AIMC Topic: Sequence Analysis

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A Guaranteed Similarity Metric Learning Framework for Biological Sequence Comparison.

IEEE/ACM transactions on computational biology and bioinformatics Oct 26, 2015
Similarity of sequences is a key mathematical notion for Classification and Phylogenetic studies in Biology. The distance and similarity between two sequence are very important and widely studied. During the last decades, Similarity(distance) metric ...
Data Interpretation, Statistical Reproducibility of Results Machine Learning Sensitivity and Specificity Pattern Recognition, Automated Algorithms Sequence Alignment Sequence Analysis
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A software framework for end-to-end genomic sequence analysis with deep learning.

Nature computational science Nov 1, 2023
Deep Learning Genomics Sequence Analysis Neural Networks, Computer Software
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A tool for feature extraction from biological sequences.

Briefings in bioinformatics May 13, 2022
With the advances in sequencing technologies, a huge amount of biological data is extracted nowadays. Analyzing this amount of data is beyond the ability of human beings, creating a splendid opportunity for machine learning methods to grow. The metho...
Humans DNA Machine Learning RNA Proteins Sequence Analysis
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eSkip-Finder: a machine learning-based web application and database to identify the optimal sequences of antisense oligonucleotides for exon skipping.

Nucleic acids research Jul 2, 2021
Exon skipping using antisense oligonucleotides (ASOs) has recently proven to be a powerful tool for mRNA splicing modulation. Several exon-skipping ASOs have been approved to treat genetic diseases worldwide. However, a significant challenge is the d...
Sequence Analysis RNA Splicing Oligonucleotides, Antisense Internet Exons Machine Learning Introns Software Databases, Nucleic Acid
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iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization.

Nucleic acids research Jun 4, 2021
Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate understanding of the sequence(-structure)-function paradigm for DNAs, RNAs and proteins. Rapid accumulation of sequences requires equally pervasive development...
Humans Machine Learning Software Amino Acid Sequence Base Sequence Sequence Analysis Animals Computational Biology
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Deep Neural Networks for Epistatic Sequence Analysis.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
We report a step-by-step protocol to use pysster, a TensorFlow-based package for building deep neural networks on a broad range of epistatic sequences such as DNA, RNA, or annotated secondary structure sequences. Pysster provides users comprehensive ...
Sequence Analysis RNA Editing Models, Genetic RNA Base Sequence Epistasis, Genetic Datasets as Topic Deep Learning ROC Curve Humans Software
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iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

Briefings in bioinformatics May 21, 2020
With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficie...
Proteins DNA RNA Sequence Analysis Internet Machine Learning Algorithms
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Is Case Sequence Analysis an Objective Assessment of Learning Curve?

Diseases of the colon and rectum Mar 1, 2020
Humans Clinical Competence Robotics Colorectal Neoplasms Learning Curve Robotic Surgical Procedures Sequence Analysis
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BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches.

Briefings in bioinformatics Jul 19, 2019
With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems is how to computationally analyze their structures and functions. Machine learning techniques are playing key roles in this field. Typi...
Sequence Analysis Humans Internet Databases, Nucleic Acid Algorithms Sequence Analysis, Protein Sequence Analysis, DNA Computational Biology Databases, Protein Sequence Analysis, RNA Machine Learning Software
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Semi-supervised learning of Hidden Markov Models for biological sequence analysis.

Bioinformatics (Oxford, England) Jul 1, 2019
MOTIVATION: Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised man...
Markov Chains Sequence Analysis Algorithms Models, Statistical Supervised Machine Learning
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