transferrable…
Scientists from the University of Hamburg, Germany, have developed a novel methodology, DiNiro, for unraveling differential regulatory disease mechanisms from single-cell RNA-seq data. Computational tools for analyzing differential gene expression profiles and differential pathway expression have been previously developed. DiNiro is the first known method based on single-cell data for analyzing differential regulatory disease mechanisms….
According to a new study presented at the 52nd Annual Meeting & Exhibition of the AADOCR, held in conjunction with the 47th Annual Meeting of the CADR, the researchers aim to develop a machine learning model for predicting cardiovascular disease using indicators of oral infections. The researchers believe that by using Machine learning, the relationship…
In the field of radiology, generative models, notably DALL-E 2, have tremendous promise for image production and modification. Scientists have shown that DALL-E 2 has learned relevant x-ray picture representations and is able to produce new images from text descriptions, expand images beyond their initial borders, and delete elements. Despite limitations, the authors feel that…
Scientists from Germany have developed SCEMILA, an inherently explainable AI tool for identifying diagnostic cells of acute myeloid leukemia (AML) subtypes. The authors find that the high-attention cells identified by SCEMILA coincide with cells labeled as diagnostically relevant by human experts. The model is trained based on single white blood cell images from digitized blood…
Scientists from the Zhejiang University of Technology, China, have proposed a new method, PAthreader, to improve the recognition accuracy of remote homologous structures by designing a three-track alignment between predicted distance profiles and structure profiles extracted from the PDB and AlphaFoldDB. As a result, AlphaFold2 performs better, and structural modeling is more precise. The results…
The experience of pain is complicated, subjective, and unique to each person. While some people have a high pain tolerance, others may regard even minor discomfort as excruciating pain. Pain affects over 1.5 billion people worldwide and costs more than $600 billion in the United States annually. Despite the availability of various pain relief medications,…
The detection of biomolecules at the nanoscale is of great significance in fundamental biology research, just as it is for biomedical investigations. The evolution of techniques for the detection and characterization of biomolecules has resulted in remarkable scale and resolution in terms of the size and mass of the molecules. Scientists from Germany have achieved…
Current COVID-19 vaccines induce neutralizing antibodies that can inhibit viral entry, but their efficiency decreases when new variations arise. On the basis of short peptide epitopes that stimulate CD8+ and CD4+ T cell responses, scientists have created a new mRNA-LNP (Lipid nanoparticle) vaccine called MIT-T-COVID. Researchers evaluated this novel vaccination on mice infected with the…
Researchers from Thailand perform a systematic cheminformatics analysis and machine learning modeling to study how human Androgen Receptors (AR) antagonists combat Prostate cancer, one of the leading causes of death in male cancer patients. Androgen receptor signaling drives the growth and prostate cancer progression. The authors perform the study on human AR antagonists from the…
Molecular biology is being revolutionized by the application of artificial intelligence to simulate the structures of proteins and their complexes. Researchers utilized a combination of in-cell crosslinking mass spectroscopy and co-fractionation mass spectroscopy in order to find protein-protein interactions in Bacillus subtilis. With the help of AlphaFold-Multimer, the scientists predicted the structures of protein interactions,…
Scientists from Prague have developed MZmine3, a scalable platform for the analysis of mass spectrometry (MS) data from different instrumental setups. The platform supports hybrid datasets from various setups such as liquid and gas chromatography (LC and GC)-MS, ion mobility spectrometry (IMS)-MS, and MS imaging. Spatial metabolomics analyses with increased annotation confidence is now feasible…
The prediction of drug-target interactions (DTI) plays a significant role in drug development by helping in the identification of new drug targets, the repurposing of current medications for new indications, and the prediction of potential adverse drug effects. This article discusses a research study that presents a novel computational model for predicting DTIs. The model…
Scientists from Finland have studied and explored two transformers (deep learning models), BERT and XLNet, in the context of predicting 6-month mortality in cardiac patients from over 23,000 heterogeneous electronic health records (EHR). Transformer neural networks in deep learning are methods used for sequential data modeling. Their inbuilt properties come in handy in predicting mortality…
Scientists from Princeton University have developed a comprehensive model of the structure and function of the open reading frame (ORF) 1 of Hepatitis E Virus (HEV), a type of RNA Virus that causes more than 20 million infections yearly. The ORF1 is a multifunctional protein essential for viral replication, however, it is still being determined…
Scientists from the University of California, Irvine, have developed NeuronChat for inferring inter-neuronal communication using single-cell transcriptomics data. Previous cell-cell communication inference methods are based on short-range signaling enabled by diffusion or physical contact. Neurons communicate via synapses involving neurotransmitter signaling and extend over long distances. The connectome, the wiring organization of neural connectivity, is…
Scientists from Johns Hopkins University lay out the possibility of energy-efficient biocomputing using brain organoids (3D brain cell cultures) that surpass in-silico computing capabilities. The recent advances in how human stem cell-derived brain organoids replicate crucial cellular, and molecular aspects of learning and memory have led to the coining of the term Organoid Intelligence (OI). The development in brain…
