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https://www.readbyqxmd.com/read/28228010/selective-fusion-of-heterogeneous-classifiers-for-predicting-substrates-of-membrane-transporters
#1
Naeem Shaikh, Mahesh Sharma, Prabha Garg
Membrane transporters play a crucial role in determining fate of administered drugs in a biological system. Early identification of plausible transporters for a drug molecule can provide insights into its therapeutic, pharmacokinetic and toxicological profile. In the present study, predictive models for classifying small molecules into substrates and non-substrates of various pharmaceutically important membrane transporters are developed using QSAR and proteochemometric (PCM) approaches. For this purpose, 4575 substrate interactions for these transporters were collected from Metabolism and Transport Database (Metrabase) and literature...
February 23, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28227670/diffuse-reflectance-spectroscopy-can-differentiate-high-grade-and-low-grade-prostatic-carcinoma
#2
Priya N Werahera, Edward A Jasion, E David Crawford, M Scott Lucia, Adrie van Bokhoven, Holly T Sullivan, Fernando J Kim, Paul D Maroni, J David Port, John W Daily, Francisco G La Rosa, Priya N Werahera, Edward A Jasion, E David Crawford, M Scott Lucia, Adrie van Bokhoven, Holly T Sullivan, Fernando J Kim, Paul D Maroni, J David Port, John W Daily, Francisco G La Rosa, John W Daily, Adrie Van Bokhoven, E David Crawford, J David Port, Priya N Werahera, M Scott Lucia, Holly T Sullivan, Paul D Maroni, Edward A Jasion, Francisco G La Rosa, Fernando J Kim
Prostate tumors are graded by the revised Gleason Score (GS) which is the sum of the two predominant Gleason grades present ranging from 6-10. GS 6 cancer exclusively with Gleason grade 3 is designated as low grade (LG) and correlates with better clinical prognosis for patients. GS >7 cancer with at least one of the Gleason grades 4 and 5 is designated as HG indicate worse prognosis for patients. Current transrectal ultrasound guided prostate biopsies often fail to correctly diagnose HG prostate cancer due to sampling errors...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227383/an-empirical-study-of-parallel-solutions-for-glcm-calculation-of-diffraction-images
#3
John Dixon, Junhua Ding, John Dixon, Junhua Ding, Junhua Ding, John Dixon
Feature calculation of large amount of images is time consuming. The GPU based CUDA framework offers an affordable solution for calculating image features in parallel. The research focused on an empirical study of different implementations of a general-purpose GPU-based solution for calculating Gray-Level Co-occurrence Matrices (GLCM) and associated features of diffraction images of biological cells. The GLCM features calculated from the diffraction images are used for rapid cell classification with the machine learning algorithm Support Vector Machine (SVM)...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227226/learning-approaches-to-improve-prediction-of-drug-sensitivity-in-breast-cancer-patients
#4
Turki Turki, Zhi Wei, Turki Turki, Zhi Wei, Turki Turki, Zhi Wei
Predicting drug response to cancer disease is an important problem in modern clinical oncology that attracted increasing recent attention from various domains such as computational biology, machine learning, and data mining. Cancer patients respond differently to each cancer therapy owing to disease diversity, genetic factors, and environmental causes. Thus, oncologists aim to identify the effective therapies for cancer patients and avoid adverse drug reactions in patients. By predicting the drug response to cancer, oncologists gain full understanding of the effective treatments on each patient, which leads to better personalized treatment...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226942/power-transfer-for-rotating-medical-machine
#5
A Sofia, A C Tavilla, R Gardenghi, D Nicolis, I Stefanini, A Sofia, A C Tavilla, R Gardenghi, D Nicolis, I Stefanini, A C Tavilla, R Gardenghi, I Stefanini, A Sofia, D Nicolis
Very often biological tissues need to be treated inside of a biomedical centrifuge even during the centrifugation step without process interruption. In this paper an advantageous energy transfer method capable of providing sufficient electric power for the rotating and active part is presented.
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226457/forecasting-nanoparticle-toxicity-using-nonlinear-predictive-regressor-learning-systems
#6
N Toschi, S Ciulli, S Diciotti, A Duggento, M Guerrisi, A Magrini, L Campagnolo, A Pietroiusti, N Toschi, S Ciulli, S Diciotti, A Duggento, M Guerrisi, A Magrini, L Campagnolo, A Pietroiusti, M Guerrisi, S Ciulli, L Campagnolo, S Diciotti, A Magrini, A Pietroiusti, A Duggento, N Toschi
Nanoparticle (NP) toxicity is determined by a vast number of topological, sterical, physico-chemical as well as biological properties, rendering a priori evaluation of the effect of NP on biological tissue as arduous as it is necessary and urgent. We aimed at mining the HORIZON 2020 MODENA COST NP cytotoxicity database through nonlinear predictive regressor learning systems in order to assess the power of available NP descriptors and assay characteristics in predicting NP toxicity. Specifically, we assessed the results of cytotoxicity assays performed on 57 NP and trained two different nonlinear regressors (Support Vector Regressors [SVR] with polynomical kernels and Radial Basis Function [RBF] regressors) within a nested-cross validation scheme for parameter optimization to predict toxicity as quantified by EC25, EC50 and slope while using the regressional ReliefF algorithm (RReliefF) for feature selection...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28221361/dynamic-control-of-chirality-and-self-assembly-of-double-stranded-helicates-with-light
#7
Depeng Zhao, Thomas van Leeuwen, Jinling Cheng, Ben L Feringa
Helicity switching in biological and artificial systems is a fundamental process that allows for the dynamic control of structures and their functions. In contrast to chemical approaches to responsive behaviour in helicates, the use of light as an external stimulus offers unique opportunities to invert the chirality of helical structures in a non-invasive manner with high spatiotemporal precision. Here, we report that unidirectional rotary motors with connecting oligobipyridyl ligands, which can dynamically change their chirality upon irradiation, assemble into metal helicates that are responsive to light...
