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https://www.readbyqxmd.com/read/29040432/machine-learning-accelerates-md-based-binding-pose-prediction-between-ligands-and-proteins
#1
Kei Terayama, Hiroaki Iwata, Mitsugu Araki, Yasushi Okuno, Koji Tsuda
Motivation: Fast and accurate prediction of protein-ligand binding structures is indispensable for structure-based drug design (SBDD) and accurate estimation of binding free energy of drug candidate molecules in drug discovery. Recently, accurate pose prediction methods based on short-MD simulations such as MM-PBSA and MM-GBSA among generated docking poses have been used. Since molecular structures obtained from MD simulation depends on the initial condition, taking the average over different initial conditions leads to better accuracy...
October 11, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29036627/using-uncertainty-to-link-and-rank-evidence-from-biomedical-literature-for-model-curation
#2
Chrysoula Zerva, Riza Batista-Navarro, Philip Day, Sophia Ananiadou
Motivation: In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author...
July 24, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29036616/deeploc-prediction-of-protein-subcellular-localization-using-deep-learning
#3
José Juan Almagro Armenteros, Casper Kaae Sønderby, Søren Kaae Sønderby, Henrik Nielsen, Ole Winther
Motivation: The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only...
July 7, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29036404/the-value-of-prior-knowledge-in-machine-learning-of-complex-network-systems
#4
Dana Ferranti, David Krane, David Craft
Motivation: Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available...
July 7, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29036382/musitedeep-a-deep-learning-framework-for-general-and-kinase-specific-phosphorylation-site-prediction
#5
Duolin Wang, Shuai Zeng, Chunhui Xu, Wangren Qiu, Yanchun Liang, Trupti Joshi, Dong Xu
Motivation: Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction...
August 3, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29036277/pyseqlab-an-open-source-python-package-for-sequence-labeling-and-segmentation
#6
Ahmed Allam, Michael Krauthammer
Motivation: Text and genomic data are composed of sequential tokens, such as words and nucleotides that give rise to higher order syntactic constructs. In this work, we aim at providing a comprehensive Python library implementing conditional random fields (CRFs), a class of probabilistic graphical models, for robust prediction of these constructs from sequential data. Results: Python Sequence Labeling (PySeqLab) is an open source package for performing supervised learning in structured prediction tasks...
July 21, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29028926/structure-based-prediction-of-protein-peptide-binding-regions-using-random-forest
#7
Ghazaleh Taherzadeh, Yaoqi Zhou, Alan Wee-Chung Liew, Yuedong Yang
Motivation: Protein-peptide interactions are one of the most important biological interactions and play crucial role in many diseases including cancer. Therefore, knowledge of these interactions provides invaluable insights into all cellular processes, functional mechanisms, and drug discovery. Protein-peptide interactions can be analyzed by studying the structures of protein-peptide complexes. However, only a small portion has known complex structures and experimental determination of protein-peptide interaction is costly and inefficient...
September 26, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29028911/genome-wide-pre-mirna-discovery-from-few-labeled-examples
#8
C Yones, G Stegmayer, D H Milone
Motivation: Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative examples. Those methods have important practical limitations when they have to be applied to a real prediction task. First, there is the challenge of dealing with a scarce number of positive (well-known) pre-miRNA examples. Secondly, it is very difficult to build a good set of negative examples for representing the full spectrum of non-miRNA sequences...
September 25, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28968749/omics-analysis-system-for-precision-oncology-oasispro-a-web-based-omics-analysis-tool-for-clinical-phenotype-prediction
#9
Kun-Hsing Yu, Michael R Fitzpatrick, Luke Pappas, Warren Chan, Jessica Kung, Michael Snyder
Summary: Precision oncology is an approach that accounts for individual differences to guide cancer management. Omics signatures have been shown to predict clinical traits for cancer patients. However, the vast amount of omics information poses an informatics challenge in systematically identifying patterns associated with health outcomes, and no general-purpose data-mining tool exists for physicians, medical researchers, and citizen scientists without significant training in programming and bioinformatics...
September 12, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28968641/membrain-contact-2-0-a-new-two-stage-machine-learning-model-for-the-prediction-enhancement-of-transmembrane-protein-residue-contacts-in-the-full-chain
#10
Jing Yang, Hong-Bin Shen
Motivation: Inter-residue contacts in proteins have been widely acknowledged to be valuable for protein 3D structure prediction. Accurate prediction of long-range transmembrane inter-helix residue contacts can significantly improve the quality of simulated membrane protein models. Results: In this paper, we present an updated MemBrain predictor, which aims to predict transmembrane protein residue contacts. Our new model benefits from an efficient learning algorithm that can mine latent structural features, which exist in original feature space...
September 15, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28961999/prediction-of-delayed-retention-of-antibodies-in-hydrophobic-interaction-chromatography-from-sequence-using-machine-learning
#11
Tushar Jain, Todd Boland, Asparouh Lilov, Irina Burnina, Michael Brown, Yingda Xu, Maximiliano Vásquez
Motivation: The hydrophobicity of a monoclonal antibody is an important biophysical property relevant for its developability into a therapeutic. In addition to characterizing heterogeneity, Hydrophobic Interaction Chromatography (HIC) is an assay that is often used to quantify the hydrophobicity of an antibody to assess downstream risks. Earlier studies have shown that retention times in this assay can be correlated to amino-acid or atomic propensities weighted by the surface areas obtained from protein 3-dimensional structures...
