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Cyborg Intelligence: Recent Progress and Future Directions

IEEE INTELLIGENT SYSTEMS 2016年第6期31卷 44-50页

作者： Wu, Zhaohui Zhou, Yongdi Shi, Zhongzhi Zhang, Changshui Li, Guanglin Zheng, Xiaoxiang Zheng, Nenggan Pan, Gang Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China East China Normal Univ Key Lab Brain Funct Genom Shanghai Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Studies Shenzhen Peoples R China Zhejiang Univ Qiushi Acad Adv Studies Hangzhou Zhejiang Peoples R China Zhejiang Univ Dept Biomed Engn Hangzhou Zhejiang Peoples R China

The combination of biological and artificial intelligence is a promising methodology to construct a novel intelligent modality, proposed as cyborg intelligence. The hierarchical conceptual framework is based on the interaction and combination of comparable components of biological cognitive units and computing intelligent units. The authors extend the previous conceptual framework and focus on sensorimotor circuits to explore the representation and integration of sensation. They then present a cognitive computing model for brain-computer integration and design efficient machine-learning algorithms for neural signal decoding. They also propose biological reconstruction methods for sensorimotor circuits to not only restore but enhance functionalities with AI. They develop a series of demonstrating systems to validate the conceptual framework of cyborg intelligence and possibly herald bright prospects and high values in diversified aspects of theoretical research, engineering techniques, and clinical applications. Finally, the authors summarize the latest research trends and challenges, which they believe will further boost new scientific frontiers in cyborg intelligence.

关键词： Man-machine systems Biological information theory Computers Artificial intelligence Brain modeling Computational modeling Actuators INSPEC: Controlled Indexing learning (artificial intelligence) biocybernetics brain-computer interfaces cognitive systems INSPEC: Non-Controlled Indexing biological reconstruction method cyborg intelligence biological intelligence artificial intelligence intelligent modality hierarchical conceptual framework biological cognitive units computing intelligent units sensorimotor circuits sensation representation sensation integration cognitive computing model brain-computer integration machine-learning algorithm neural signal decoding Author Keywords intelligent systems cyborg intelligence computational framework multimodal sensory representation cognitive model machine learning biological functionality reconstruction brain machine integration

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Genome-enabled prediction using probabilistic neural network classifiers

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BMC GENOMICS 2016年第1期17卷 1-16页

作者： Manuel Gonzalez-Camacho, Juan Crossa, Jose Perez-Rodriguez, Paulino Ornella, Leonardo Gianola, Daniel Colegio Postgrad Campus Montecillo Mexico City 056230 DF Mexico Int Maize & Wheat Improvement Ctr CIMMYT Biometr & Stat Unit Apdo Postal 6-641 Mexico City *** DF Mexico NIDERA SEMILLAS SA Ruta 8 Km 376 RA-2600 Venado Tuerto Argentina Univ Wisconsin Dept Anim Sci Madison WI 53706 USA

Background: Multi-layer perceptron (MLP) and radial basis function neural networks (RBFNN) have been shown to be effective in genome-enabled prediction. Here, we evaluated and compared the classification performance of an MLP classifier versus that of a probabilistic neural network (PNN), to predict the probability of membership of one individual in a phenotypic class of interest, using genomic and phenotypic data as input variables. We used 16 maize and 17 wheat genomic and phenotypic datasets with different trait-environment combinations (sample sizes ranged from 290 to 300 individuals) with 1.4 k and 55 k SNP chips. Classifiers were tested using continuous traits that were categorized into three classes (upper, middle and lower) based on the empirical distribution of each trait, constructed on the basis of two percentiles (15-85 % and 30-70 %). We focused on the 15 and 30 % percentiles for the upper and lower classes for selecting the best individuals, as commonly done in genomic selection. Wheat datasets were also used with two classes. The criteria for assessing the predictive accuracy of the two classifiers were the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCpr). Parameters of both classifiers were estimated by optimizing the AUC for a specific class of interest. Results: The AUC and AUCpr criteria provided enough evidence to conclude that PNN was more accurate than MLP for assigning maize and wheat lines to the correct upper, middle or lower class for the complex traits analyzed. Results for the wheat datasets with continuous traits split into two and three classes showed that the performance of PNN with three classes was higher than with two classes when classifying individuals into the upper and lower (15 or 30 %) categories. Conclusions: The PNN classifier outperformed the MLP classifier in all 33 (maize and wheat) datasets when using AUC and AUCpr for selecting individuals of a specific c

