Plain-language summaries of new research papers in bioinformatics, computational biology and life sciences.
The success of machine learning-based approaches for the prediction of protein properties from their amino acid sequences can be attributed to the overall accessibility of genome sequencing data. Several reoccurring difficulties have been noted throughout time while reading submitted manuscripts and released publications, which makes it difficult to comprehend and reproduce certain stated findings. A…
Scientists from the University of Helsinki, Finland, developed a drug repurposing scoring algorithm, DrugRepo, that predicted 6229 new drug-disease relationships across 545 diseases for 516 approved drugs. The effectiveness of the algorithm was validated through evaluation studies that identified ≥0.4 as an appropriate threshold for discovering new candidate drugs. The study is also accompanied by…
Phylogenetic approaches can be used to infer the order in which mutations arise during cancer progression by analyzing patterns of mutation occurrence in a population of cells. By identifying clades of related cells, it is possible to trace the evolutionary history of each cell and determine which mutations occurred first. This information can then be…
Scientists at the Institute of Parasitology and Biomedicine and the University of Granada, Spain, collaborated to develop two pipelines that could automate and optimize metagenomics and metatranscriptomics data analysis. These pipelines could be adapted for 16S, shotgun, and RNA-Seq data. Its performance was validated through three studies by assessing their taxonomy classification ability. When Anton…
TransFlow (Transmission Workflow), a convenient, quick, effective, and thorough WGS-based transmission analysis pipeline, is presented by Chinese researchers. TransFlow integrates a number of cutting-edge technologies to perform transmission analysis on raw sequencing data. This analysis includes quality control, sequence alignment, variant calling, downstream transmission grouping, transmission network reconstruction, and transmission risk factor inference. TransFlow is…
Federated machine learning enables the largest brain tumor study ever conducted without sharing patient data by allowing researchers to securely analyze distributed datasets while maintaining data privacy. The breakthrough research from Penn Medicine and Intel Corporation published in Nature Communications has the potential to revolutionize medical research and improve our understanding of brain tumors. It also provides…
Researchers from MIT CSAIL employed a potent deep-learning model to extract crucial information from electronic health records that might help with personalized medicine. The adoption of electronic health records aims to enhance and simplify healthcare. Beyond the purview of clinical trials, the vast quantity of data in these now-digital records might be utilized to address…
Researchers at University College London discovered multimorbidity and comorbidity illness trends using a dataset spanning 3.8 million patients in England. Patients and clinicians can use the study findings and interactive online tools to prevent and detect comorbid conditions. In contrast, biomedical researchers and pharmaceutical manufacturers can use them to provide lead compounds targeting common or…
Argonne National Laboratory researchers, together with partners from six different universities, NVIDIA, and Cerebras Inc., have collaborated to create GenSLMs, that learn the evolutionary landscape of the SARS-CoV2 genome using large language models (LLMs) resources. The Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research in 2022 has been awarded to GenSLMs. Rapid sequencing…
Fudan University researchers introduced DxFormer, a new automatic diagnostic framework in which each symptom is regarded as a token, formalizing symptom inquiry and disease diagnosis to a language generation model and a sequence classification model, respectively. The decoder encoder structure, an inverted version of Transformer, was utilized to learn the representation of symptoms by jointly…
Scientists from Ludwig Maximilians University Hospital Munich, Germany have developed a new machine-learning method for classifying hard to diagnose sinonasal tumors. The DNA methylation information was utilized to thoroughly examine the molecular patterns of sinonasal undifferentiated carcinomas (SNUCs), which were inaccurately defined and contested in earlier investigations. Sinonasal tumors have a variety of histopathologies and…
Researchers used a multi-OMICS technique and targeted sequencing (TargetSeq) to identify the processes that may govern the progression of brain metastases. Regardless of intracranial location, it was found that the expression of E-cadherin (Ecad) or Nerve Growth Factor Receptor (NGFR) progresses tumors into proliferative and therapy-resistant states. The investigation of MAPK inhibitor-naive and refractory melanoma…
Qualitative data analysis, understanding patterns, finding possible targets/candidates, and intuitively displaying the results are among the crucially difficult aspects of next-generation sequencing experiments. These challenges are even more difficult for researchers who are not accustomed to writing computer code because the bulk of the analysis tools accessible demand programming knowledge. A customizable Python-based analysis program…
