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https://www.readbyqxmd.com/read/28087243/using-deep-learning-to-investigate-the-neuroimaging-correlates-of-psychiatric-and-neurological-disorders-methods-and-applications
#1
REVIEW
Sandra Vieira, Walter H L Pinaya, Andrea Mechelli
Deep learning (DL) is a family of machine learning methods that has gained considerable attention in the scientific community, breaking benchmark records in areas such as speech and visual recognition. DL differs from conventional machine learning methods by virtue of its ability to learn the optimal representation from the raw data through consecutive nonlinear transformations, achieving increasingly higher levels of abstraction and complexity. Given its ability to detect abstract and complex patterns, DL has been applied in neuroimaging studies of psychiatric and neurological disorders, which are characterised by subtle and diffuse alterations...
January 10, 2017: Neuroscience and Biobehavioral Reviews
https://www.readbyqxmd.com/read/28086747/microrna-based-pan-cancer-diagnosis-and-treatment-recommendation
#2
Nikhil Cheerla, Olivier Gevaert
BACKGROUND: The current state-of-the-art in cancer diagnosis and treatment is not ideal; diagnostic tests are accurate but invasive, and treatments are "one-size fits-all" instead of being personalized. Recently, miRNA's have garnered significant attention as cancer biomarkers, owing to their ease of access (circulating miRNA in the blood) and stability. There have been many studies showing the effectiveness of miRNA data in diagnosing specific cancer types, but few studies explore the role of miRNA in predicting treatment outcome...
January 13, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28079723/toward-automating-hiv-identification-machine-learning-for-rapid-identification-of-hiv-related-social-media-data
#3
Sean D Young, Wenchao Yu, Wei Wang
INTRODUCTION: "Social big data" from technologies such as social media, wearable devices, and online searches continue to grow and can be used as tools for HIV research. Although researchers can uncover patterns and insights associated with HIV trends and transmission, the review process is time consuming and resource intensive. Machine learning methods derived from computer science might be used to assist HIV domain experts by learning how to rapidly and accurately identify patterns associated with HIV from a large set of social data...
February 1, 2017: Journal of Acquired Immune Deficiency Syndromes: JAIDS
https://www.readbyqxmd.com/read/28077889/differential-diagnosis-of-erythmato-squamous-diseases-using-classification-and-regression-tree
#4
Keivan Maghooli, Mostafa Langarizadeh, Leila Shahmoradi, Mahdi Habibi-Koolaee, Mohamad Jebraeily, Hamid Bouraghi
INTRODUCTION: Differential diagnosis of Erythmato-Squamous Diseases (ESD) is a major challenge in the field of dermatology. The ESD diseases are placed into six different classes. Data mining is the process for detection of hidden patterns. In the case of ESD, data mining help us to predict the diseases. Different algorithms were developed for this purpose. OBJECTIVE: we aimed to use the Classification and Regression Tree (CART) to predict differential diagnosis of ESD...
October 2016: Acta Informatica Medica: AIM
https://www.readbyqxmd.com/read/28077404/high-throughput-sequencing-of-the-t-cell-receptor-repertoire-pitfalls-and-opportunities
#5
James M Heather, Mazlina Ismail, Theres Oakes, Benny Chain
T-cell specificity is determined by the T-cell receptor, a heterodimeric protein coded for by an extremely diverse set of genes produced by imprecise somatic gene recombination. Massively parallel high-throughput sequencing allows millions of different T-cell receptor genes to be characterized from a single sample of blood or tissue. However, the extraordinary heterogeneity of the immune repertoire poses significant challenges for subsequent analysis of the data. We outline the major steps in processing of repertoire data, considering low-level processing of raw sequence files and high-level algorithms, which seek to extract biological or pathological information...
January 10, 2017: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/28076801/msmbuilder-statistical-models-for-biomolecular-dynamics
#6
Matthew P Harrigan, Mohammad M Sultan, Carlos X Hernández, Brooke E Husic, Peter Eastman, Christian R Schwantes, Kyle A Beauchamp, Robert T McGibbon, Vijay S Pande
MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis...