March 2017: Nature Chemistry
https://www.readbyqxmd.com/read/28217262/the-effects-of-shared-information-on-semantic-calculations-in-the-gene%C3%A2-ontology
#8
Paul W Bible, Hong-Wei Sun, Maria I Morasso, Rasiah Loganantharaj, Lai Wei
The structured vocabulary that describes gene function, the gene ontology (GO), serves as a powerful tool in biological research. One application of GO in computational biology calculates semantic similarity between two concepts to make inferences about the functional similarity of genes. A class of term similarity algorithms explicitly calculates the shared information (SI) between concepts then substitutes this calculation into traditional term similarity measures such as Resnik, Lin, and Jiang-Conrath. Alternative SI approaches, when combined with ontology choice and term similarity type, lead to many gene-to-gene similarity measures...
2017: Computational and Structural Biotechnology Journal
https://www.readbyqxmd.com/read/28215473/machine-learning-based-prediction-of-adverse-drug-effects-an-example-of-seizure-inducing-compounds
#9
Mengxuan Gao, Hideyoshi Igata, Aoi Takeuchi, Kaoru Sato, Yuji Ikegaya
Various biological factors have been implicated in convulsive seizures, involving side effects of drugs. For the preclinical safety assessment of drug development, it is difficult to predict seizure-inducing side effects. Here, we introduced a machine learning-based in vitro system designed to detect seizure-inducing side effects. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices, while 14 drugs were bath-perfused at 5 different concentrations each. For each experimental condition, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning...
January 28, 2017: Journal of Pharmacological Sciences
https://www.readbyqxmd.com/read/28210651/analysis-of-platelet-rich-plasma-extraction-variations-in-platelet-and-blood-components-between-4-common-commercial-kits
#10
Jane Fitzpatrick, Max K Bulsara, Paul Robert McCrory, Martin D Richardson, Ming Hao Zheng
BACKGROUND: Platelet-rich plasma (PRP) has been extensively used as a treatment in tissue healing in tendinopathy, muscle injury, and osteoarthritis. However, there is variation in methods of extraction, and this produces different types of PRP. PURPOSE: To determine the composition of PRP obtained from 4 commercial separation kits, which would allow assessment of current classification systems used in cross-study comparisons. STUDY DESIGN: Controlled laboratory study...
January 2017: Orthopaedic Journal of Sports Medicine
https://www.readbyqxmd.com/read/28199113/quantitative-characterization-of-domain-motions-in-molecular-machines
#11
Suvrajit Maji, Rezvan Shahoei, Klaus Schulten, Joachim Frank
The work of molecular machines such as the ribosome is accompanied by conformational changes, often characterized by relative motions of their domains. The method we have developed seeks to quantify these motions in a general way, facilitating comparisons of results obtained by different researchers. Typically there are multiple snapshots of a structure in the form of pdb coordinates resulting from flexible fitting of low-resolution density maps, from X-ray crystallography, or from molecular dynamics simulation trajectories...
February 15, 2017: Journal of Physical Chemistry. B
https://www.readbyqxmd.com/read/28198674/sequence-specific-bias-correction-for-rna-seq-data-using-recurrent-neural-networks
#12
Yao-Zhong Zhang, Rui Yamaguchi, Seiya Imoto, Satoru Miyano
BACKGROUND: The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures...
January 25, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28196722/comparison-of-models-for-predicting-the-changes-in-phytoplankton-community-composition-in-the-receiving-water-system-of-an-inter-basin-water-transfer-project
#13
Qinghui Zeng, Yi Liu, Hongtao Zhao, Mingdong Sun, Xuyong Li
Inter-basin water transfer projects might cause complex hydro-chemical and biological variation in the receiving aquatic ecosystems. Whether machine learning models can be used to predict changes in phytoplankton community composition caused by water transfer projects have rarely been studied. In the present study, we used machine learning models to predict the total algal cell densities and changes in phytoplankton community composition in Miyun reservoir caused by the middle route of the South-to-North Water Transfer Project (SNWTP)...
