How artificial intelligence is changing biology and medicine, from protein structure prediction to AI-driven drug discovery and genomics.
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…
Research presented at the 2022 World Cancer Congress shows that deep learning algorithm outperformed radiologists in detecting head and neck cancer spread. The algorithm was able to detect small areas of cancer spread that were not detected by the radiologists. This is a potentially important finding, as early detection of cancer spread can lead to…
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…
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 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…
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…
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…
Throughout the years, dissecting gene regulation has been a major challenge for researchers. Using high-throughput sequencing, understanding chromatin landscapes and conformations has set a new era in genomic medicine. Researchers from Shanghai, China, have presented DLoopCaller – a deep learning approach to predict chromatin architecture to identify gene regulation down to transcription factor. Chromatin is…
Deep learning extensively affects studying genetics and the breakthrough research associated with them. The study focuses on understanding the building of complex models rather than complex datasets using model interpretations in Explainable Artificial Intelligence (xAI). This helps in understanding the utility of complex models to decipher biological datasets. Artificial intelligence-based deep learning models are known…
Researchers from the MIT-IBM Watson AI lab have developed a new machine learning-based model that recognizes ALS disease progression. The research will help design future clinical trials and discover mechanisms behind the disease. The study also highlights that the model can be extrapolated to other neurodegenerative disorders, like Alzheimer’s and Parkinson’s, to trace disease patterns. Neurodegenerative diseases…
A new powerful machine learning tool, ‘SigProfilerExtractor‘ has identified a link between bladder cancer and tobacco smoking. The study was led by researchers at the University of California San Diego School of Medicine and published in the journal Cell Genomics. The study may help researchers determine what environmental factors could cause cancer in certain patients, such…
The researchers from the University of Central Florida identified promising drug candidates using an AI-based tool, “AttentionSiteDTI,” that uses natural language to model drug-target protein interactions. Drug discovery has become a major bottleneck in modern medicine. Researchers have developed several methods to speed up the process in recent years, such as high-throughput screening (HTS). HTS…
Predicting protein structure has been revolutionized by machine learning over the past two years. A similar breakthrough in protein design has now been reported in the journal Science. The researchers from the Baker lab at the University of Washington demonstrate that machine learning makes the design of proteins much more accurate and faster with a…
The new tool “CheXzero” improves clinical AI design by overcoming the major hurdle of labeling datasets for model training. Scientists at Harvard Medical School and Stanford University have developed an artificial intelligence diagnostic tool that can detect diseases directly from natural language descriptions of chest X-rays. This is a major step forward in clinical AI…
A challenge to tuberculosis treatment regimen design is the need to combine three or more antibiotics. The study shows how machine learning can provide a data-driven perspective on this challenge, so researchers can consider new drug combinations to provide better treatments. The researchers believe this new approach will increase scientists’ ability to determine which drug…
Novo Nordisk, a leading global healthcare company headquartered in Denmark, has entered into a new strategic collaboration with Microsoft Corp. to accelerate drug discovery and development using big data and artificial intelligence. A new strategic collaboration between the two companies aims to combine Microsoft’s capabilities in data science, cloud computing, and artificial intelligence (AI) with…
It may be possible for clinicians to use artificial intelligence to identify patients who are most prone to experiencing harmful side effects when taking some common antidepressants, antihistamines, and bladder medications. In a study published in Age and Ageing, researchers from the Universities of Exeter and Kent and Medway NHS and Social Care Partnership Trust…
Scientists have developed a new CT technique using artificial intelligence (AI) to spot multiple myeloma-related bone diseases, using lower radiation doses than conventional CT, according to a study published in Radiology, a journal of the Radiological Society of North America. After nearly two decades of development, the technology was used in clinical practice in 2021….