August 16, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28961695/an-introduction-to-deep-learning-on-biological-sequence-data-examples-and-solutions
#12
Vanessa Isabell Jurtz, Alexander Rosenberg Johansen, Morten Nielsen, Jose Juan Almagro Armenteros, Henrik Nielsen, Casper Kaae Sønderby, Ole Winther, Søren Kaae Sønderby
Motivation: Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology...
August 23, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28923002/deep-learning-methods-for-protein-torsion-angle-prediction
#13
Haiou Li, Jie Hou, Badri Adhikari, Qiang Lyu, Jianlin Cheng
BACKGROUND: Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins...
September 18, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28914640/machine-learning-novel-bioinformatics-approaches-for-combating-antimicrobial-resistance
#14
Nenad Macesic, Fernanda Polubriaginof, Nicholas P Tatonetti
PURPOSE OF REVIEW: Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR. RECENT FINDINGS: The advent of large-scale data sets provided by next-generation sequencing and electronic health records make applying machine learning to the study and treatment of AMR possible...
September 12, 2017: Current Opinion in Infectious Diseases
https://www.readbyqxmd.com/read/28903538/possum-a-bioinformatics-toolkit-for-generating-numerical-sequence-feature-descriptors-based-on-pssm-profiles
#15
Jiawei Wang, Bingjiao Yang, Jerico Revote, André Leier, Tatiana T Marquez-Lago, Geoffrey Webb, Jiangning Song, Kuo-Chen Chou, Trevor Lithgow
Summary: Evolutionary information in the form of a Position-Specific Scoring Matrix (PSSM) is a widely used and highly informative representation of protein sequences. Accordingly, PSSM-based feature descriptors have been successfully applied to improve the performance of various predictors of protein attributes. Even though a number of algorithms have been proposed in previous studies, there is currently no universal web server or toolkit available for generating this wide variety of descriptors...
September 1, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28886064/sexcmd-development-and-validation-of-sex-marker-sequences-for-whole-exome-genome-and-rna-sequencing
#16
Seongmun Jeong, Jiwoong Kim, Won Park, Hongmin Jeon, Namshin Kim
Over the last decade, a large number of nucleotide sequences have been generated by next-generation sequencing technologies and deposited to public databases. However, most of these datasets do not specify the sex of individuals sampled because researchers typically ignore or hide this information. Male and female genomes in many species have distinctive sex chromosomes, XX/XY and ZW/ZZ, and expression levels of many sex-related genes differ between the sexes. Herein, we describe how to develop sex marker sequences from syntenic regions of sex chromosomes and use them to quickly identify the sex of individuals being analyzed...
2017: PloS One
https://www.readbyqxmd.com/read/28859346/data-science-priorities-for-a-university-hospital-based-institute-of-infectious-diseases-a-viewpoint
#17
Alain-Jacques Valleron
Automation of laboratory tests, bioinformatic analysis of biological sequences, and professional data management are used routinely in a modern university hospital-based infectious diseases institute. This dates back to at least the 1980s. However, the scientific methods of this 21st century are changing with the increased power and speed of computers, with the "big data" revolution having already happened in genomics and environment, and eventually arriving in medical informatics. The research will be increasingly "data driven," and the powerful machine learning methods whose efficiency is demonstrated in daily life will also revolutionize medical research...
August 15, 2017: Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America
https://www.readbyqxmd.com/read/28830349/prediction-of-bacterial-small-rnas-in-the-rsma-csra-and-toxt-pathways-a-machine-learning-approach
#18
Carl Tony Fakhry, Prajna Kulkarni, Ping Chen, Rahul Kulkarni, Kourosh Zarringhalam
BACKGROUND: Small RNAs (sRNAs) constitute an important class of post-transcriptional regulators that control critical cellular processes in bacteria. Recent research using high-throughput transcriptomic approaches has led to a dramatic increase in the discovery of bacterial sRNAs. However, it is generally believed that the currently identified sRNAs constitute a limited subset of the bacterial sRNA repertoire. In several cases, sRNAs belonging to a specific class are already known and the challenge is to identify additional sRNAs belonging to the same class...
August 22, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28764323/lipidccs-prediction-of-collision-cross-section-values-for-lipids-with-high-precision-to-support-ion-mobility-mass-spectrometry-based-lipidomics
#19
Zhiwei Zhou, Jia Tu, Xin Xiong, Xiaotao Shen, Zheng-Jiang Zhu
The use of collision cross-section (CCS) values derived from ion mobility-mass spectrometry (IM-MS) has been proven to facilitate lipid identifications. Its utility is restricted by the limited availability of CCS values. Recently, the machine-learning algorithm-based prediction (e.g., MetCCS) is reported to generate CCS values in a large-scale. However, the prediction precision is not sufficient to differentiate lipids due to their high structural similarities and subtle differences on CCS values. To address this challenge, we developed a new approach, namely, LipidCCS, to precisely predict lipid CCS values...
September 5, 2017: Analytical Chemistry
https://www.readbyqxmd.com/read/28733902/clustering-and-candidate-motif-detection-in-exosomal-mirnas-by-application-of-machine-learning-algorithms
#20
Pallavi Gaur, Anoop Chaturvedi
BACKGROUND: The clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Recent progress in the field of exosome research and more particularly regarding exosomal miRNAs has led much bioinformatic-based research to come into existence. The information on clustering pattern and candidate motifs in miRNAs of exosomal origin would help in analyzing existing, as well as newly discovered miRNAs within exosomes...
July 22, 2017: Interdisciplinary Sciences, Computational Life Sciences
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