关键词： Average precision Bayesian classifier Genomic selection machine-learning algorithm Multi-layer perceptron Non-parametric model

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Enhancing confidence in indirect analog/RF testing against the lack of correlation between regular parameters and indirect measurements

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MICROELECTRONICS JOURNAL 2014年第3期45卷 336-344页

作者： Ayari, Haithem Azais, Florence Bernard, Serge Comte, Mariane Kerzerho, Vincent Renovell, Michel Univ Montpellier 2 CNRS LIRMM F-34392 Montpellier France

The greedy specification testing remains mandatory for analog and radio frequency (RF) integrated circuits because of the accuracy of the sorting based on these measurements. Unfortunately, to be implemented, this kind of testing method often incurs very high costs (expensive instruments, long test time ...). A common approach, in the literature, is the so-called indirect/alternate test strategy. This strategy consists in deriving targeted specifications from low-cost Indirect Measurements (IMs). During the industrial test phase, the estimation of regular specifications using IMs is based on a correlation model that has been built previously, during a training phase. Despite the substantial test cost reduction offered by this strategy, its deployment in industry is limited, mainly because of a lack of confidence in the accuracy of estimations made by the correlation model. A solution to increase the confidence in the estimation of specifications using the indirect approach is to implement redundancy in the prediction phase. In this paper, we demonstrate that the redundancy implementation brings more than identifying rare misjudged circuits from a high-correlated model. Indeed redundancy massively increases the accuracy despite of the lack of accurate models that have been assumed in previous implementations of redundant indirect testing. This approach is illustrated on a real case study for which we have experimental measurements on a set of 10,000 devices. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Test Analog/RF integrated circuits Alternate test Indirect test Prediction error machine-learning algorithm

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Identification of Gene Signatures Used to Recognize Biological Characteristics of Gastric Cancer Upon Gene Expression Data

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BIOMARKER INSIGHTS 2014年 9卷 67-76页

作者： Yan, Zhi Luke, Brian T. Tsang, Shirley X. Xing, Rui Pan, Yuanming Liu, Yixuan Wang, Jinlian Geng, Tao Li, Jiangeng Lu, Youyong Peking Univ Canc Hosp Key Lab Carcinogenesis & Translat Res Minist Educ Lab Mol Oncol Beijing Peoples R China Peking Univ Inst Key Lab Carcinogenesis & Translat Res Minist Educ Lab Mol Oncol Beijing Peoples R China Adv Biomed Comp Ctr Frederick Natl Lab Canc Res Frederick MD 21702 USA BioMatrix Rockville MD 20850 USA Georgetown Univ Lombardi Comprehens Canc Ctr Washington DC USA Beijing Univ Technol Coll Elect Informat & Control Engn Beijing Peoples R China

High-throughput gene expression microarrays can be examined by machine-learning algorithms to identify gene signatures that recognize the biological characteristics of specific human diseases, including cancer, with high sensitivity and specificity. A previous study compared 20 astric cancer (GC) samples against 20 normal tissue (NT) samples and identified 1,519 differentially expressed genes (DEGs). In this study, Classification Information Index (CII), Information Gain Index (IGI), and RELIEF algorithms are used to mine the previously reported gene expression profiling data. In all, 29 of these genes are identified by all three algorithms and are treated as GC candidate biomarkers. Three biomarkers, COL1A2, ATP4B, and HADHSC, are selected and further examined using quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) staining in two independent sets of GC and normal adjacent tissue (NAT) samples. Our study shows that COL1A2 and HADHSC are the two best biomarkers from the microarray data, distinguishing all GC from the NT, whereas ATP4B is diagnostically significant in lab tests because of its wider range of fold-changes in expression. Herein, a data-mining model applicable for small sample sizes is presented and discussed. Our result suggested that this mining model may be useful in small sample-size studies to identify putative biomarkers and potential biological features of GC.

关键词： gastric cancer gene signature microarray machine-learning algorithm

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A Bottom-up Approach to Sustained Curtailment and Comfort for Controlled Demand Response 2

A Bottom-up Approach to Sustained Curtailment and Comfort fo...