A research team led by Professor Matthias Mann from the Max Planck Institute of Biochemistry has developed AlphaPeptDeep. AlphaPeptDeep is a Python framework built on the PyTorch deep learning library that can learn and predict peptide properties. AlphaPeptDeep additionally includes a plug-in capability that allows non-experts to develop models with just a few lines of…
A new deep learning method developed by a team of researchers at Chalmers can generate regulatory DNA sequences that control gene expression in a gene-specific manner. Despite significant sequence divergence from natural DNA, in vivo measurements reveal that 57% of the synthetic sequences with high expression levels outperform the expression levels of the correspondingly highly…
Scientists from Xi’an Children’s Hospital have shown that metagenomic next-generation sequencing (mNGS) of cell-free DNA (cfDNA) and RNA may help detect trace infections from various bodily fluids and blood samples extracted from newborns. Researchers also performed a local polynomial regression fitting analysis, which revealed that the best time for detecting mNGS was 1 to 3…
Researchers at AstraZeneca and Ono Pharmaceuticals sought to create a model that could predict the likelihood that individuals with Type 2 Diabetes Mellitus (T2DM) who had no prior history of CKD or CVDs would develop CKD/HF. Using the prediction model, it would be possible to identify individuals with T2DM who are at a high risk…
Researchers from the University of Antwerp screened 17939 assembled metagenomic samples from 21 different biomes, varying in sequencing quality and depth, over 46 countries, across 6 continents, for a period of 14 years (2005-2019) for clinically crucial antibiotic resistance genes (ARGs) commonly found in human biomes, anthropogenic biomes, and natural biomes such as freshwater, marine,…
Scientists at the Center for Integrative Medical Sciences Yokohama, Japan, designed a platform-independent application to run UniverSC, a wrapper for the 10X Genomics CellRanger program that works with different single-cell technologies, via a graphical user interface, operable on macOS, Windows, and Linux Ubuntu, eliminating data processing for single-cell RNAseq (scRNAseq) analysis for researchers who are naive to…
AlphaFold, a machine learning system developed by Google DeepMind, can predict the three-dimensional structures of proteins with unprecedented accuracy, leading to a better understanding of how these structures relate to function. The researchers from USDA-ARS describe a methodology “Functional Annotations using Sequence and Structure Orthology (FASSO),” which takes advantage of AlphaFold along with sequence and…
In the article released in PLoS Computational Biology, researcher Alfonso Nieto-Castanon of Boston University describes a technique called fc-MVPA (functional connectivity Multivariate Pattern Analysis). The novel technique uses classic MVPA to evaluate each voxel in a three-dimensional space and traces functional connections to the brain. Monte Carlo simulations are also used in the study to…
Cancer is a complicated disease brought on by the interaction of various informational layers. Tumor origin, the appearance of genomic and transcriptomic variations, or interactions with the microenvironment are all factors that contribute to the difficulty of treating tumors. With reference to the types of tumor heterogeneity and the accessible data from next-generation sequencing, the…
Researchers from Penn State University present CLIMB (Composite Likelihood eMpirical Bayes), a statistical tool that discovers patterns of condition specificity in a given genomic data. It also provides a generic framework for a variety of investigations, such as aggregating genomic features with comparable condition-specific patterns. CLIMB outperforms the existing options in statistical precision while capturing…
Recent research conducted by the research group of Savas Tay at the University of Chicago, Illinois, USA, introduced a deep learning platform, OrganoID to analyze and interpret the organoid models for personalized drug screening. The team has interpreted results and validated the platform using pancreatic cancer organoid images, revolutionizing biomedicine. Introduction Organoid research has…
With a thorough potential of the Ribonucleic Acids (RNA) in cellular processes, the molecule is deemed more than just being transitionary and has emerged as a master regulator of cellular processes. The regulatory functions to code proteins and untranslated regions in mRNA significantly contribute towards post-translational modifications. A review published in Nature Reviews Genetics gives biological insights…
A research team led by Yury V. Bukhman from the Morgridge Institute for Research has put forth a reference genome with haplotype-resolved assemblies and gene annotations deciphering the Nile Rat (Arvicanthis niloticus) as a carbohydrate-sensitive model for diabetes research. An animal model is a living, genetically engineered organism used for research and investigating human diseases….