A deep learning model based on voice data from crowd-sourced audio samples detects COVID-19 cases by analyzing subtle vocal changes. At an international congress of the European Respiratory Society in Barcelona, Spain, held from 4th–6th September, researchers presented a study that uses deep learning to identify COVID-19 patients using voice data. The research findings were…
Cancer is a disease that affects millions of people worldwide. According to the World Health Organization (WHO), cancer is the second leading cause of death globally, after cardiovascular diseases. In 2018, about 9.6 million deaths were reported due to cancer. The WHO estimates that over 1.8 million people died from cancer in 2017. The number…
Nearly half of the cases of the most common cancer worldwide, lung cancer, are treated with radiation therapy (RT). RT planning is a time-consuming, resource-intensive process that can take days to weeks to complete, and even highly trained doctors disagree on how much tissue to target with radiation. Furthermore, as cancer rates rise, a global…
Scientists from MIT developed a device resembling a Wi-Fi router that employs a neural network to detect the presence and severity of the most rapidly growing neurological disease in the world, Parkinson’s disease. Parkinson’s disease (PD) affects more than 1 million Americans as of 2020, causing $52 billion in economic costs each year. Drugs have…
A study at Rutgers University of dozens of artificial intelligence (AI) software programs used in precision medicine to prevent, diagnose, and treat disease discovered that there is not a single program that can be applied to all the treatments. The Rutgers study is among the first to investigate competing AI algorithms and software in genomics,…
Scientists from The City University of New York present a protocol for collecting and analyzing data for KBG syndrome that emphasizes the use of videoconference and artificial intelligence (AI). Based on this information, the authors make recommendations for diagnostic and treatment approaches for KBG syndrome. The study reports 25 previously unpublished individuals from 22 families…
Scientists present an interpretable, loosely guided, multimodal deep learning strategy for cancer prognosis, which also investigates the use of multimodal interpretability to describe how WSI and molecular characteristics affect risk. The objective prognostic models that can be created from histology pictures have shown promise in the quickly evolving field of computational pathology. Most prognostic models,…
Researchers at Meta AI developed an artificial intelligence (AI) model that can precisely predict entire atomic protein structures from a single sequence of a protein. The use of language model representations to replace the requirement for explicit homologous sequences distinguishes ESMFold from AlphaFold2. As a result of this simplification, ESMFold is significantly faster than the…
Kyoto University scientists have discovered that they can decode from brain activity the subjects’ capacities to forecast their placements and scenes within the labyrinth, as well as the degree of confidence in their predictions, using partial-observation mazes in virtual reality (VR). Imagine a future in which a disaster completely destroys someone’s hometown. How quickly could…
Scientists from University College London demonstrate the use of various cutting-edge artificial neural networks to analyze images of bacterial microscopy. The approach will help future researchers to explore the prospective deep learning (DL) applications and employ pre-trained models in their research on a designated platform. Microbiology, the study of microorganisms and their community, is a…
The AlphaFold Protein Structure Database, created by DeepMind and EMBL’s European Bioinformatics Institute (EMBL-EBI), makes AI-enabled predictions of the 3D structures of nearly all cataloged proteins available to the scientific community for free and without restrictions. It has been a year since AlphaFold was made available for free worldwide distribution and AlphaFold Protein Structure Database…
Scientists unleash the potential of digital pathology data by training computer-aided diagnosis models and making use of convolutional neural networks without human annotations. The cancer diagnostics model developed in the study for empowering digital pathology limits the human effort to a minimum. Deep learning algorithms’ improved performance and the digitization of healthcare operations are opening…
In order to better prevent and cure debilitating ailments including Alzheimer’s disease, schizophrenia, and autism, new research at Georgia State University’s TReNDS Center may result in early detection. For a recent study published in Nature Scientific Reports, a team of seven scientists from Georgia State University created a sophisticated computer program to sift through enormous…
The new AI-based technology developed by the scientists at Johns Hopkins University recognizes patients susceptible to sepsis, which is otherwise notoriously hard to detect and progresses rapidly. Early diagnosis and treatment of sepsis are associated with better patient outcomes. Early warning systems based on machine learning may shorten the diagnosis time, but only a few…
Like our brains, a new computer program created by Parham Aarabi of the University of Toronto can store and retrieve information strategically. An experimental tool that uses the novel algorithm to aid those with memory loss has also been developed by the associate professor in the Faculty of Applied Science & Engineering’s Edward S. Rogers…
MIT researchers have developed a geometric deep-learning model, EquiBind, which is 1,200 times faster than one of the fastest computational molecular docking models currently in use, QuickVina2-W, for effective binding of drug-like substances to proteins. One of the main challenges in drug discovery is to predict the binding properties of a drug-like chemical to a…
Scientists use PandaOmics, an AI-driven target identification tool, to examine the expression profiles of direct iPSC-derived motor neurons from Answer Amyotrophic Lateral Sclerosis (ALS) and central nervous system samples from public datasets. The discovery of novel therapy regimens is urgently needed for the severe neurological disease amyotrophic lateral sclerosis, which has unclear pathophysiology. Here, the…
A research team led by Professor Aydogan Ozcan from the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA) and colleagues have developed an artificial intelligence (AI) powered innovative bacterial colony detection system using a thin-film transistor (TFT) array, which is a widely used technology in mobile phones and other displays….
