scholarly journals BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches

2019 ◽  
Vol 47 (20) ◽  
pp. e127-e127 ◽  
Author(s):  
Bin Liu ◽  
Xin Gao ◽  
Hanyu Zhang
Keyword(s):  
Machine Learning ◽  
Sequence Analysis ◽  
State Of The Art ◽  
Protein Sequences ◽  
Residue Level ◽  
Learning Approaches ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Biological Sequence Analysis ◽  
Level Analysis

Abstract As the first web server to analyze various biological sequences at sequence level based on machine learning approaches, many powerful predictors in the field of computational biology have been developed with the assistance of the BioSeq-Analysis. However, the BioSeq-Analysis can be only applied to the sequence-level analysis tasks, preventing its applications to the residue-level analysis tasks, and an intelligent tool that is able to automatically generate various predictors for biological sequence analysis at both residue level and sequence level is highly desired. In this regard, we decided to publish an important updated server covering a total of 26 features at the residue level and 90 features at the sequence level called BioSeq-Analysis2.0 (http://bliulab.net/BioSeq-Analysis2.0/), by which the users only need to upload the benchmark dataset, and the BioSeq-Analysis2.0 can generate the predictors for both residue-level analysis and sequence-level analysis tasks. Furthermore, the corresponding stand-alone tool was also provided, which can be downloaded from http://bliulab.net/BioSeq-Analysis2.0/download/. To the best of our knowledge, the BioSeq-Analysis2.0 is the first tool for generating predictors for biological sequence analysis tasks at residue level. Specifically, the experimental results indicated that the predictors developed by BioSeq-Analysis2.0 can achieve comparable or even better performance than the existing state-of-the-art predictors.

BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches

2017 ◽  
Vol 20 (4) ◽  
pp. 1280-1294 ◽  
Author(s):  
Bin Liu
Keyword(s):  
Machine Learning ◽  
Sequence Analysis ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Biological Sequence ◽  
Protein Sequence Analysis ◽  
Data Set ◽  
Biological Sequence Analysis ◽  
Benchmark Data ◽  
Learning Techniques

AbstractWith 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. Typically, predictors based on machine learning techniques contain three main steps: feature extraction, predictor construction and performance evaluation. Although several Web servers and stand-alone tools have been developed to facilitate the biological sequence analysis, they only focus on individual step. In this regard, in this study a powerful Web server called BioSeq-Analysis (http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/) has been proposed to automatically complete the three main steps for constructing a predictor. The user only needs to upload the benchmark data set. BioSeq-Analysis can generate the optimized predictor based on the benchmark data set, and the performance measures can be reported as well. Furthermore, to maximize user’s convenience, its stand-alone program was also released, which can be downloaded from http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/download/, and can be directly run on Windows, Linux and UNIX. Applied to three sequence analysis tasks, experimental results showed that the predictors generated by BioSeq-Analysis even outperformed some state-of-the-art methods. It is anticipated that BioSeq-Analysis will become a useful tool for biological sequence analysis.

Clustering Techniques for Biological Sequence Analysis: A Review

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Jyoti Lakhani ◽  
Anupama Chowdhary ◽  
Dharmesh Harwani
Keyword(s):  
Sequence Analysis ◽  
Motif Discovery ◽  
Rough Set Theory ◽  
Clustering Algorithms ◽  
Biological Data ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Clustering Techniques ◽  
Biological Sequence Analysis ◽  
Evolutionary Clustering

In the present scenario there are a variety of technical tools for supporting and validating wet-lab experiments in the field of science and biotechnology. In order to analyze biological sequences it is necessary to group similar genes. Grouping of genes can be done by using various techniques like pattern matching, classification, clustering etc. In the present study clustering is used as a tool for analyzing biological data. Clustering of Biological sequences is a very interesting and fascinating area as various researchers are working on it. But simple clustering algorithms are not much suitable for sequence analysis problems. Most of the biological sequence analysis problems are NP-hard and some strong optimization algorithm are required for these types of problems. The manuscript presented here is a survey of various clustering techniques useful for analysis of biological sequences. The 3+ stage review process is adopted for the review of literature. To prepare this report 98 papers have been reviewed from year 1997 to 2014 according to the year of publish. The papers reviewed have discussed various issues related to the analysis of biological sequences. The major issues discovered in the reviewed papers were prediction, sequence alignment, motif discovery, cluster boundary prediction etc. Various solution approaches used by researchers for the biological sequence analysis are evolutionary clustering, neural networks, hierarchical clustering, k-means, Go technologies, feature selection, incremental approach, bio-inspired methods, particle swarm optimization, fuzzy techniques, rough set theory and bi-clustering etc. Researchers have applied these solution approaches on various types of datasets. In this communication we have also discussed about these datasets and the parameters used with results mentioned in papers.

