Saturday, February 4, 2023

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Machine Learning in Oncology: A Tabular Machine Learning Approach to Pan-cancer Tumor-only Variant Calling

Accurately identifying somatic mutations and differentiating them from germline variations is critical for precision oncology and precise tumor-mutational burden (TMB) computation. For this purpose,...

AI Proteins: Unlocking the Potential of Protein Generation from Scratch with Natural Language Model “ProGen”

Scientists from the University of California, San Francisco, have created an inventive AI system that can create entirely new enzymes from scratch. Even though...

Luminary for Lipidomics Research Applications: Guiding the Choice of Informatics Software and Tools

There has been an increase in research in numerous areas of biology and medicine due to the development of mass spectrometry lipidomics. As a...

De Novo Drug Design Using Generative Deep Learning: Implementing Chemical Language Models with Molecular Structures and Bioactivity

Chemical language models (CLMs) can be used to construct molecules with desired features. Textual representations of novel chemical structures, such as simplified molecular input...

ML meets Structural Bioinformatics: Novel Machine Learning Approaches Transforming Protein Research Landscape

Scientists from University College London, UK, study the Machine Learning (ML) approaches that have successfully closed the ever-widening gap between large amounts of protein...

Discovering the Secrets of Drug-Targeted Genome Interactions with a New DNA Sequencing Method “Chem-map”

Scientists from the University of Cambridge have introduced a cutting-edge DNA sequencing method that can precisely pinpoint the location and mechanism of small molecule...

Aston University Paves the Way for New Understanding of Viruses with Complete Reconstruction

Aston University scientists have made a groundbreaking discovery in the field of virology. They were able to create a 3D model of the virus...

Meet “Espresso”: The Solution for Accurate Quantification of Transcript Isoforms in Error-Prone RNA-Seq Data

The Children's Hospital of Philadelphia (CHOP) scientists have created ESPRESSO, a new computational tool for analyzing RNA-seq data. ESPRESSO aims to improve the detection...

Revolutionizing Healthcare: Federated Learning Deployed Across Hospitals to Train Deep Learning Models Securely

Owkin, an AI biotech company, has successfully used federated learning (FL) for the first time to train deep learning models on histopathology data from...

Deep Learning and Radiomics: A Game-changer for Identifying Glioblastoma and Brain Metastases

According to a recent study from Karl Landsteiner University of Health Sciences (KL Krems), using radiomics and deep learning algorithms can quickly and accurately...

ImageMol – A Powerful Self-supervised Image Representation Learning Framework for Accurate Prediction of Molecular Properties and Drug Targets

Scientists from Case Western Reserve University, Cleveland, USA, developed a self-supervised image representation learning framework, "ImageMol," for predicting molecular properties and drug targets with...

BioNTech’s Acquisition of InstaDeep Set to Revolutionize AI-Powered Drug Discovery, Design, and Development

BioNTech, a leading biopharmaceutical company, has announced plans to fully acquire InstaDeep, an artificial intelligence company specializing in drug discovery, design, and development. This...

Discovering the Secrets of the Human Kinome: An Atlas of Substrate Specificities for Serine/Threonine Kinases

Researchers from MIT have created a comprehensive map of over 300 protein kinases and their targets, with the goal of potentially discovering new cancer...

Uncovering Lung Cancer’s Immune Evasion Tactics: Groundbreaking Study Sheds Light on a Key Mechanism

Lung cancer is one of the most common and deadly types of cancer worldwide, and the ability of cancer cells to evade the body's immune system poses a significant challenge in treating it. A team of Weill Cornell Medicine scientists recently made a significant breakthrough in understanding this process. They discovered a genetic signature that can predict patient survival and identified a key mechanism lung cancer cells use to avoid immune attacks. This groundbreaking discovery could hasten the development of treatments that can circumvent tumor defense mechanisms, increasing the chances of survival for lung cancer patients. The signaling pathway involving IRE1 and XBP1 is becoming increasingly recognized as a key player in cancer progression and immune system suppression in various...

Meet SPICEMIX: A Machine Learning Approach for Improved Cell Identity Understanding

Scientists from Carnegie Mellon University developed SPICEMIX, a machine learning method that allows researchers to better understand the contribution of various spatial patterns to...

Digital Twins Unlock the Key to New Biomarker Discovery for Atherosclerosis

A recent study utilized digital twin technology to identify new markers for atherosclerotic coronary artery disease (ASCAD). The study was conducted by G3 Therapeutics,...

Predicting Drug Success: The Role of Computer Models in Determining Drug’s Binding Ability to Proteins

When it comes to developing new drugs, one of the most important considerations is how well a candidate drug will bind to its target...

Revolutionizing Cancer Detection: A New Database Maps Proteins that could Predict Cancer Types

The ability to predict cancer has advanced with the recent release of a new database that includes information on cancer proteins and was developed...

Speeding Up Drug Formulation Development: Harnessing the Power of Machine Learning

Scientists at the University of Toronto advanced drug formulation development by utilizing machine learning techniques to predict and guide the design of long-acting injectable...

CHESSBOARD: A Bayesian Approach to Identifying Cancer Subtypes through Unsupervised Detection of RNA Splicing

Finding cancer subtypes is a crucial first step in creating individualized care. In particular, RNA splicing change-based sub-typing has been encouraged by a number...

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Computational and experimental pipeline of RESP

Expediating the Discovery of Super Tight-Binding Antibodies with an AI Model “RESP”

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Scientists from UCSD have devised an efficient AI-based pipeline, RESP, for identifying high-affinity antibodies. Typically, researchers would employ directed evolution to enable iterative mutagenesis...
AlphaFill: An AI Algorithm that Implants Ligands and Cofactors in AlphaFold Models

Meet AlphaFill: An AI Algorithm to Fill Missing Ligands and Cofactors in AlphaFold Models

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According to Levinthal's paradox, each protein may adopt around 10300 distinct structures. We now know 3-D structures for about 98% of the human proteome...
DGNN-DDI: A Cutting-edge Tool for Drug-Drug Interaction Prediction using Dual Graph Neural Network

DGNN-DDI: A Cutting-edge Tool for Drug-Drug Interaction Prediction using Dual Graph Neural Network

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Scientists from Shaanxi Normal University, China, developed a novel Drug-Drug-Interactions (DDI) prediction tool that implements a dual graph neural network architecture to incorporate chemical...
Connection Between Immune Response & Persistent Smell Loss Post-COVID-19

New Study Finds Connection Between Immune Response & Persistent Smell Loss Post-COVID-19

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Most SARS-CoV-2 patients experience anosmia, or a loss of smell, which can linger for months after recovery. According to olfactory epithelium samples taken from...
ZFDesign: A Universal Deep Learning Model for Zinc Finger Design Enabling Transcription Factor Reprogramming

Meet ZFDesign: A Universal Deep Learning Model for Zinc Finger Design Enabling Transcription Factor...

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Controlling gene expression by engineering Cys2His2 zinc finger (ZF) domains to bind particular target sequences in the genome has many therapeutic applications. However, due...