Scientists from the National University of Singapore and DSO National Laboratories, Singapore develop IDentif.AI-x, a pandemic readiness platform powered by artificial intelligence for the rapid prioritization of COVID-19 optimized combination therapy regimens. By combining a prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus and Vero E6 assay with a quadratic optimization workflow,…
Scientists at Cardiff University have developed a state-of-the-art artificial intelligence (AI) system, ‘TranSalNet,’ which precisely anticipates the regions of an image where a person is most likely to gaze. The researchers claim that the unique method more accurately simulates human vision than any previous system based on human brain mechanics and its ability to discriminate…
Novel discovery by scientists from UT Southwestern University identifies co-component signal transduction systems in gut bacteria that could help fight gastrointestinal infections. Scientists used artificial intelligence to identify a new family of enteric bacteria sensing genes that are related by structure and likely function but not genetic sequence. The research, which was published in PNAS,…
Scientists from the Korea Advanced Institute of Science and Technology propose the PICASSO technique, which enables more than 15-color imaging of spatially overlapping proteins in a single imaging round without using any reference emission spectra. For a wide spectrum of researchers, the PICASSO technique offers a multiplexed imaging tool with great accessibility and accuracy. According…
The algorithms that track users’ Netflix viewing habits could soon be used to track cancer development in order to direct doctors in managing cancer. An international team of researchers led by Dr. Nischalan Pillay (UCL Cancer Institute) and Dr. Ludmil Alexandrov (University of California, San Diego) used AI to identify 21 frequent faults in the…
Scientists used hematoxylin and eosin (H&E) images with a deep learning approach to predict disease outcomes, an emerging technique for the improvement of clinical trials via computational methodology. For treatment choices and disease outcome monitoring, it is highly clinically relevant to understand the variables that affect a cancer patient’s prognosis. The use of whole slide images…
Scientists from Akademi University, Turku, Finland, and Pasteur Institute, Paris, France have developed a new software TrackMate v7 that may allow tracking things in photos. The technology, which is based on artificial intelligence, will serve as a catalyst for future discoveries in the biological sciences. Object tracking, or following objects across time, is a critical…
Scientists have developed DeepCycle, a deep learning approach combined with RNA velocity to reveal cell cycle gene regulation. This methodology is particularly appropriate to study the interplay of the cell cycle with pluripotency and cell reprogramming. The method could lead to the discovery of cell-cycle dysregulated mechanisms in malignancies, as well as prospective therapeutic targets. Even…
The advent of technological advances such as artificial intelligence (AI), machine learning (ML), automation, robotics, etc., has accelerated the pace of drug discovery as well as the constancy maintained by the automated procedure reduces human errors and provides improved data quality. The goal of drug discovery and development is to bring new medicines to patients…
Scientists from Koa Health and Birmingham and Solihull Mental Health NHS Foundation Trust developed a machine learning model that uses electronic health records to detect the risk of a mental health crisis in patients over 28 days. The machine-learning algorithm continuously monitors patients for the possibility of a mental health crisis using anonymized electronic health…
CancerVar leverages machine learning frameworks to evaluate the potential relevance of somatic cancer mutations in terms of cancer diagnosis, prognosis, and targetability, in addition to simply discovering them. Researchers at Children’s Hospital of Philadelphia (CHOP) developed CancerVar, a novel machine learning empowered tool to interpret somatic variants in cancer. CancerVar can be accessed either as…
A team of scientists and engineers at the University of Texas at Austin created an enzyme that could break down environmentally damaging polymers in hours to days instead of centuries. The breakthrough discovery published in Nature could help to tackle one of the world’s most important environmental issues: how to deal with the billions of…
A deep learning-based method for predicting the biophysical and biochemical features of protein-ligand complexes developed by researchers could help discover new drug candidates. Scientists formulate novel parallel graph neural networks for decoding protein-ligand interactions. This methodology can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of…
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