Neural Networks

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GeneFormer: A Transformer-based Approach for Efficient Gene Data Compression
ByAnchal Negi June 1, 2024October 22, 2026

In a recent study, scientists from Tsinghua University, China, introduced GeneFormer, a unique system for compressing gene sequences. The technique uses transformers to make compression of DNA sequences more efficient than any other previous methods, which brings about smaller files and faster processing times, thus making it possible to handle this ever-increasing pile of genetic…

Read More GeneFormer: A Transformer-based Approach for Efficient Gene Data Compression
AI | Bioinformatics | Drug Discovery | Neural Network | News | Paper Summary | Structural Biology | Tools | Trending

Accelerating Drug Discovery with QComp’s Innovative QSAR-based Data Completion Framework
ByAnshika Gupta May 29, 2024October 22, 2026

In drug discovery, the accurate prediction of chemical activities is paramount. The conventional forms of QSAR that were initially developed and widely used are the traditional or classical approach of QSAR modeling. However, there is a fundamentally new approach that redefines the future of data completion in drug discovery, called QSAR-Complete (QComp), developed by the…

Read More Accelerating Drug Discovery with QComp’s Innovative QSAR-based Data Completion Framework
AI | Bioinformatics | Deep Learning | Drug Discovery | Neural Network | News | Paper Summary | Structural Biology | Tools | Trending

The False Positive Problem in Drug Discovery: Can ChemFH Be the Solution?
ByCBIRT May 28, 2024October 22, 2026

In the drug discovery process, high-throughput screening quickly evaluates a wide range of chemical compounds to find hit compounds for certain biological targets. False-positive results, on the other hand, cause delays in hit compound screening and waste money. In order to address this, scientists from Central South University, China, and collaborators have developed ChemFH, an…

Read More The False Positive Problem in Drug Discovery: Can ChemFH Be the Solution?
AI | Bioengineering | Bioinformatics | Computational Models | News | Paper Summary | Protein Design | Trending

Breakthrough in Protein Design: MIT Researchers Develop Computational Approach for Easier Protein Engineering
ByDeotima Chakraborty April 7, 2024October 22, 2026

Modeling the combinatorially huge number of sequences is not practical since previous approaches frequently restrict optimization to a narrow mutational radius, restricting the design space for novel proteins with improved fitness for a desired attribute. Researchers from the Massachusetts Institute of Technology suggest flattening the fitness landscape in place of heuristics to make protein optimization…

Read More Breakthrough in Protein Design: MIT Researchers Develop Computational Approach for Easier Protein Engineering
AI | Bioinformatics | Neural Network | News | Paper Summary | RNA-Seq | Transcriptomics | Trending

Meet NeuroVelo: A New Tool to Decode Cellular Dynamics from Single-cell Transcriptomic Data
ByDeotima Chakraborty November 25, 2023November 25, 2023

A study conducted at the International School for Advanced Studies (SISSA) in Trieste, Italy, introduced NeuroVelo, a novel method tackling the challenge of reconstructing temporal cellular dynamics from static single-cell transcriptomics. Unlike existing methods, NeuroVelo combines optimal linear projection learning with a non-linear low-dimensional dynamical system, allowing for better interpretation of results and improved predictive…

Read More Meet NeuroVelo: A New Tool to Decode Cellular Dynamics from Single-cell Transcriptomic Data
Bioinformatics | Cancer Research | Deep Learning | Neural Network | News | Trending

Deep Learning Technology Developed by SNU Researchers Helps Predict Cancer Patient’s Survival Rate
ByDr. Tamanna Anwar August 19, 2022August 22, 2022

A research team at Seoul National University (SNU) developed deep learning technology that can more accurately predict a cancer patient’s survival rate. According to the research published today in the journal Nature Biomedical Engineering, computational pathology methods for analyzing whole-slide images (WSIs) typically do not take histopathological features from the tumor microenvironment into account. In…

Read More Deep Learning Technology Developed by SNU Researchers Helps Predict Cancer Patient’s Survival Rate
Bioinformatics | Deep Learning | Neural Network | News | Paper Summary | Tools | Trending

‘DeepRank’ – A Deep Learning Framework for Data Mining Protein-Protein Interfaces Using 3D Convolutional Neural Networks
ByTanveen Kaur May 16, 2022November 9, 2022

Scientists formulate a deep learning framework, DeepRank, for the data mining of 3D protein-protein interfaces. They demonstrated the efficiency and readiness of DeepRank on two unique difficulties in structural biology. 3D structures of protein complexes give essential data to decipher biological processes at the molecular scale. The huge measure of experimentally and computationally resolved protein-protein…

Read More ‘DeepRank’ – A Deep Learning Framework for Data Mining Protein-Protein Interfaces Using 3D Convolutional Neural Networks
AI | Bioinformatics | Computational Models | Covid-19 News | Deep Learning | Machine Learning | Neural Network | News | Paper Summary | Structural Biology | Trending

A Novel Parallel Graph Neural Networks Based Method for Decoding Protein-Ligand Interactions
ByTanveen Kaur May 14, 2022November 9, 2022

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…

Read More A Novel Parallel Graph Neural Networks Based Method for Decoding Protein-Ligand Interactions

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