scholarly journals BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models

2021 ◽  
Author(s):  
Hong-Liang Li ◽  
Yi-He Pang ◽  
Bin Liu
Keyword(s):  
Sequence Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Sequence Data ◽  
Language Models ◽  
Biological Sequence ◽  
Protein Sequence Analysis ◽  
Processing Technologies ◽  
Biological Sequence Analysis ◽  
Important Field

Abstract In order to uncover the meanings of ‘book of life’, 155 different biological language models (BLMs) for DNA, RNA and protein sequence analysis are discussed in this study, which are able to extract the linguistic properties of ‘book of life’. We also extend the BLMs into a system called BioSeq-BLM for automatically representing and analyzing the sequence data. Experimental results show that the predictors generated by BioSeq-BLM achieve comparable or even obviously better performance than the exiting state-of-the-art predictors published in literatures, indicating that BioSeq-BLM will provide new approaches for biological sequence analysis based on natural language processing technologies, and contribute to the development of this very important field. In order to help the readers to use BioSeq-BLM for their own experiments, the corresponding web server and stand-alone package are established and released, which can be freely accessed at http://bliulab.net/BioSeq-BLM/.

Representation learning applications in biological sequence analysis

2021 ◽  
Author(s):  
Hitoshi Iuchi ◽  
Taro Matsutani ◽  
Keisuke Yamada ◽  
Shunsuke Sumi ◽  
Shion Hosoda ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Language Processing ◽  
High Throughput Sequencing ◽  
Probabilistic Models ◽  
Representation Learning ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Structure Estimation ◽  
Biological Sequence Analysis ◽  
Essential Step

Remarkable advances in high-throughput sequencing have resulted in rapid data accumulation, and analyzing biological (DNA/RNA/protein) sequences to discover new insights in biology has become more critical and challenging. To tackle this issue, the application of natural language processing (NLP) to biological sequence analysis has received increased attention, because biological sequences are regarded as sentences and k-mers in these sequences as words. Embedding is an essential step in NLP, which converts words into vectors. This transformation is called representation learning and can be applied to biological sequences. Vectorized biological sequences can be used for function and structure estimation, or as inputs for other probabilistic models. Given the importance and growing trend in the application of representation learning in biology, here, we review the existing knowledge in representation learning for biological sequence analysis.

scholarly journals Supervised learning for the detection of negation and of its scope in French and Brazilian Portuguese biomedical corpora

2020 ◽  
pp. 1-21 ◽  
Author(s):  
Clément Dalloux ◽  
Vincent Claveau ◽  
Natalia Grabar ◽  
Lucas Emanuel Silva Oliveira ◽  
Claudia Maria Cabral Moro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
State Of The Art ◽  
Automatic Detection ◽  
Brazilian Portuguese ◽  
Supervised Machine Learning ◽  
Biomedical Domain ◽  
Learning Approaches ◽  
Cross Domain ◽  
Automatic Methods

Abstract Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented.

scholarly journals Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map

2021 ◽  
Vol 11 (8) ◽  
pp. 785
Author(s):  
Quentin Miagoux ◽  
Vidisha Singh ◽  
Dereck de Mézquita ◽  
Valerie Chaudru ◽  
Mohamed Elati ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
State Of The Art ◽  
Biological Information ◽  
Response To Treatment ◽  
Patient Specific ◽  
Learning Approaches ◽  
Data Types ◽  
Disease Heterogeneity ◽  
Combining Data

Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients’ data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.

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