Malaria, a parasitic disease transmitted by mosquitoes, poses a grave global health menace. With countless annual cases and the escalating defiance of traditional antimalarial drugs, there is an urgent need to expedite the pursuit of innovative treatments. In a recent publication in Frontiers on May 25, 2023, scientists from the Royal Botanic Gardens, Kew, and…
A groundbreaking study conducted by scientists from MIT and McMaster University has unveiled a revolutionary artificial intelligence-based antibiotic that effectively eliminates a specific strain of bacteria often associated with drug-resistant infections. If it receives approval for patient usage, the newly developed antibiotic has the potential to significantly contribute to the battle against Acinetobacter baumannii, a…
Artificial intelligence (AI) has significantly changed the scientific research landscape in recent years, yet structure-based drug development has not been significantly affected. However, a promising development comes from German scientists in the form of the MISATO dataset, a curated collection of protein-ligand complexes, molecular dynamics traces, and electronic properties. This article explores the significance of…
A team of UCSF, UCSD, and Brown University scientists has produced a multi-scale map of protein assemblies pertaining to damage response. The DNA Damage Response (DDR) makes sure that DNA replication, as well as transcription, are error-free. Disruptions in DDR cause several diseases. It is quite challenging to determine the proteins managing the DDR as…
In recent years, significant advancements in machine learning, particularly AlphaFold 2 (AF2), have revolutionized protein structure prediction. These breakthroughs have generated excitement in the field of drug discovery, as accurate protein structures are crucial for rational drug design. However, the translation of these advances into predicting ligand binding poses for drug candidates remains uncertain. This…
Scientists from CHOP, Philadelphia, and UCLA have developed an integrated computational workflow to discover novel cancer immunotherapy targets from pre-mRNA alternative splicing (AS). The prevalence of alternate splicing across different cancers is wide, yet the vast repertoire of cancer immunotherapy targets remains unexplored. IRIS has been developed to identify AS-derived tumor antigens (TAs) for T…
Genomics to Notebook (g2nb) is a new environment that combines the popular JupyterLab notebook system with well-established bioinformatics platforms to provide a seamless and accessible workflow for genomics analysis. While Jupyter Notebook and JupyterLab have become widely used in data science and bioinformatics, incorporating existing bioinformatics analysis platforms like Galaxy and GenePattern into the notebook…
Artificial intelligence nowadays dominates our news feeds, capturing public interest as ChatGPT and similar AI advancements take center stage. Biologists are actively investigating novel ways to use AI to utilize the abundance of chatbots to understand the complex operations of our genes. The Power of Artificial Intelligence in Gene Activation Research The University of California,…
A consortium of scientists from across the globe has undertaken a massive computational effort to produce predictive models on the effect of phosphorothioate (PS) stereochemistry on the structure and stability of the PS-DNA.RNA hybrid in the realm of gene silencing. Phosphorothioates have proven significant in oligonucleotide-based cancer and neurological disease therapeutics. PS-oligonucleotides have acquired a…
Mount Sinai researchers have developed a machine learning algorithm that allows healthcare facilities to forecast the chance of death for specific patients undergoing cardiac surgery. The Need for Accurate Mortality Risk Prediction Accurately assessing the mortality risk of patients undergoing cardiac surgery is of utmost importance. It will allow healthcare professionals to make informed decisions,…
Scientists from the Max Planck Institute of Molecular Physiology, Germany, have developed a method, TomoTwin, an open-source general picking model for cryogenic-electron tomograms. The method is based on deep metric learning and incorporates the embedding of tomograms in an information-rich, high-dimensional space that separates macromolecules according to their three-dimensional structures. The localization of particles within…
The University of Queensland researchers have developed a comprehensive plan outlining the integration of smartwatches into Australia’s healthcare system. However, the researchers agree that significant obstacles must be solved before this becomes a reality. A significant change in how we monitor our health and general well-being has resulted from recent technological advancements. The smartwatch is…
Scientists from Seoul National University, South Korea, and the Max Planck Institute for Multidisciplinary Sciences, Germany, have developed a deep learning-based method called Foldseek, which is a highly accelerated and accurate method for searching protein structures. Technological advances in structure prediction methods have yielded millions of protein structures, thus creating a bottleneck for searching operations…
The genetic mechanisms underlying cancer have been the focus of research by scientists and medical professionals worldwide. The human genome has undergone random insertions of small DNA fragments known as “jumping genes” during the history of evolution. Transposable elements, another name for these specific DNA pieces, have been linked to the development and spread of…
