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https://www.readbyqxmd.com/read/30338743/virtual-screening-meets-deep-learning
#1
Javier Perez-Sianes, Horacio Perez-Sanchez, Fernando Diaz
BACKGROUND: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer-aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies...
October 18, 2018: Current Computer-aided Drug Design
https://www.readbyqxmd.com/read/30336326/estimation-theory-and-neural-networks-revisited-rekf-and-rsvsf-as-optimization-techniques-for-deep-learning
#2
Mahmoud Ismail, Mina Attari, Saeid Habibi, Samir Ziada
Deep-Learning has become a leading strategy for artificial intelligence and is being applied in many fields due to its excellent performance that has surpassed human cognitive abilities in a number of classification and control problems (Ciregan, Meier, & Schmidhuber, 2012; Mnih et al., 2015). However, the training process of Deep-Learning is usually slow and requires high-performance computing, capable of handling large datasets. The optimization of the training method can improve the learning rate of the Deep-Learning networks and result in a higher performance while using the same number of training epochs (cycles)...
October 3, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/30334108/machine-meets-biology-a-primer-on-artificial-intelligence-in-cardiology-and-cardiac-imaging
#3
REVIEW
Matthew E Dilsizian, Eliot L Siegel
PURPOSE OF REVIEW: An understanding of the basics concepts of deep learning can be helpful in not only understanding the potential applications of this technique but also in critically reviewing literature in which neural networks are utilized for analysis and modeling. RECENT FINDINGS: The term "deep learning" has been applied to a subset of machine learning that utilizes a "neural network" and is often used interchangeably with "artificial intelligence...
October 18, 2018: Current Cardiology Reports
https://www.readbyqxmd.com/read/30325645/will-machine-learning-end-the-viability-of-radiology-as-a-thriving-medical-specialty
#4
Stephen Chan, Eliot L Siegel
There have been tremendous advances in artificial intelligence and machine learning within the past decade, especially in the application of deep learning to various challenges. These include advanced competitive games (such as Chess and Go), self-driving cars, speech recognition, and intelligent personal assistants. Rapid advances in computer vision for recognition of objects in pictures have led some individuals, including computer science experts and health care system experts in machine learning, to make predictions that machine learning algorithms will soon lead to the replacement of the radiologist...
October 16, 2018: British Journal of Radiology
https://www.readbyqxmd.com/read/30322916/comparing-continual-task-learning-in-minds-and-machines
#5
Timo Flesch, Jan Balaguer, Ronald Dekker, Hamed Nili, Christopher Summerfield
Humans can learn to perform multiple tasks in succession over the lifespan ("continual" learning), whereas current machine learning systems fail. Here, we investigated the cognitive mechanisms that permit successful continual learning in humans and harnessed our behavioral findings for neural network design. Humans categorized naturalistic images of trees according to one of two orthogonal task rules that were learned by trial and error. Training regimes that focused on individual rules for prolonged periods (blocked training) improved human performance on a later test involving randomly interleaved rules, compared with control regimes that trained in an interleaved fashion...
October 15, 2018: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/30322481/novel-quantitative-pet-techniques-for-clinical-decision-support-in-oncology
#6
REVIEW
Habib Zaidi, Abass Alavi, Issam El Naqa
Quantitative image analysis has deep roots in the usage of positron emission tomography (PET) in clinical and research settings to address a wide variety of diseases. It has been extensively employed to assess molecular and physiological biomarkers in vivo in healthy and disease states, in oncology, cardiology, neurology, and psychiatry. Quantitative PET allows relating the time-varying activity concentration in tissues/organs of interest and the basic functional parameters governing the biological processes being studied...
November 2018: Seminars in Nuclear Medicine
https://www.readbyqxmd.com/read/30315284/artificial-intelligence-assisted-gastroenterology-promises-and-pitfalls
#7
REVIEW
James K Ruffle, Adam D Farmer, Qasim Aziz
Technological advances in artificial intelligence (AI) represent an enticing opportunity to benefit gastroenterological practice. Moreover, AI, through machine or deep learning, permits the ability to develop predictive models from large datasets. Possibilities of predictive model development in machine learning are numerous dependent on the clinical question. For example, binary classifiers aim to stratify allocation to a categorical outcome, such as the presence or absence of a gastrointestinal disease. In addition, continuous variable fitting techniques can be used to predict quantity of a therapeutic response, thus offering a tool to predict which therapeutic intervention may be most beneficial to the given patient...
