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Machine Learning and Artificial Intelligence in Bioinformatics

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  1. In short-read DNA sequencing experiments, the read coverage is a key parameter to successfully assemble the reads and reconstruct the sequence of the input DNA. When coverage is very low, the original sequence...

    Authors: Louis Ranjard, Thomas K. F. Wong and Allen G. Rodrigo

    Citation: BMC Bioinformatics 2019 20:654

    Content type: Research Article

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  2. Computational compound repositioning has the potential for identifying new uses for existing drugs, and new algorithms and data source aggregation strategies provide ever-improving results via in silico metric...

    Authors: Michael Mayers, Tong Shu Li, Núria Queralt-Rosinach and Andrew I. Su

    Citation: BMC Bioinformatics 2019 20:653

    Content type: Research article

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  3. The Bacteria Biotope (BB) task is a biomedical relation extraction (RE) that aims to study the interaction between bacteria and their locations. This task is considered to pertain to fundamental knowledge in a...

    Authors: Amarin Jettakul, Duangdao Wichadakul and Peerapon Vateekul

    Citation: BMC Bioinformatics 2019 20:627

    Content type: Research Article

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  4. Microarray datasets consist of complex and high-dimensional samples and genes, and generally the number of samples is much smaller than the number of genes. Due to this data imbalance, gene selection is a dema...

    Authors: Russul Alanni, Jingyu Hou, Hasseeb Azzawi and Yong Xiang

    Citation: BMC Bioinformatics 2019 20:608

    Content type: Research article

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  5. De novo drug discovery is a time-consuming and expensive process. Nowadays, drug repositioning is utilized as a common strategy to discover a new drug indication for existing drugs. This strategy is mostly use...

    Authors: Mahroo Moridi, Marzieh Ghadirinia, Ali Sharifi-Zarchi and Fatemeh Zare-Mirakabad

    Citation: BMC Bioinformatics 2019 20:577

    Content type: Research article

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  6. Cancer subtype classification attains the great importance for accurate diagnosis and personalized treatment of cancer. Latest developments in high-throughput sequencing technologies have rapidly produced mult...

    Authors: Jing Xu, Peng Wu, Yuehui Chen, Qingfang Meng, Hussain Dawood and Hassan Dawood

    Citation: BMC Bioinformatics 2019 20:527

    Content type: Methodology Article

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  7. Network inference is crucial for biomedicine and systems biology. Biological entities and their associations are often modeled as interaction networks. Examples include drug protein interaction or gene regulat...

    Authors: Konstantinos Pliakos and Celine Vens

    Citation: BMC Bioinformatics 2019 20:525

    Content type: Research Article

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  8. Protein subcellular localization plays a crucial role in understanding cell function. Proteins need to be in the right place at the right time, and combine with the corresponding molecules to fulfill their fun...

    Authors: Fan Yang, Yang Liu, Yanbin Wang, Zhijian Yin and Zhen Yang

    Citation: BMC Bioinformatics 2019 20:522

    Content type: Research article

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  9. Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling i...

    Authors: Sunyoung Kwon, Ho Bae, Jeonghee Jo and Sungroh Yoon

    Citation: BMC Bioinformatics 2019 20:521

    Content type: Methodology Article

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  10. When applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient’s phenotypes. Typically, this is done through annotation, fil...

    Authors: James M. Holt, Brandon Wilk, Camille L. Birch, Donna M. Brown, Manavalan Gajapathy, Alexander C. Moss, Nadiya Sosonkina, Melissa A. Wilk, Julie A. Anderson, Jeremy M. Harris, Jacob M. Kelly, Fariba Shaterferdosian, Angelina E. Uno-Antonison, Arthur Weborg and Elizabeth A. Worthey

    Citation: BMC Bioinformatics 2019 20:496

    Content type: Research Article

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  11. The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional ann...

    Authors: Chih-Hao Fang, Nawanol Theera-Ampornpunt, Michael A. Roth, Ananth Grama and Somali Chaterji

    Citation: BMC Bioinformatics 2019 20:488

    Content type: Research Article

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  12. MicroRNAs (miRNAs) are noncoding RNA molecules heavily involved in human tumors, in which few of them circulating the human body. Finding a tumor-associated signature of miRNA, that is, the minimum miRNA entit...

    Authors: Alejandro Lopez-Rincon, Marlet Martinez-Archundia, Gustavo U. Martinez-Ruiz, Alexander Schoenhuth and Alberto Tonda

    Citation: BMC Bioinformatics 2019 20:480

    Content type: Research Article

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  13. The adverse reactions that are caused by drugs are potentially life-threatening problems. Comprehensive knowledge of adverse drug reactions (ADRs) can reduce their detrimental impacts on patients. Detecting AD...

    Authors: Tongxuan Zhang, Hongfei Lin, Yuqi Ren, Liang Yang, Bo Xu, Zhihao Yang, Jian Wang and Yijia Zhang

    Citation: BMC Bioinformatics 2019 20:479

    Content type: Research Article

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  14. Binding sites are the pockets of proteins that can bind drugs; the discovery of these pockets is a critical step in drug design. With the help of computers, protein pockets prediction can save manpower and fin...

    Authors: Mingjian Jiang, Zhen Li, Yujie Bian and Zhiqiang Wei

    Citation: BMC Bioinformatics 2019 20:478

    Content type: Methodology Article

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  15. Long-chain non-coding RNA (lncRNA) is closely related to many biological activities. Since its sequence structure is similar to that of messenger RNA (mRNA), it is difficult to distinguish between the two base...