Artificial intelligence (AI) and cellular biology have combined to change several sectors, including the mapping and monitoring of cellular activity within the human body, as technology continues to improve at an unprecedented rate. In a quest for a deeper understanding of conditions such as dementia and infectious diseases like COVID-19, scientists at IMB applied artificial…
Neuroscientists from the University of Pittsburgh extend a previously developed hierarchical model of auditory categorization by including several adaptive neural mechanisms that aid auditory categorization. Humans and animals need to generalize over the vast variability of communication sounds for robust auditory perception. The variability arises at both the level of production of sound as well…
The emerging field of biocomputing holds immense promise for transforming both the realms of computing and medicine. With its potential applications ranging from early detection and treatment of diseases to futuristic enhancements of human memory, biocomputing represents a groundbreaking avenue for medical research. However, conventional computer hardware has certain limitations when it comes to interacting…
Scientists from the University of Washington, USA, studied how deep learning boosts and improves de novo protein binder design and devised a protocol for deep learning-aided protein binder design. In recent times, it has been plausible to design novel high-affinity protein-binding proteins using the structural information of the target alone. However, given the low success…
Recent achievements in the field of artificial intelligence (AI) have helped to develop effective medical imaging frameworks that can achieve professional clinical expertise. However, they often generated inadequate outputs while assessing clinical data, which was different from the training data. Google researchers and collaborators at the Georgia Institute of Technology and Northwestern University have proposed a new…
Machine learning-based methods have played a significant role in detecting RNA modifications. The advent of new high-throughput experimental and computational techniques has propelled the discovery of RNA modifications. The epitranscriptome refers to the biochemical modifications of the RNA (transcriptome) within a cell. Such modifications significantly impact several biological processes. The authors of the paper published…
The primary objective of modern proteomics studies is to analyze and isolate the proteome of individual cells from a pool of similar cells. Existing techniques like RNA sequencing, microarrays, quantitative PCR, and reverse transcription-PCR (qRT-PCR) are designed to analyze RNA in a population of cells in general. These techniques fail to quantify phenotypically different individual…
Scientists from Amsterdam, Netherlands, have developed an automated and open-source workflow for the design of 3D fragment libraries in the KNIME software. Fragment-based drug discovery (FBDD) has proven successful in producing a growing list of clinical candidates, with six approved drugs as of 2022. Fragments, or smaller molecules, are used as screening compounds as opposed…
Recent developments in comparative genomics studies, along with the availability of cutting-edge computational tools, have revived the study of evolutionary biology and genetics in a new way. Existing computational tools can efficiently interpret orthologous clusters of genes and determine phylogenetic relationships among species. The recently upgraded release of the Web-based program OrthoVenn, called OrthoVenn3, with…
Scientists from the University of Toronto, Canada, have developed a foundation model for single-cell analysis, scGPT, by generative pre-training on over ten million cells. The generative pre-trained transformer for single-cell analysis is capable of capturing meaningful and crucial biological insights at the gene and cellular levels. The foundation model can be finetuned for numerous downstream…
Using the heart as an investigational model, scientists at the Broad Institute of MIT and Harvard have designed an autoencoder-based machine-learning pipeline that can effectively predict a patient’s heart condition based on image data from ECGs and MRIs. The approach could also be used to detect markers related to cardiovascular diseases. Nearly all areas of…
Scientists from the University of Saskatchewan, Canada, have performed a benchmarking analysis of the self-supervised contrastive learning methods used in image-based plant phenotyping. Plant phenotyping, traditionally performed manually, is now being handed over to deep learning-based methods. However, there is still a scarcity of methods for plant phenotyping tasks such as detection and counting. The…
The molecular surface of a protein is represented in the form of geometric and chemical attributes that create the unique fingerprint for identifying protein interaction. Geometric deep-learning-based algorithms can be used to analyze these fingerprints, which can help researchers design new computational tools to study Protein interactions. Inferring protein-protein interactions (PPIs) solely from their structural…
Scientists from the University of Southern California, USA, shed light on the computational approaches that are transforming the drug discovery process from computer-aided to computer-driven. The flood of data about ligand properties and binding to therapeutic targets, coupled with their 3D structures, has revolutionized the drug discovery methodology. The advancement of computing capacities and the…