February 10, 2017: Environmental Pollution
https://www.readbyqxmd.com/read/28194222/intelligibility-evaluation-of-pathological-speech-through-multigranularity-feature-extraction-and-optimization
#14
Chunying Fang, Haifeng Li, Lin Ma, Mancai Zhang
Pathological speech usually refers to speech distortion resulting from illness or other biological insults. The assessment of pathological speech plays an important role in assisting the experts, while automatic evaluation of speech intelligibility is difficult because it is usually nonstationary and mutational. In this paper, we carry out an independent innovation of feature extraction and reduction, and we describe a multigranularity combined feature scheme which is optimized by the hierarchical visual method...
2017: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/28193032/transcription-factor-response-elements-on-tip-a-sensitive-approach-for-large-scale-endogenous-transcription-factor-quantitative-identification
#15
Wenhao Shi, Kai Li, Lei Song, Mingwei Liu, Yunzhi Wang, Wanlin Liu, Xia Xia, Zhaoyu Qin, Bei Zhen, Yi Wang, Fuchu He, Jun Qin, Chen Ding
The ability to map endogenous transcription factors (TFs) DNA binding activity at the proteome scale will greatly enhance our understanding of various biological processes. Here we report a highly sensitive, rapid, and high-throughput approach, transcription factor response elements on tip-mass spectrometry (TOT-MS), that allows for quantitative measurement of endogenous TFs. A total of 150 TFs from 1 μg of nuclear extracts can be quantified with single shot mass spectrometry detection in 1 h of machine time...
December 20, 2016: Analytical Chemistry
https://www.readbyqxmd.com/read/28190869/single-molecule-detection-thermal-fluctuation-and-life
#16
Toshio Yanagida, Yoshiharu Ishii
Single molecule detection has contributed to our understanding of the unique mechanisms of life. Unlike artificial man-made machines, biological molecular machines integrate thermal noises rather than avoid them. For example, single molecule detection has demonstrated that myosin motors undergo biased Brownian motion for stepwise movement and that single protein molecules spontaneously change their conformation, for switching to interactions with other proteins, in response to thermal fluctuation. Thus, molecular machines have flexibility and efficiency not seen in artificial machines...
2017: Proceedings of the Japan Academy. Series B, Physical and Biological Sciences
https://www.readbyqxmd.com/read/28186207/complexity-of-the-ruminococcus-flavefaciens-fd-1-cellulosome-reflects-an-expansion-of-family-related-protein-protein-interactions
#17
Vered Israeli-Ruimy, Pedro Bule, Sadanari Jindou, Bareket Dassa, Sarah Moraïs, Ilya Borovok, Yoav Barak, Michal Slutzki, Yuval Hamberg, Vânia Cardoso, Victor D Alves, Shabir Najmudin, Bryan A White, Harry J Flint, Harry J Gilbert, Raphael Lamed, Carlos M G A Fontes, Edward A Bayer
Protein-protein interactions play a vital role in cellular processes as exemplified by assembly of the intricate multi-enzyme cellulosome complex. Cellulosomes are assembled by selective high-affinity binding of enzyme-borne dockerin modules to repeated cohesin modules of structural proteins termed scaffoldins. Recent sequencing of the fiber-degrading Ruminococcus flavefaciens FD-1 genome revealed a particularly elaborate cellulosome system. In total, 223 dockerin-bearing ORFs potentially involved in cellulosome assembly and a variety of multi-modular scaffoldins were identified, and the dockerins were classified into six major groups...
February 10, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28185545/mirnatip-a-som-based-mirna-target-interactions-predictor
#18
Antonino Fiannaca, Massimo La Rosa, Laura La Paglia, Riccardo Rizzo, Alfonso Urso
BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNA sequences with regulatory functions to post-transcriptional level for several biological processes, such as cell disease progression and metastasis. MiRNAs interact with target messenger RNA (mRNA) genes by base pairing. Experimental identification of miRNA target is one of the major challenges in cancer biology because miRNAs can act as tumour suppressors or oncogenes by targeting different type of targets. The use of machine learning methods for the prediction of the target genes is considered a valid support to investigate miRNA functions and to guide related wet-lab experiments...
September 22, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/28185019/biological-ageing-and-clinical-consequences-of-modern-technology
#19
Marios Kyriazis
The pace of technology is steadily increasing, and this has a widespread effect on all areas of health and society. When we interact with this technological environment we are exposed to a wide variety of new stimuli and challenges, which may modulate the stress response and thus change the way we respond and adapt. In this Opinion paper I will examine certain aspects of the human-computer interaction with regards to health and ageing. There are practical, everyday effects which also include social and cultural elements...
February 9, 2017: Biogerontology
https://www.readbyqxmd.com/read/28182019/a-modular-approach-to-the-design-fabrication-and-characterization-of-muscle-powered-biological-machines
#20
Ritu Raman, Caroline Cvetkovic, Rashid Bashir
Biological machines consisting of cells and biomaterials have the potential to dynamically sense, process, respond, and adapt to environmental signals in real time. As a first step toward the realization of such machines, which will require biological actuators that can generate force and perform mechanical work, we have developed a method of manufacturing modular skeletal muscle actuators that can generate up to 1.7 mN (3.2 kPa) of passive tension force and 300 μN (0.56 kPa) of active tension force in response to external stimulation...
March 2017: Nature Protocols
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