January 10, 2017: Biophysical Journal
https://www.readbyqxmd.com/read/28075373/visual-object-tracking-based-on-cross-modality-gaussian-bernoulli-deep-boltzmann-machines-with-rgb-d-sensors
#7
Mingxin Jiang, Zhigeng Pan, Zhenzhou Tang
Visual object tracking technology is one of the key issues in computer vision. In this paper, we propose a visual object tracking algorithm based on cross-modality featuredeep learning using Gaussian-Bernoulli deep Boltzmann machines (DBM) with RGB-D sensors. First, a cross-modality featurelearning network based on aGaussian-Bernoulli DBM is constructed, which can extract cross-modality features of the samples in RGB-D video data. Second, the cross-modality features of the samples are input into the logistic regression classifier, andthe observation likelihood model is established according to the confidence score of the classifier...
January 10, 2017: Sensors
https://www.readbyqxmd.com/read/28075331/predictive-modeling-of-respiratory-tumor-motion-for-real-time-prediction-of-baseline-shifts
#8
Arvind Balasubramanian, Rittika Shamsuddin, Balakrishnan Prabhakaran, Amit Sawant
Baseline shifts in respiratory patterns can result in significant spatiotemporal changes in patient anatomy (compared to that captured during simulation), in turn, causing geometric and dosimetric errors in the administration of thoracic and abdominal radiotherapy. We propose predictive modeling of the tumor motion trajectories for predicting a baseline shift ahead of its occurrence. The key idea is to use the features of the tumor motion trajectory over a 1-minute window, and predict the occurrence of a baseline shift in the 5 seconds that immediately follow (lookahead window)...
January 11, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/28074633/a-computational-interactome-for-prioritizing-genes-associated-with-complex-agronomic-traits-in-rice
#9
Shiwei Liu, Yihui Liu, Jiawei Zhao, Shitao Cai, Hongmei Qian, Kaijing Zuo, Lingxia Zhao, Lida Zhang
Rice is one of the most important staple foods for more than half of the world's population. Many rice traits are quantitative, complex and controlled by multiple interacting genes. Thus, a full understanding of genetic relationships will be critical to systematically identify genes controlling agronomic traits. We developed a genome-wide rice protein-protein interaction network (RicePPINet, http://netbio.sjtu.edu.cn/riceppinet/) using machine-learning with structural relationship and functional information...
January 11, 2017: Plant Journal: for Cell and Molecular Biology
https://www.readbyqxmd.com/read/28074598/prediction-of-skin-sensitization-potency-using-machine-learning-approaches
#10
Qingda Zang, Michael Paris, David M Lehmann, Shannon Bell, Nicole Kleinstreuer, David Allen, Joanna Matheson, Abigail Jacobs, Warren Casey, Judy Strickland
The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non-sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes...
January 10, 2017: Journal of Applied Toxicology: JAT
https://www.readbyqxmd.com/read/28073761/seeing-the-trees-through-the-forest-sequence-based-homo-and-heteromeric-protein-protein-interaction-sites-prediction-using-random-forest
#11
Qingzhen Hou, Paul De Geest, Wim F Vranken, Jaap Heringa, K Anton Feenstra
MOTIVATION: Genome sequencing is producing an ever-increasing amount of associated protein sequences. Few of these sequences have experimentally validated annotations, however, and computational predictions are becoming increasingly successful in producing such annotations. One key challenge remains the prediction of the amino acids in a given protein sequence that are involved in proteinprotein interactions. Such predictions are typically based on machine learning methods that take advantage of the properties and sequence positions of amino acids that are known to be involved in interaction...
January 10, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28073590/objective-detection-of-apoptosis-in-rat-renal-tissue-sections-using-light-microscopy-and-free-image-analysis-software-with-subsequent-machine-learning-detection-of-apoptosis-in-renal-tissue
#12
Nayana Damiani Macedo, Aline Rodrigues Buzin, Isabela Bastos Binotti Abreu de Araujo, Breno Valentim Nogueira, Tadeu Uggere de Andrade, Denise Coutinho Endringer, Dominik Lenz
OBJECTIVE: The current study proposes an automated machine learning approach for the quantification of cells in cell death pathways according to DNA fragmentation. METHODS: A total of 17 images of kidney histological slide samples from male Wistar rats were used. The slides were photographed using an Axio Zeiss Vert.A1 microscope with a 40x objective lens coupled with an Axio Cam MRC Zeiss camera and Zen 2012 software. The images were analyzed using CellProfiler (version 2...
December 28, 2016: Tissue & Cell
https://www.readbyqxmd.com/read/28072816/systematic-analysis-of-transcriptional-and-post-transcriptional-regulation-of-metabolism-in-yeast
#13
Emanuel Gonçalves, Zrinka Raguz Nakic, Mattia Zampieri, Omar Wagih, David Ochoa, Uwe Sauer, Pedro Beltrao, Julio Saez-Rodriguez
Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kinases...