Advancements in fluorescence microscopy-based imaging techniques have made it possible for researchers to view and analyze mitochondrial networks in 4D. Researchers at UCSD have released a new software called MitoTNT that utilizes fluorescence microscopy data to track mitochondrial time-varying networks. The efficiency of mitochondrial network tracking depends on the quality of the image generated by…
Scientists from Columbia University, New York, have reported the possibility of automated cell type annotation in single-cell RNA seq (scRNA-seq) analysis using GPT-4, a chatbot. While cell type annotation is a significant step in the pipeline of sc-RNAseq analysis, it is often time-consuming and requires manual annotation using canonical marker genes. Automated cell-type annotation typically…
Protein identification has gained much importance over the last few years. In the shotgun proteomics-based approach, peptide fragments are used to identify proteins. The quantification of proteins from identified peptides is a difficult task. Scientists have suggested ProInfer, a new machine learning-based approach for performing Protein Inference based on biological networks. Mass Spectrometry (MS)-based Proteomics…
There is a growing number of incidences of colorectal cancer in people under the age of 50 (EOCRC), but the intrinsic molecular mechanisms underpinning it are not very well understood. A recent study was conducted on 79 CRC patients of Chinese ancestry to understand the differences in the omics of EOCRC compared to general CRC….
Advancement in the field of genome sequencing has seen an increase in the amount of sequence data. In spite of that knowledge regarding the properties of protein still needs to be interpreted. Machine learning methods, particularly deep learning methods, are used to train language models to predict protein properties. A recent addition to the existing…
Scientists from the University of Cambridge present Multifideity PubChem Bioassay, MF-PCBA, a collection of 60 molecular datasets with multiple modalities, reflecting the real-world nature of HTS (high-throughput screening) projects in drug discovery. MF-PCBA is a curated collection of datasets with two data modalities for each dataset, viz., primary and confirmatory screening, termed multi-fidelity. Previously, the…
The human microbiome is an essential aspect of the human body and could be responsible for several health conditions. Yet, much of microbiome transmission within and across populations and how that shapes an individual’s microbiome’s genetic makeup.is still to be understood. A recent paper in nature explores the transmission patterns of the gut and oral…
Scientists from Denmark and Pennsylvania have developed a machine learning-based platform, RAVEN, that is able to design T-cell vaccines for COVID-19. These vaccines are expected to last longer than the currently available ones, thereby providing immunity against future variants of concern (VOCs). The in-silico approach for locating and selecting relevant immunogenic T-cell regions in the SARS-Cov-2 genome…
The RNA-Seq (RNA sequencing) program at The Hospital for Sick Children (SickKids) is focused on developing new tools for Pediatric Rare Disease research. Their clinically approved RNA sequencing platform combines RNA and Genome sequencing methods to create personalized medicine. The study also reviewed the applicability of Trio RNA sequencing methods in identifying mutations causing inherited diseases in…
Data-driven drug discovery provides an effective path for drug development using large bioassay datasets containing the bioactivity profiles of millions of compounds. Recently, researchers have developed BEAR (Bioactive compound Enrichment by Assay Repositioning), a novel method of finding potential drug candidates by exploiting those bioassay datasets. BEAR can discover a diverse range of potential dr…
Google scientists have developed a novel aging clock based on retinal images using deep learning techniques called “eyeAge.” The biological clock is often said to vary from chronological age for an individual and has been previously studied extensively using predictive aging clocks based on biomarkers such as DNA methylation. These often require blood samples and…
Computer programming is crucial for life scientists, as it enables them to perform many essential research tasks. However, learning to code can be challenging for life science students and researchers. Recently, a study evaluated the effectiveness of one large language model (LLM), ChatGPT, in completing basic-to-moderate-level programming exercises from an introductory bioinformatics course. The results…
Google has made significant advancements in the field of AI-based medical technology with the launch of Med-PaLM 2, a limited-access tool that utilizes the medical language learning model (LLM). Med-PaLM 2 is designed to accurately answer medical questions, marking a major breakthrough in the field. Google also launched its Cloud-Based Claims Acceleration Suite to speed…
The rapid influx of large genomic data during the recent pandemic caused by COVID-19 constrained the researcher’s ability to analyze vast microbial genomes at once due to the lack of faster and more powerful tools. Researchers at EMBL-EBI have recently come forward with a robust bioinformatics tool MAPLE which efficiently reduces the size of bulky genomic data….