The post-doctoral researchers from Memorial Sloan Kettering Cancer Centre (MSK) developed an upfront and coherent way to study copy number alterations (CNA) deletions in the laboratory using the pancreatic and melanoma cancer mouse models. The CRISPR-based method revealed clinically relevant observations into the multifaceted nature of 9p21.3 ommissions that contribute to up to 15% of…
Teams of researchers across 18 institutes spread over 11 countries have worked together to assess the utility of AlphaFold2 (AF2) predictions in the analysis of distinctive structural elements, the impact of missense variants, the prediction of function and ligand binding sites, the modeling of interactions, and the modeling of experimental structural data. A significant biological…
A large percentage of the global population is shown to have been affected by different types of cancer and increased mortality. Hepatocellular carcinoma, or primary liver cancer is one the life-threatening disease and the fastest-growing cancer type in the United States. A recent study conducted by Hidewaki Nakagawa et al. leveraged the proteomic profiling of…
Deep-SMOLM is a deep-learning-based estimator created by Washington University in St. Louis researchers that blends machine learning, physical laws, and biological principles. At a rate ten times quicker than iterative estimators, Deep-SMOLM correctly and precisely reconstructs 5D information of simulated biological fibers and experimental amyloid fibrils from images, including highly overlapping single molecule structures. A…
A recent study published in ACS Synthetic Biology has presented an analogous level of adaptability to enzymes. A team of researchers from Osaka University has illustrated the hypothesis to identify the amino acid residues actively involved in cofactor specificity by integrating a logistic regression model with an amino acid sequence dataset. The machine learning approach…
A recent study led under the supervision of Cenk Sahinalp et al. and conducted jointly at the National Institute of Health, UCSD, and Indiana University introduced an optimized framework for strain-level microbial identification. The team has provided a comparative approach with reference to other taxonomic classifiers and embedded results with higher sensitivity and specificity. Introduction…
An extensive protein database that reveals the structures of millions of metagenomic proteins has been created by researchers under the direction of Alex Rives from Meta (formerly known as Facebook). With DeepMind’s AlphaFold demystifying 220 million protein structures from DNA databases in early 2022, this groundbreaking study has surpassed these findings by unraveling 617 million…
A team of researchers from the Broad Institute of MIT and Harvard, Brigham and Women’s Hospital, and Harvard Medical School has demonstrated a computational technique called Hi-CNV to identify millions of copy number variants harboring in the human genome and link them to health-related traits such as height, lipid levels, and bone density by imposing…
A team of researchers led by Prof. Jiangning Song from Monash University, Australia, has developed a novel ML ensemble predictor named PreAcrs that can identify anti-CRISPR proteins from a given polypeptide sequence with good accuracy and robustness. Clustered regularly interspaced short palindromic repeats (CRISPR) is a family of DNA sequences, CRISPR-associated protein (Cas) is a family…
Evident with the increasing cost and laborious work of diagnosis of cancer, using the gold standard Immunohistochemical (IHC) staining of tissues. Researchers from the University of California, Los Angeles, described a label-free virtual HER2 IHC staining based on a deep learning method. Introduction Immunohistochemical staining of cancer tissues, such as the HER2 biomarker, is widely…
Cancer is a significant threat worldwide. In a study published in ACS Cancer, authors Bray et al. stated that cancer may overtake cardiovascular disease as the leading cause of premature death worldwide over the course of this century. Cancer biomarkers are the biological molecules produced by the tumor in a person; this helps characterize the…
Researchers from IIIT-Delhi and the University of Queensland have partnered to create Precily, a deep neural network-based modeling approach to predict the responsiveness of tumors to various drugs using gene expression data and anti-cancer drug descriptors. Personalized cancer medicine or precision medicine is the need of the hour for better treatment of highly convoluted diseases…