    Authors: Jianghui Wen, Yeshu Liu, Yu Shi, Haoran Huang, Bing Deng and Xinping Xiao

    Citation: BMC Bioinformatics 2019 20:469

    Content type: Methodology article

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  16. MiRNAs play significant roles in many fundamental and important biological processes, and predicting potential miRNA-disease associations makes contributions to understanding the molecular mechanism of human d...

    Authors: Yuchong Gong, Yanqing Niu, Wen Zhang and Xiaohong Li

    Citation: BMC Bioinformatics 2019 20:468

    Content type: Research article

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  17. Although many of the genic features in Mycobacterium abscessus have been fully validated, a comprehensive understanding of the regulatory elements remains lacking. Moreover, there is little understanding of how t...

    Authors: Patrick M. Staunton, Aleksandra A. Miranda-CasoLuengo, Brendan J. Loftus and Isobel Claire Gormley

    Citation: BMC Bioinformatics 2019 20:466

    Content type: Research Article

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  18. The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical...

    Authors: Leanne S. Whitmore, Bernard Nguyen, Ali Pinar, Anthe George and Corey M. Hudson

    Citation: BMC Bioinformatics 2019 20:461

    Content type: Software

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  19. With the advent of array-based techniques to measure methylation levels in primary tumor samples, systematic investigations of methylomes have widely been performed on a large number of tumor entities. Most of...

    Authors: Pascal David Johann, Natalie Jäger, Stefan M. Pfister and Martin Sill

    Citation: BMC Bioinformatics 2019 20:428

    Content type: Methodology article

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  20. Since the number of known lncRNA-disease associations verified by biological experiments is quite limited, it has been a challenging task to uncover human disease-related lncRNAs in recent years. Moreover, con...

    Authors: Jingwen Yu, Zhanwei Xuan, Xiang Feng, Quan Zou and Lei Wang

    Citation: BMC Bioinformatics 2019 20:396

    Content type: Research article

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  21. Alkaloids, a class of organic compounds that contain nitrogen bases, are mainly synthesized as secondary metabolites in plants and fungi, and they have a wide range of bioactivities. Although there are thousan...

    Authors: Ryohei Eguchi, Naoaki Ono, Aki Hirai Morita, Tetsuo Katsuragi, Satoshi Nakamura, Ming Huang, Md. Altaf-Ul-Amin and Shigehiko Kanaya

    Citation: BMC Bioinformatics 2019 20:380

    Content type: Research article

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  22. Unsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation of the da...

    Authors: Savvas Kinalis, Finn Cilius Nielsen, Ole Winther and Frederik Otzen Bagger

    Citation: BMC Bioinformatics 2019 20:379

    Content type: Methodology article

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  23. In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensional...

    Authors: Marina Esteban-Medina, María Peña-Chilet, Carlos Loucera and Joaquín Dopazo

    Citation: BMC Bioinformatics 2019 20:370

    Content type: Research article

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  24. Predicting meaningful miRNA-disease associations (MDAs) is costly. Therefore, an increasing number of researchers are beginning to focus on methods to predict potential MDAs. Thus, prediction methods with impr...

    Authors: Ying-Lian Gao, Zhen Cui, Jin-Xing Liu, Juan Wang and Chun-Hou Zheng

    Citation: BMC Bioinformatics 2019 20:353

    Content type: Methodology article

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  25. Modern genomic and proteomic profiling methods produce large amounts of data from tissue and blood-based samples that are of potential utility for improving patient care. However, the design of precision medic...

    Authors: Joanna Roder, Carlos Oliveira, Lelia Net, Maxim Tsypin, Benjamin Linstid and Heinrich Roder

    Citation: BMC Bioinformatics 2019 20:325

    Content type: Methodology article

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  26. Although various machine learning-based predictors have been developed for estimating protein–protein interactions, their performances vary with dataset and species, and are affected by two primary aspects: ch...

    Authors: Kuan-Hsi Chen, Tsai-Feng Wang and Yuh-Jyh Hu

    Citation: BMC Bioinformatics 2019 20:308

    Content type: Methodology article

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  27. Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mos...

    Authors: Zheng Gao, Gang Fu, Chunping Ouyang, Satoshi Tsutsui, Xiaozhong Liu, Jeremy Yang, Christopher Gessner, Brian Foote, David Wild, Ying Ding and Qi Yu

    Citation: BMC Bioinformatics 2019 20:306

    Content type: Methodology article

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  28. Modern molecular profiling techniques are yielding vast amounts of data from patient samples that could be utilized with machine learning methods to provide important biological insights and improvements in pa...

    Authors: Heinrich Roder, Carlos Oliveira, Lelia Net, Benjamin Linstid, Maxim Tsypin and Joanna Roder

    Citation: BMC Bioinformatics 2019 20:273

    Content type: Methodology article

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  29. Automatic extraction of chemical-disease relations (CDR) from unstructured text is of essential importance for disease treatment and drug development. Meanwhile, biomedical experts have built many highly-struc...

    Authors: Huiwei Zhou, Chengkun Lang, Zhuang Liu, Shixian Ning, Yingyu Lin and Lei Du

    Citation: BMC Bioinformatics 2019 20:260

    Content type: Research article

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  30. Computational approaches for the determination of biologically-active/native three-dimensional structures of proteins with novel sequences have to handle several challenges. The (conformation) space of possibl...

    Authors: Ahmed Bin Zaman and Amarda Shehu

    Citation: BMC Bioinformatics 2019 20:211

    Content type: Research article

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