Controllable protein design with language models
The twenty-first century is presenting humankind with unprecedented environmental and
medical challenges. The ability to design novel proteins tailored for specific purposes would …
medical challenges. The ability to design novel proteins tailored for specific purposes would …
Deep learning and its applications in biomedicine
C Cao, F Liu, H Tan, D Song, W Shu… - Genomics …, 2018 - academic.oup.com
Advances in biological and medical technologies have been providing us explosive
volumes of biological and physiological data, such as medical images …
volumes of biological and physiological data, such as medical images …
A survey of deep neural network architectures and their applications
Since the proposal of a fast learning algorithm for deep belief networks in 2006, the deep
learning techniques have drawn ever-increasing research interests because of their …
learning techniques have drawn ever-increasing research interests because of their …
Applications of deep learning in biomedicine
Increases in throughput and installed base of biomedical research equipment led to a
massive accumulation of-omics data known to be highly variable, high-dimensional, and …
massive accumulation of-omics data known to be highly variable, high-dimensional, and …
Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data
Deep learning is rapidly advancing many areas of science and technology with multiple
success stories in image, text, voice and video recognition, robotics, and autonomous …
success stories in image, text, voice and video recognition, robotics, and autonomous …
RaptorX-Property: a web server for protein structure property prediction
Abstract RaptorX Property (http://raptorx2. uchicago. edu/StructurePropertyPred/predict/) is a
web server predicting structure property of a protein sequence without using any templates …
web server predicting structure property of a protein sequence without using any templates …
DeepSF: deep convolutional neural network for mapping protein sequences to folds
Motivation Protein fold recognition is an important problem in structural bioinformatics.
Almost all traditional fold recognition methods use sequence (homology) comparison to …
Almost all traditional fold recognition methods use sequence (homology) comparison to …
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions
F Meng, VN Uversky, L Kurgan - Cellular and Molecular Life Sciences, 2017 - Springer
Computational prediction of intrinsic disorder in protein sequences dates back to late 1970
and has flourished in the last two decades. We provide a brief historical overview, and we …
and has flourished in the last two decades. We provide a brief historical overview, and we …
A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction
Y Liu, X Wang, B Liu - Briefings in bioinformatics, 2019 - academic.oup.com
Intrinsically disordered proteins and regions are widely distributed in proteins, which are
associated with many biological processes and diseases. Accurate prediction of intrinsically …
associated with many biological processes and diseases. Accurate prediction of intrinsically …
[HTML][HTML] GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification
Exponential growth of biomedical literature and clinical data demands more robust yet
precise computational methodologies to extract useful insights from biomedical literature …
precise computational methodologies to extract useful insights from biomedical literature …