AIMC Topic: Sequence Analysis, Protein

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Deep Robust Framework for Protein Function Prediction Using Variable-Length Protein Sequences.

IEEE/ACM transactions on computational biology and bioinformatics Apr 16, 2019
The order of amino acids in a protein sequence enables the protein to acquire a conformation suitable for performing functions, thereby motivating the need to analyze these sequences for predicting functions. Although machine learning based approache...
Protein Conformation Sequence Analysis, Protein Algorithms Deep Learning Computational Biology Proteins Discriminant Analysis Amino Acid Sequence
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Seq2seq Fingerprint with Byte-Pair Encoding for Predicting Changes in Protein Stability upon Single Point Mutation.

IEEE/ACM transactions on computational biology and bioinformatics Apr 1, 2019
The engineering of stable proteins is crucial for various industrial purposes. Several machine learning methods have been developed to predict changes in the stability of proteins corresponding to single point mutations. To improve the prediction acc...
Neural Networks, Computer Computational Biology Databases, Genetic Humans Protein Stability Proteins Sequence Analysis, Protein Unsupervised Machine Learning Point Mutation Amino Acid Sequence
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NeuroPIpred: a tool to predict, design and scan insect neuropeptides.

Scientific reports Mar 26, 2019
Insect neuropeptides and their associated receptors have been one of the potential targets for the pest control. The present study describes in silico models developed using natural and modified insect neuropeptides for predicting and designing new n...
Sequence Analysis, Protein Software Animals Support Vector Machine Databases, Protein Amino Acid Sequence Neuropeptides Humans
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Classification of Antibacterial Peptides Using Long Short-Term Memory Recurrent Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics Mar 7, 2019
Antimicrobial peptides are short amino acid sequences that may be antibacterial, antifungal, and antiviral. Most machine learning methodologies applied to identifying antibacterial peptides have developed feature vectors of identical lengths for each...
Algorithms Anti-Bacterial Agents Computational Biology Neural Networks, Computer Databases, Protein Sequence Analysis, Protein Pore Forming Cytotoxic Proteins
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RPITER: A Hierarchical Deep Learning Framework for ncRNA⁻Protein Interaction Prediction.

International journal of molecular sciences Mar 1, 2019
Non-coding RNAs (ncRNAs) play crucial roles in multiple fundamental biological processes, such as post-transcriptional gene regulation, and are implicated in many complex human diseases. Mostly ncRNAs function by interacting with corresponding RNA-bi...
Humans Machine Learning Software Sequence Analysis, RNA Animals RNA, Long Noncoding Protein Binding Sequence Analysis, Protein
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Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform.

International journal of molecular sciences Feb 21, 2019
It is significant for biological cells to predict self-interacting proteins (SIPs) in the field of bioinformatics. SIPs mean that two or more identical proteins can interact with each other by one gene expression. This plays a major role in the evolu...
Humans Animals Protein Binding Saccharomyces cerevisiae Proteins Binding Sites Support Vector Machine Sequence Analysis, Protein Fourier Analysis
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SignalP 5.0 improves signal peptide predictions using deep neural networks.

Nature biotechnology Feb 18, 2019
Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish ...
Protein Sorting Signals Archaeal Proteins Eukaryota Sequence Analysis, Protein Software Algorithms Computational Biology Bacterial Proteins Amino Acid Sequence Biotechnology Neural Networks, Computer
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TargetDBP: Accurate DNA-Binding Protein Prediction Via Sequence-Based Multi-View Feature Learning.

IEEE/ACM transactions on computational biology and bioinformatics Jan 18, 2019
Accurately identifying DNA-binding proteins (DBPs) from protein sequence information is an important but challenging task for protein function annotations. In this paper, we establish a novel computational method, named TargetDBP, for accurately targ...
Computational Biology Machine Learning DNA-Binding Proteins Support Vector Machine Sequence Analysis, Protein Databases, Protein Algorithms Position-Specific Scoring Matrices
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DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure.

PloS one Jan 8, 2019
Rapid, accurate prediction of protein structure from amino acid sequence would accelerate fields as diverse as drug discovery, synthetic biology and disease diagnosis. Massively improved prediction of protein structures has been driven by improving t...
Protein Structure, Tertiary Algorithms Sequence Analysis, Protein Databases, Protein Support Vector Machine Deep Learning Models, Molecular Proteins Computational Biology Amino Acid Sequence
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Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning.

Scientific reports Oct 15, 2018
RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow fo...
Computational Biology Algorithms RNA-Binding Proteins Sensitivity and Specificity Proteome Neural Networks, Computer Deep Learning Databases, Protein Machine Learning Amino Acid Sequence Sequence Analysis, Protein
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