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...

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....

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...

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...

The molecular surface of a protein is represented in the form of geometric and chemical attributes that create the unique fingerprint for identifying protein...

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...

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...

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...

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...

There is a growing number of incidences of colorectal cancer in people under the age of 50 (EOCRC), but the intrinsic molecular mechanisms underpinning...

Advancement in the field of genome sequencing has seen an increase in the amount of sequence data. In spite of that knowledge regarding the...

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...

The human microbiome is an essential aspect of the human body and could be responsible for several health conditions. Yet, much of microbiome transmission...

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...

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...

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...

Google scientists have developed a novel aging clock based on retinal images using deep learning techniques called "eyeAge." The biological clock is often said...

Computer programming is crucial for life scientists, as it enables them to perform many essential research tasks. However, learning to code can be challenging...

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...

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...

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....

As researchers gather increasingly complex and extensive data, it is also becoming increasingly difficult to accurately and efficiently process and analyze everything. Julia, a...

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...

Scientists from the Max Planck Institute for the Science of Light, Germany, have developed a novel methodology for rapid pathological analysis of cells from...

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...

Scientists from UPenn have developed a suite of tools and algorithms for analyzing heterogeneous and large RNAseq datasets for detecting, quantifying, and visualizing the...

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...

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...

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....

Neuropsychiatric disorders are a serious public health concern, given that they are the leading cause of disability and account for millions of deaths worldwide....

Researchers at the Abdul Jameel Clinic for Machine Learning in Health, MIT, have developed a novel method, DiffDock, an accelerated drug discovery pipeline using...

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,...

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,...

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...

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...

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...

Conventionally, cancer diagnostic procedures have been known for their invasive nature and enormous cost. However, researchers from KAIST and Korea University have developed a...

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...

Cancer therapies often become ineffective due to the development of resistance in the tumor cells against the treatment. One potential strategy to overcome this...

Scientists from the University of Tübingen, Germany, have developed a novel workflow for predicting robust druggable targets against emerging viral infections such as the...

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...

The discovery of cryptic pockets has widened the scope of drug development by permitting the targeting of proteins previously deemed undruggable due to the...

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...

Healthcare providers have been using routine hospital tests to evaluate patients' health for decades. Electrocardiogram (ECG) tests are frequently a part of these tests....

Scientists from The Hospital for Sick Children, Toronto, have developed an atlas for childhood cancer diagnostic classification using machine learning approaches. Pediatric cancers are...

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....

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...

According to a new study presented at the 52nd Annual Meeting & Exhibition of the AADOCR, held in conjunction with the 47th Annual Meeting...

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...

Scientists from Germany have developed SCEMILA, an inherently explainable AI tool for identifying diagnostic cells of acute myeloid leukemia (AML) subtypes. The authors find...