October 12, 2018: American Journal of Gastroenterology
https://www.readbyqxmd.com/read/30314421/use-of-a-deep-belief-network-for-small-high-level-abstraction-data-sets-using-artificial-intelligence-with-rule-extraction
#8
Yoichi Hayashi
We describe a simple method to transfer from weights in deep neural networks (NNs) trained by a deep belief network (DBN) to weights in a backpropagation NN (BPNN) in the recursive-rule eXtraction (Re-RX) algorithm with J48graft (Re-RX with J48graft) and propose a new method to extract accurate and interpretable classification rules for rating category data sets. We apply this method to the Wisconsin Breast Cancer Data Set (WBCD), the Mammographic Mass Data Set, and the Dermatology Dataset, which are small, high-abstraction data sets with prior knowledge...
October 12, 2018: Neural Computation
https://www.readbyqxmd.com/read/30306468/computer-aided-orthopaedic-surgery-state-of-the-art-and-future-perspectives
#9
Guoyan Zheng, Lutz-P Nolte
Introduced more than two decades ago, computer-aided orthopaedic surgery (CAOS) has emerged as a new and independent area, due to the importance of treatment of musculoskeletal diseases in orthopaedics and traumatology, increasing availability of different imaging modalities and advances in analytics and navigation tools. The aim of this chapter is to present the basic elements of CAOS devices and to review state-of-the-art examples of different imaging modalities used to create the virtual representations, of different position tracking devices for navigation systems, of different surgical robots, of different methods for registration and referencing, and of CAOS modules that have been realized for different surgical procedures...
2018: Advances in Experimental Medicine and Biology
https://www.readbyqxmd.com/read/30295070/artificial-intelligence-based-breast-cancer-nodal-metastasis-detection
#10
Yun Liu, Timo Kohlberger, Mohammad Norouzi, George E Dahl, Jenny L Smith, Arash Mohtashamian, Niels Olson, Lily H Peng, Jason D Hipp, Martin C Stumpe
CONTEXT.—: Nodal metastasis of a primary tumor influences therapy decisions for a variety of cancers. Histologic identification of tumor cells in lymph nodes can be laborious and error-prone, especially for small tumor foci. OBJECTIVE.—: To evaluate the application and clinical implementation of a state-of-the-art deep learning-based artificial intelligence algorithm (LYmph Node Assistant or LYNA) for detection of metastatic breast cancer in sentinel lymph node biopsies...
October 8, 2018: Archives of Pathology & Laboratory Medicine
https://www.readbyqxmd.com/read/30294501/diagnostic-performance-of-deep-learning-algorithms-applied-to-three-common-diagnoses-in-dermatopathology
#11
Thomas George Olsen, B Hunter Jackson, Theresa Ann Feeser, Michael N Kent, John C Moad, Smita Krishnamurthy, Denise D Lunsford, Rajath E Soans
Background: Artificial intelligence is advancing at an accelerated pace into clinical applications, providing opportunities for increased efficiency, improved accuracy, and cost savings through computer-aided diagnostics. Dermatopathology, with emphasis on pattern recognition, offers a unique opportunity for testing deep learning algorithms. Aims: This study aims to determine the accuracy of deep learning algorithms to diagnose three common dermatopathology diagnoses...
2018: Journal of Pathology Informatics
https://www.readbyqxmd.com/read/30276245/hunting-for-organic-molecules-with-artificial-intelligence-molecules-optimized-for-desired-excitation-energies
#12
Masato Sumita, Xiufeng Yang, Shinsuke Ishihara, Ryo Tamura, Koji Tsuda
This work presents a proof-of-concept study in artificial-intelligence-assisted (AI-assisted) chemistry where a machine-learning-based molecule generator is coupled with density functional theory (DFT) calculations, synthesis, and measurement. Although deep-learning-based molecule generators have shown promise, it is unclear to what extent they can be useful in real-world materials development. To assess the reliability of AI-assisted chemistry, we prepared a platform using a molecule generator and a DFT simulator, and attempted to generate novel photofunctional molecules whose lowest excited states lie at desired energetic levels...
September 26, 2018: ACS Central Science
https://www.readbyqxmd.com/read/30275284/an-automated-grading-system-for-detection-of-vision-threatening-referable-diabetic-retinopathy-on-the-basis-of-color-fundus-photographs
#13
Zhixi Li, Stuart Keel, Chi Liu, Yifan He, Wei Meng, Jane Scheetz, Pei Ying Lee, Jonathan Shaw, Daniel Ting, Tien Wong, Hugh Taylor, Robert Chang, Mingguang He
OBJECTIVE: The goal of this study was to describe the development and validation of an artificial intelligence-based, deep learning algorithm (DLA) for the detection of referable diabetic retinopathy (DR). RESEARCH DESIGN AND METHODS: A DLA using a convolutional neural network was developed for automated detection of vision-threatening referable DR (preproliferative DR or worse, diabetic macular edema, or both). The DLA was tested by using a set of 106,244 nonstereoscopic retinal images...