Sepsis is a life-threatening condition damaging multiple organs at a time due to the host’s aberrant immune responses against infections. The current study supported by the National Heart, Lung, and Blood Institute was conducted at the University of California, San Francisco, USA, to apply an integrated host-microbe metagenomics approach to accurately diagnose potential sepsis in…
A team of researchers led by Thomas Terwilliger from the New Mexico Consortium developed a procedure where the AlphaFold structure prediction models can be automatically rebuilt based on density maps. These models might, therefore, be used as templates for generating new AlphaFold models in the future. Machine learning algorithms are widely implicated in the prediction…
Owing to the rise of global food insecurity, several initiatives have been made to understand the disease-causing plant pathogens and crop failure. Researchers from the Banfield group at the John Innes Centre and collaborators have elucidated how rice blast fungus ZiF (Zinc finger) effectors bind a conserved Exo70 interface to manipulate plant exocytosis. Introduction Throughout…
Owing to the increase in malaria infections and morbidities associated with it, an upgradation of diagnostics gold standard procedures can help elevate the disease burden. MTA-BME Morpho-dynamics Research Group from Budapest has integrated neural networks (NN), reducing the data dimension to address the limitations. High speeding the classification of RBCs (red blood cells) increases the…
Researchers from Trinity College Dublin have introduced a machine-learning model that classifies human macrophages based on cellular autofluorescence imaging of NAD(P)H. They have also shown that this technique is powerful for analyzing macrophage metabolism and classification. The Two-photon fluorescence lifetime imaging microscopy (2P-FLIM) is a non-invasive imaging technique that aids in visualizing cellular metabolism by…
The iconic bacterial genus from sheep rumen ‘Quinella’ has been the center of bacterial taxonomy for various reasons. The research group from Massey University, New Zealand, and the National University of Singapore have deciphered the genomic insights using metagenomic sequencing. The analysis provided observations into taxonomic diversity and assess genetic data for the physiological understanding…
According to the study by Prof. Judy Gichoya of Emory University, the standard deep learning models effectively predicted the patient’s race using only diagnostic images like X-rays, CT scans, and mammograms, even when the images were heavily altered, cropped, or noised. Studies have shown that AI deep learning models mimic the unconscious racial bias and…
Investigators from Brigham and Women’s Hospital developed a novel approach to identify time-frequency peaks from sleep brain waves revealing heterogeneity between both healthy and neurologically diseased subjects. The approach provides fingerprint identification unique to each individual and identifies the potential for neurodegenerative disease phenotyping and biomarker identification. Introduction Traditionally, the identification of brainwaves in the…
A team of computer scientists from the City University of New York has created a novel framework named the context-aware deconfounding autoencoder, which is capable of predicting the drug response elicited in a patient from a neural network model trained using cell-line data, thereby suggesting the model’s potential application in personalized medicine. Omics profiling has…
Researchers from MIT University, Cambridge, have introduced a novel generative model, ‘SQUID’, to facilitate the shape-conditioned generation of chemically diverse molecules for drug design in 3D conformation. The team has successfully introduced equivariant deep working models to challenge current 2D generative models. Introduction Computer-aided drug design, CADD, has been reformed by integrating generative models. CADD…
A brand-new framework called sc-linker has been proposed by researchers from the Broad Institute of MIT and Harvard that connects human illness and phenotypes to different cell types and physiological processes. This is done by integrating Genome-Wide Association Studies summary statistics, epigenomic data, and single-cell RNA (scRNAseq) sequence data from multiple tissues, cells, diseases, and…
Recent advancements in SRT (Spatial Resolved Transcriptomics) technology has allowed in-depth research to measure gene activity in tissue samples and map the activity based on their composition. A collaborative team of researchers from China has introduced a multi-view graph collaborative model, ‘stMVC’ (Spatial Transcriptomics data analysis by Multiple View Collaborative-learning), to analyze SRT data and elucidate tissue heterogeneity to…
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