AIMC Topic: Proteins

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Artificial Intelligence Steering Molecular Therapy in the Absence of Information on Target Structure and Regulation.

Journal of chemical information and modeling
Protein associations are at the core of biological activity, and the drug-based disruption of dysfunctional associations poses a major challenge to targeted therapy. The problem becomes daunting when the structure and regulated modulation of the comp...

Multimodal deep representation learning for protein interaction identification and protein family classification.

BMC bioinformatics
BACKGROUND: Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve ...

DeepEP: a deep learning framework for identifying essential proteins.

BMC bioinformatics
BACKGROUND: Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this p...

NNTox: Gene Ontology-Based Protein Toxicity Prediction Using Neural Network.

Scientific reports
With advancements in synthetic biology, the cost and the time needed for designing and synthesizing customized gene products have been steadily decreasing. Many research laboratories in academia as well as industry routinely create genetically engine...

Analysis of the Human Protein Atlas Image Classification competition.

Nature methods
Pinpointing subcellular protein localizations from microscopy images is easy to the trained eye, but challenging to automate. Based on the Human Protein Atlas image collection, we held a competition to identify deep learning solutions to solve this t...

Sounds interesting: can sonification help us design new proteins?

Expert review of proteomics
: The practice of turning scientific data into music, a practice known as sonification, is a growing field. Driven by analogies between the hierarchical structures of proteins and many forms of music, multiple attempts of mapping proteins to music ha...

Learning a functional grammar of protein domains using natural language word embedding techniques.

Proteins
In this paper, using Word2vec, a widely-used natural language processing method, we demonstrate that protein domains may have a learnable implicit semantic "meaning" in the context of their functional contributions to the multi-domain proteins in whi...

High-accuracy protein structures by combining machine-learning with physics-based refinement.

Proteins
Protein structure prediction has long been available as an alternative to experimental structure determination, especially via homology modeling based on templates from related sequences. Recently, models based on distance restraints from coevolution...

Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis.

Scientific reports
Machine learning (ML) is ubiquitous in bioinformatics, due to its versatility. One of the most crucial aspects to consider while training a ML model is to carefully select the optimal feature encoding for the problem at hand. Biophysical propensity s...

Machine learning techniques for protein function prediction.

Proteins
Proteins play important roles in living organisms, and their function is directly linked with their structure. Due to the growing gap between the number of proteins being discovered and their functional characterization (in particular as a result of ...