October 1, 2018: Diabetes Care
https://www.readbyqxmd.com/read/30253801/development-and-validation-of-an-endoscopic-images-based-deep-learning-model-for-detection-with-nasopharyngeal-malignancies
#14
Chaofeng Li, Bingzhong Jing, Liangru Ke, Bin Li, Weixiong Xia, Caisheng He, Chaonan Qian, Chong Zhao, Haiqiang Mai, Mingyuan Chen, Kajia Cao, Haoyuan Mo, Ling Guo, Qiuyan Chen, Linquan Tang, Wenze Qiu, Yahui Yu, Hu Liang, Xinjun Huang, Guoying Liu, Wangzhong Li, Lin Wang, Rui Sun, Xiong Zou, Shanshan Guo, Peiyu Huang, Donghua Luo, Fang Qiu, Yishan Wu, Yijun Hua, Kuiyuan Liu, Shuhui Lv, Jingjing Miao, Yanqun Xiang, Ying Sun, Xiang Guo, Xing Lv
BACKGROUND: Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for malignancy identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning. METHODS: An endoscopic images-based nasopharyngeal malignancy detection model (eNPM-DM) consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation...
September 25, 2018: Cancer communications
https://www.readbyqxmd.com/read/30232585/artificial-intelligence-using-neural-network-architecture-for-radiology-ainnar-classification-of-mr-imaging-sequences
#15
Tomoyuki Noguchi, Daichi Higa, Takashi Asada, Yusuke Kawata, Akihiro Machitori, Yoshitaka Shida, Takashi Okafuji, Kota Yokoyama, Fumiya Uchiyama, Tsuyoshi Tajima
PURPOSE: The confusion of MRI sequence names could be solved if MR images were automatically identified after image data acquisition. We revealed the ability of deep learning to classify head MRI sequences. MATERIALS AND METHODS: Seventy-eight patients with mild cognitive impairment (MCI) having apparently normal head MR images and 78 intracranial hemorrhage (ICH) patients with morphologically deformed head MR images were enrolled. Six imaging protocols were selected to be performed: T2-weighted imaging, fluid attenuated inversion recovery imaging, T2-star-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient mapping, and source images of time-of-flight magnetic resonance angiography...
September 19, 2018: Japanese Journal of Radiology
https://www.readbyqxmd.com/read/30231499/deep-learning-in-drug-discovery-and-medicine-scratching-the-surface
#16
REVIEW
Dibyendu Dana, Satishkumar V Gadhiya, Luce G St Surin, David Li, Farha Naaz, Quaisar Ali, Latha Paka, Michael A Yamin, Mahesh Narayan, Itzhak D Goldberg, Prakash Narayan
The practice of medicine is ever evolving. Diagnosing disease, which is often the first step in a cure, has seen a sea change from the discerning hands of the neighborhood physician to the use of sophisticated machines to use of information gleaned from biomarkers obtained by the most minimally invasive of means. The last 100 or so years have borne witness to the enormous success story of allopathy, a practice that found favor over earlier practices of medical purgatory and homeopathy. Nevertheless, failures of this approach coupled with the omics and bioinformatics revolution spurred precision medicine, a platform wherein the molecular profile of an individual patient drives the selection of therapy...
September 18, 2018: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://www.readbyqxmd.com/read/30178025/clinical-implications-and-challenges-of-artificial-intelligence-and-deep-learning
#17
EDITORIAL
William W Stead
No abstract text is available yet for this article.
September 18, 2018: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/30171033/can-deep-learning-improve-genomic-prediction-of-complex-human-traits
#18
Pau Bellot, Gustavo de Los Campos, Miguel Pérez-Enciso
The genetic analysis of complex traits does not escape the current excitement around artificial intelligence, including a renewed interest in 'deep learning' (DL) techniques such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). However, the performance of DL for genomic prediction of complex human traits has not been comprehensively tested. To provide an evaluation of MLPs and CNNs, we used data from distantly related white Caucasian individuals (n ∼ 100k individuals, m ∼ 500k SNPs, k=1000) of the interim release of the UK biobank...
August 31, 2018: Genetics
https://www.readbyqxmd.com/read/30169563/binding-site-matching-in-rational-drug-design-algorithms-and-applications
#19
Misagh Naderi, Jeffrey Mitchell Lemoine, Rajiv Gandhi Govindaraj, Omar Zade Kana, Wei Pan Feinstein, Michal Brylinski
Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly...
August 31, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30145103/artificial-intelligence-in-stroke-care-deep-learning-or-superficial-insight
#20
David S Liebeskind
No abstract text is available yet for this article.
September 2018: EBioMedicine
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