Scientists from the Broad Institute of MIT and Harvard have developed TEMPOmap, a method that unravels subcellular RNA profiles spatiotemporally at the single-cell level. The method incorporates pulse-chase metabolic labeling and multiplexed-three dimensional in situ sequencing for simultaneous profiling of the age and location of individual RNAs. The authors discovered kinetic gene clusters while performing RNA kinetic…
As researchers gather increasingly complex and extensive data, it is also becoming increasingly difficult to accurately and efficiently process and analyze everything. Julia, a relatively new programming language, presents unique benefits and features, making it an ideal solution for this challenge. Its three key features make it stand out from other existing languages, i.e., speed,…
Shine is a computational approach for extracting distinct, well-conserved biomarkers from a large dataset of the microbial genome. The method has been clinically tested and showed promising results in detecting all pathogenic microorganisms with lower false positives or false negatives rates. Shine utilizes a two-directional approach to identify consensus sequences by identifying distinct regions or…
Scientists from the Max Planck Institute for the Science of Light, Germany, have developed a novel methodology for rapid pathological analysis of cells from cancerous tissue samples using artificial intelligence and a single-cell approach. This methodology is capable of generating tumor biopsy analysis results in as early as 30 minutes, enabling intra-operative consultation pathology, which…
Bispecific antibodies (BsAbs) are an emerging type of immunotherapy and have great potential to treat various diseases. Recently, researchers have developed a novel single-cell platform that can efficiently discover rare BsAb clones with unique and desirable properties that would otherwise be close to impossible to discover using other existing conventional low-throughput, biased, and trial-and-error methods….
Scientists from UPenn have developed a suite of tools and algorithms for analyzing heterogeneous and large RNAseq datasets for detecting, quantifying, and visualizing the splicing variations therein. The suite of algorithms and tools is present as a part of the package MAJIQ version 2. The former MAJIQ (Modeling Alternative Junction Inclusion Quantification) package was developed…
Next-generation sequencing (NGS) is becoming increasingly accessible and affordable, illustrating its growing importance and adoption in the field of clinical and biomedical genetics. To keep up with this growth, King’s College London researchers have developed DNAscan2, a novel, user-friendly, end-to-end pipeline for analyzing NGS data. The software’s versatility, scalability, and the fact that it is…
Recently, a paper published on Cell reports the EN-TEx, a system/project that challenges conventional functional genomics research by providing a comprehensive resource that goes beyond the use of a single haploid reference genome. The resource contains information on personal epigenomes and associated models that could be combined with other genome annotation resources. By utilizing the…
| Cookie | Duration | Description |
|---|---|---|
| cookielawinfo-checkbox-analytics | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics". |
| cookielawinfo-checkbox-functional | 11 months | The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". |
| cookielawinfo-checkbox-necessary | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary". |
| cookielawinfo-checkbox-others | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other. |
| cookielawinfo-checkbox-performance | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance". |
| viewed_cookie_policy | 11 months | The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data. |