January 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28072555/on-constructing-ensembles-for-combinatorial-optimisation
#14
Emma Hart, Kevin Sim
Although the use of ensemble methods in machine-learning is ubiquitous due to their proven ability to outperform their constituent algorithms, ensembles of optimisation algorithms have received relatively little attention. Existing approaches lag behind machine-learning in both theory and practice, with no principled design guide-lines available. In this paper, we address fundamental questions regarding ensemble composition in optimisation using the domain of bin-packing as a example; in particular we investigate the trade-off between accuracy and diversity, and whether diversity metrics can be used as a proxy for constructing an ensemble, proposing a number of novel metrics for comparing algorithm diversity...
January 10, 2017: Evolutionary Computation
https://www.readbyqxmd.com/read/28071176/using-machine-learning-to-predict-laboratory-test-results
#15
Edmund E Wilkes
No abstract text is available yet for this article.
November 2016: Annals of Clinical Biochemistry
https://www.readbyqxmd.com/read/28070986/predicting-severity-of-disease-causing-variants
#16
Abhishek Niroula, Mauno Vihinen
Most diseases, including those of genetic origin, express a continuum of severity. Clinical interventions for numerous diseases are based on the severity of the phenotype. Predicting severity due to genetic variants could facilitate diagnosis and choice of therapy. Although computational predictions have been used as evidence for classifying the disease-relevance of genetic variants, special tools for predicting disease severity in large scale are missing. Here, we manually curated a dataset containing variants leading to severe and less severe phenotypes and studied the abilities of variation impact predictors to distinguish between them...
January 9, 2017: Human Mutation
https://www.readbyqxmd.com/read/28070484/deep-learning-predictions-of-survival-based-on-mri-in-amyotrophic-lateral-sclerosis
#17
Hannelore K van der Burgh, Ruben Schmidt, Henk-Jan Westeneng, Marcel A de Reus, Leonard H van den Berg, Martijn P van den Heuvel
Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic...
2017: NeuroImage: Clinical
https://www.readbyqxmd.com/read/28068461/a-new-prediction-model-for-evaluating-treatment-resistant-depression
#18
Alexander Kautzky, Pia Baldinger-Melich, Georg S Kranz, Thomas Vanicek, Daniel Souery, Stuart Montgomery, Julien Mendlewicz, Joseph Zohar, Alessandro Serretti, Rupert Lanzenberger, Siegfried Kasper
OBJECTIVE: Despite a broad arsenal of antidepressants, about a third of patients suffering from major depressive disorder (MDD) do not respond sufficiently to adequate treatment. Using the data pool of the Group for the Study of Resistant Depression and machine learning, we intended to draw new insights featuring 48 clinical, sociodemographic, and psychosocial predictors for treatment outcome. METHOD: Patients were enrolled starting from January 2000 and diagnosed according to DSM-IV...
January 3, 2017: Journal of Clinical Psychiatry
https://www.readbyqxmd.com/read/28068401/advantages-of-synthetic-noise-and-machine-learning-for-analyzing-radioecological-data-sets
#19
Igor Shuryak
The ecological effects of accidental or malicious radioactive contamination are insufficiently understood because of the hazards and difficulties associated with conducting studies in radioactively-polluted areas. Data sets from severely contaminated locations can therefore be small. Moreover, many potentially important factors, such as soil concentrations of toxic chemicals, pH, and temperature, can be correlated with radiation levels and with each other. In such situations, commonly-used statistical techniques like generalized linear models (GLMs) may not be able to provide useful information about how radiation and/or these other variables affect the outcome (e...
2017: PloS One
https://www.readbyqxmd.com/read/28067976/single-nucleobase-identification-using-biophysical-signatures-from-nanoelectronic-quantum-tunneling
#20
Lee E Korshoj, Sepideh Afsari, Sajida Khan, Anushree Chatterjee, Prashant Nagpal
Nanoelectronic DNA sequencing can provide an important alternative to sequencing-by-synthesis by reducing sample preparation time, cost, and complexity as a high-throughput next-generation technique with accurate single-molecule identification. However, sample noise and signature overlap continue to prevent high-resolution and accurate sequencing results. Probing the molecular orbitals of chemically distinct DNA nucleobases offers a path for facile sequence identification, but molecular entropy (from nucleotide conformations) makes such identification difficult when relying only on the energies of lowest-unoccupied and highest-occupied molecular orbitals (LUMO and HOMO)...
January 9, 2017: Small
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