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/.

scholarly journals Constructing benchmark test sets for biological sequence analysis using independent set algorithms

2021 ◽  
Author(s):  
Samantha Petti ◽  
Sean R Eddy
Keyword(s):  
Sequence Analysis ◽  
Sequence Data ◽  
Independent Set ◽  
Training Sequence ◽  
Test Sequence ◽  
Biological Sequence ◽  
Biological Sequence Analysis ◽  
Training Sequences ◽  
Benchmark Datasets ◽  
Test Sets

Statistical inference and machine learning methods are benchmarked on test data independent of the data used to train the method. Biological sequence families are highly non-independent because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in bench marking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new meth- ods for splitting sequence data into dissimilar training and test sets. These algo rithms input a sequence family and produce a split in which each test sequence is less than p % identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.

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.

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.

Towards corpus and model: Hierarchical structured-attention-based features for Indonesian named entity recognition

2021 ◽  
pp. 1-12
Author(s):  
Yingwen Fu ◽  
Nankai Lin ◽  
Xiaotian Lin ◽  
Shengyi Jiang
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Language Models ◽  
Neural Models ◽  
Performance Models ◽  
Named Entity ◽  
High Resource ◽  
Benchmark Datasets

Named entity recognition (NER) is fundamental to natural language processing (NLP). Most state-of-the-art researches on NER are based on pre-trained language models (PLMs) or classic neural models. However, these researches are mainly oriented to high-resource languages such as English. While for Indonesian, related resources (both in dataset and technology) are not yet well-developed. Besides, affix is an important word composition for Indonesian language, indicating the essentiality of character and token features for token-wise Indonesian NLP tasks. However, features extracted by currently top-performance models are insufficient. Aiming at Indonesian NER task, in this paper, we build an Indonesian NER dataset (IDNER) comprising over 50 thousand sentences (over 670 thousand tokens) to alleviate the shortage of labeled resources in Indonesian. Furthermore, we construct a hierarchical structured-attention-based model (HSA) for Indonesian NER to extract sequence features from different perspectives. Specifically, we use an enhanced convolutional structure as well as an enhanced attention structure to extract deeper features from characters and tokens. Experimental results show that HSA establishes competitive performance on IDNER and three benchmark datasets.

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