keyword
MENU ▼
Read by QxMD icon Read
search

bioinformatics using machine learning

keyword
https://www.readbyqxmd.com/read/29228185/dncon2-improved-protein-contact-prediction-using-two-level-deep-convolutional-neural-networks
#1
Badri Adhikari, Jie Hou, Jianlin Cheng
Motivation: Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps is essential to further improve ab initio structure prediction...
December 8, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29224147/comprehensive-whole-dna-methylome-analysis-by-integrating-medip-seq-and-mre-seq
#2
Xiaoyun Xing, Bo Zhang, Daofeng Li, Ting Wang
Understanding the role of DNA methylation often requires accurate assessment and comparison of these modifications in a genome-wide fashion. Sequencing-based DNA methylation profiling provides an unprecedented opportunity to map and compare complete DNA CpG methylomes. These include whole genome bisulfite sequencing (WGBS), Reduced-Representation Bisulfite-Sequencing (RRBS), and enrichment-based methods such as MeDIP-seq, MBD-seq, and MRE-seq. An investigator needs a method that is flexible with the quantity of input DNA, provides the appropriate balance among genomic CpG coverage, resolution, quantitative accuracy, and cost, and comes with robust bioinformatics software for analyzing the data...
2018: Methods in Molecular Biology
https://www.readbyqxmd.com/read/29218881/data-driven-advice-for-applying-machine-learning-to-bioinformatics-problems
#3
Randal S Olson, William La Cava, Zairah Mustahsan, Akshay Varik, Jason H Moore
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29194464/computer-aided-biomarker-discovery-for-precision-medicine-data-resources-models-and-applications
#4
Yuxin Lin, Fuliang Qian, Li Shen, Feifei Chen, Jiajia Chen, Bairong Shen
Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening...
November 29, 2017: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/29186331/ddr-efficient-computational-method-to-predict-drug-target-interactions-using-graph-mining-and-machine-learning-approaches
#5
Rawan S Olayan, Haitham Ashoor, Vladimir B Bajic
Motivation: Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. Results: We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins...
November 24, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29182723/extreme-learning-machines-for-reverse-engineering-of-gene-regulatory-networks-from-expression-time-series
#6
M Rubiolo, D H Milone, G Stegmayer
Motivation: The reconstruction of gene regulatory networks (GRNs) from genes profiles has a growing interest in bioinformatics for understanding the complex regulatory mechanisms in cellular systems. GRNs explicitly represent the cause-effect of regulation among a group of genes and its reconstruction is today a challenging computational problem. Several methods were proposed, but most of them require different input sources to provide an acceptable prediction. Thus, it is a great challenge to reconstruct a GRN only from temporal gene-expression data...
November 22, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29137603/clustertad-an-unsupervised-machine-learning-approach-to-detecting-topologically-associated-domains-of-chromosomes-from-hi-c-data
#7
Oluwatosin Oluwadare, Jianlin Cheng
BACKGROUND: With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-C technique can generate genome-wide chromosomal interaction (contact) data, which can be used to investigate the higher-level organization of chromosomes, such as Topologically Associated Domains (TAD), i.e., locally packed chromosome regions bounded together by intra chromosomal contacts...
November 14, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29136092/microrpm-a-microrna-prediction-model-based-only-on-plant-small-rna-sequencing-data
#8
Kuan-Chien Tseng, Yi-Fan Chiang-Hsieh, Hsuan Pai, Chi-Nga Chow, Shu-Chuan Lee, Han-Qin Zheng, Po-Li Kuo, Guan-Zhen Li, Yu-Cheng Hung, Na-Sheng Lin, Wen-Chi Chang
Motivation: MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine learning-based approaches have been developed to identify novel miRNAs from next generation sequencing (NGS) data. Typically, precursor/genomic sequences are required as references for most methods. However, the non-availability of genomic sequences is often a limitation in miRNA discovery in non-model plants...
November 9, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29109389/overlooked-short-toxin-like-proteins-a-shortcut-to-drug-design
#9
Michal Linial, Nadav Rappoport, Dan Ofer
Short stable peptides have huge potential for novel therapies and biosimilars. Cysteine-rich short proteins are characterized by multiple disulfide bridges in a compact structure. Many of these metazoan proteins are processed, folded, and secreted as soluble stable folds. These properties are shared by both marine and terrestrial animal toxins. These stable short proteins are promising sources for new drug development. We developed ClanTox (classifier of animal toxins) to identify toxin-like proteins (TOLIPs) using machine learning models trained on a large-scale proteomic database...