Scientists have developed a new deep-learning-based approach called DRPScore for identifying native-like RNA-protein complexes essential for understanding cellular processes. DRPScore outperforms existing methods in identifying native-like structures with high accuracy, even in challenging cases involving significant conformational change. The method was tested on sets of RNA-protein complexes with varying degrees of conformational change induced by…
Scientists from the University of Michigan have developed a single-cell guided pipeline to aid the repurposing of drugs (ASGARD) by defining a drug score for drug recommendation. The drug score is calculated by considering intercellular heterogeneity in each patient tissue sample. Single-cell RNA sequencing (scRNA-seq) is essential for addressing and analyzing intercellular heterogeneity in several…
For many years, scientists have been baffled by the difficult task of designing enzymes, which is regarded as one of the most difficult challenges in biochemistry. However, the introduction of new techniques that allow the creation of scaffolds tailored to specific functions has provided new hope. The research published in Nature reports the production of highly active and…
Scientists from Michigan State University have introduced the LongBondEliminator, a tool designed to detect and correct ring penetrations in molecular simulations. Ring penetrations is a prevalent problem that leads to unrealistic physical behavior in simulations, and LongBondEliminator provides a powerful solution and has the potential to enhance the accuracy of simulations for a number of…
Scientists from China have developed DeepBIO, an automated and interpretable deep learning platform for high-throughput biological sequence functional analysis. The first-of-its-kind platform enables researchers to develop new deep-learning architectures addressing particular biological questions. For any given biological sequence data, DeepBIO offers 42 state-of-the-art deep learning algorithms for model training, comparison, optimization, and evaluation in a…
Substantial progress has been made in assembling the first complete human genome sequence by the Telomere-to-Telomere consortium. However, complex repetitions in diploid genomes are still difficult to decode. Using lengthy, accurate reads and haplotype-specific markers, the Verkko pipeline is an automated method for assembling diploid genomes with high precision. The construction of pangenomes and chromosome-scale…
Single-cell RNA sequencing (scRNA-seq) has proved to be a powerful tool for deciphering the entire repertoire of transcripts in a single cell. Traditional sequencing techniques detect averaged transcript levels for cell populations. The emerging technology of scRNA-seq enables the classification of cell subpopulation and cell heterogeneity. The authors of the review focus on the recent…
King’s College London scientists examined biological samples from hundreds of participants in a European study. The researchers studied the biological data at different stages, including genes, proteins, metabolites, cognitive function, clinical diagnosis, and the development of Alzheimer’s disease (AD). The researchers present new perspectives on the variations of specific proteins and metabolites associated with Alzheimer’s…
Scientists from the University of Maryland have developed PRAWNS, a scalable and efficient tool for multi-genome analysis. With the unprecedented number of complete and nearly complete genomes being accessible, researchers interested in understanding the genomic basis of phenotypes, such as antibiotic resistance in bacteria, require computational tools capable of performing multiple genome comparisons efficiently. However,…
Researchers from Scripps Research and Japan have discovered how a protein’s “poisoned” form can set off a chain of events promoting specific cancers’ growth. The research findings were published in Nature Communications. A potential drug that could return the protein to its original state was developed as a result of this study. The drug significantly…
Scientists from the Wellcome Sanger Institute, Hinxton, UK, and the University of Tartu, Estonia, have developed a machine learning-based algorithm, MinsePIE, to predict prime editing insertion efficiencies. The authors performed a comprehensive analysis of prime editing efficiencies by designing a library of 3604 sequences of varying lengths and measuring the frequency of insertion into four…
Scientists from the University of Southern Denmark have made an interesting discovery that may help in the fight against antibiotic-resistant bacteria. The research team has identified five novel virus species that were not known earlier to the scientific community. The finding may offer new treatment strategies against deadly infections as the virus species discovered can…
Scientists from Purdue University unravel cell migration direction under the influence of environmental cues using a ternary logic gate system. Cells detect several environmental cues and process intracellular signals to determine the direction of migration. Cell migration is involved in various physiological mechanisms as well as pathological processes. Signaling pathways and molecules involved in directed…
The biological activity of a protein is determined not only by its static, three-dimensional structure but also by its dynamic characteristics, which form a variety of shapes. This study demonstrates that machine learning can be taught on protein simulation data to build physically realistic conformational ensembles without requiring expensive sampling. The researchers trained a generative…
Scientists from the Genome Institute of Singapore and A*STAR Singapore have developed a neural network-based method that implements a multi-instance learning framework, “m6Anet,” to accurately detect the RNA modification m6A from genomic data. Around 150 types of RNA modifications are currently known, of which m6A methylation is noteworthy for its role in diseases. These modifications…
Autism Spectrum Disorder (ASD), also known as autism, is a global health issue affecting millions of children worldwide. According to the World Health Organization (WHO), approximately 1 in 100 children has autism globally, with the prevalence varying across different regions and countries. Identifying autism in early childhood is crucial for providing autistic children and their…
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