October 29, 2017: Toxins
https://www.readbyqxmd.com/read/29106441/orchid-a-novel-management-annotation-and-machine-learning-framework-for-analyzing-cancer-mutations
#10
Clinton L Cario, John S Witte
Motivation: As whole-genome tumor sequence and biological annotation datasets grow in size, number, and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments, and machine learning algorithms, there is also a need for the integration of functionality across frameworks. Results: We present orchid, a python based software package for the management, annotation, and machine learning of cancer mutations...
November 2, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29104167/protein-secondary-structure-prediction-based-on-the-fuzzy-support-vector-machine-with-the-hyperplane-optimization
#11
Shangxin Xie, Zhong Li, Hailong Hu
The prediction of the protein secondary structure is a crucial point in bioinformatics and related fields. In the last years, machine learning methods have become a valuable tool, achieving satisfactory results. However, the prediction accuracy needs to be further ameliorated. This paper proposes a new method based on an improved fuzzy support vector machine (FSVM) for the prediction of the secondary structure of proteins. Unlike traditional methods to set the membership function, it firstly constructs an approximate optimal separating hyperplane by iterating the class centers in the feature space...
November 2, 2017: Gene
https://www.readbyqxmd.com/read/29092009/machine-learning-annotation-of-human-branchpoints
#12
Bethany Signal, Brian S Gloss, Marcel E Dinger, Tim R Mercer
Motivation: The branchpoint element is required for the first lariat-forming reaction in splicing. However current catalogues of human branchpoints remain incomplete due to the difficulty in experimentally identifying these splicing elements. To address this limitation, we have developed a machine-learning algorithm - branchpointer - to identify branchpoint elements solely from gene annotations and genomic sequence. Results: Using branchpointer, we annotate branchpoint elements in 85% of human gene introns with sensitivity (61...
October 28, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29065906/biological-function-integrated-prediction-of-severe-radiographic-progression-in-rheumatoid-arthritis-a-nested-case-control-study
#13
Young Bin Joo, Yul Kim, Youngho Park, Kwangwoo Kim, Jeong Ah Ryu, Seunghun Lee, So-Young Bang, Hye-Soon Lee, Gwan-Su Yi, Sang-Cheol Bae
BACKGROUND: Radiographic progression is reported to be highly heritable in rheumatoid arthritis (RA). However, previous study using genetic loci showed an insufficient accuracy of prediction for radiographic progression. The aim of this study is to identify a biologically relevant prediction model of radiographic progression in patients with RA using a genome-wide association study (GWAS) combined with bioinformatics analysis. METHODS: We obtained genome-wide single nucleotide polymorphism (SNP) data for 374 Korean patients with RA using Illumina HumanOmni2...
October 25, 2017: Arthritis Research & Therapy
https://www.readbyqxmd.com/read/29061110/a-rapid-and-accurate-approach-for-prediction-of-interactomes-from-co-elution-data-prince
#14
R Greg Stacey, Michael A Skinnider, Nichollas E Scott, Leonard J Foster
BACKGROUND: An organism's protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes...
October 23, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29060464/activemotif-interactive-motif-discovery-with-human-feedback
#15
Younghoon Kim, Woonghee Lee, Keonwoo Kim
Motif detection, which is to discover short patterns involved in many important biological processes, has been recently raised as an important task in bioinformatics. The traditional algorithms to find a sequence motif have been developed using machine learning only without involving the experience and domain knowledge of human experts effectively. In this paper, we propose an interactive motif discovery system by introducing a new learning algorithm, by generalizing a well-known statistical motif model, whose inference can be shepherded by human feedback...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29040432/machine-learning-accelerates-md-based-binding-pose-prediction-between-ligands-and-proteins
#16
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
#17
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
#18
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...
November 1, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29036404/the-value-of-prior-knowledge-in-machine-learning-of-complex-network-systems
#19
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...
November 15, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29036382/musitedeep-a-deep-learning-framework-for-general-and-kinase-specific-phosphorylation-site-prediction
#20
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
keyword
keyword
45747
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"