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EEE Conference on Technologies for Sustainability (SusTech)

作者： Frincu, Marc Gima, Zachary Univ Southern Calif Dept Elect Engn Los Angeles CA 90089 USA Univ Southern Calif Dept Mech Engn Los Angeles CA 90089 USA

ISBN: (纸本)9781479952380

Curtailment prediction and efficient demand response (DR) strategy selection challenge the effectiveness of developing smart grid applications. Here we present solutions to both challenges, taking a bottom-up approach to demonstrate that curtailment at the equipment-level determined based on the equipment mechanical properties and models can be used to efficiently estimate curtailment and to optimize human comfort during a DR event across a subset of energy consumers. We focus on a controlled microgrid environment present on the University of Southern California campus and address two HVAC oriented DR strategies: variable frequency drive and global temperature reset. Strong correlation between equipment and building level consumption was found to exist in less than half the cases, showing that building level consumption can be successfully used as well for those scenarios. Several fast heuristics aiming to optimize human comfort during equipment level DR proved to be successful and showed that the best method depends on the DR strategy. Finally, relying on the equipment specifications and consumption models for our analyses circumvents the common challenges associated with statistical and machine-learning algorithm approaches that rely on large amounts of historical data.

关键词： demand side management distributed power generation smart power grids DR strategy selection HVAC oriented DR strategies University of Southern California bottom-up approach building level consumption controlled demand response controlled microgrid environment curtailment prediction energy consumers equipment mechanical properties global temperature reset human comfort machine-learning algorithm smart grid applications statistical algorithm sustained comfort sustained curtailment variable frequency drive Buildings Cooling Correlation Educational institutions Load modeling Microgrids Springs COMFORT Smart grids machine learning algorithms bottom-up approach variable frequency drives distributed power generation Load modeling Educational institutions Buildings springs Device Spring Demand-Responsive MICRO-GRID Cooling DR

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Sweet-Heart - An integrated suite of enabling computational tools for automated MS2/MS3 sequencing and identification of glycopeptides

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JOURNAL OF PROTEOMICS 2013年 84卷 1-16页

作者： Wu, Sz-Wei Liang, Suh-Yuen Pu, Tsung-Hsien Chang, Fang-Yu Khoo, Kay-Hooi Natl Taiwan Univ Inst Biochem Sci Taipei Taiwan Acad Sinica NRPGM Core Facil Prote & Glyc Taipei 115 Taiwan Acad Sinica Inst Biol Chem Taipei 115 Taiwan

High efficiency identification of intact glycopeptides from a shotgun glycoproteomic LC-MS2 dataset remains problematic. The prevalent mode of identifying the de-N-glycosylated peptides is littered with false positives and addresses only the issue of site occupancy. Here, we present Sweet-Heart, a computational tool set developed to tackle the heart of the problems in MS' sequencing of glycopeptide. It accepts low resolution and low accuracy ion trap MS2 data, filters for glycopeptides, couples knowledge-based de novo interpretation of glycosylation-dependent fragmentation pattern with protein database search, and uses machine-learning algorithm to score the computed glyco and peptide combinations. Higher ranking candidates are then compiled into a list of MS2/MS3 entries to drive subsequent rounds of targeted MS3 sequencing of putative peptide backbone, allowing its validation by database search in a fully automated fashion. With additional fishing out of all related glycoforms and final data integration, the platform proves to be sufficiently sensitive and selective, conducive to novel glycosylation discovery, and robust enough to discriminate, among others, N-glycolyl neuraminic acid/fucose from N-acetyl neuraminic acid/hexose. A critical appraisal of its computing performance shows that Sweet-Heart allows high sensitivity comprehensive mapping of site-specific glycosylation for isolated glycoproteins and facilitates analysis of glycoproteomic data. Biological significance The biological relevance of protein site-specific glycosylation cannot be meaningfully addressed without first defining its pattern by direct analysis of glycopeptides. Sweet-Heart is a novel suite of computational tools allowing for automated analysis of mass spectrometry-based glycopeptide sequencing data. It is developed to accept ion trap MS2/MS3 data and uses a machine learning algorithm to score and rank the candidate peptide core and glycosyl substituent combinations. By eliminating the

关键词： Site-specific glycosylation Glycoproteomics Automated glycopeptide identification MS3 machine-learning algorithm EGFR

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Genomic approach towards personalized anticancer drug therapy

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PHARMACOGENOMICS 2012年第2期13卷 191-199页

作者： Midorikawa, Yutaka Tsuji, Shingo Takayama, Tadatoshi Aburatani, Hiroyuki Univ Tokyo Adv Sci & Technol Res Ctr Genome Sci Div Meguro Ku Tokyo 1538904 Japan Nihon Univ Sch Med Dept Digest Surg Itabashi Ku Tokyo 1738610 Japan

Stratification of patients for multidrug response is a promising strategy for cancer treatment. Genome-based prediction models have great potential for this purpose because the extent of drug sensitivity may be attributed to the heterogeneity of the underlying genetic characteristics of cancer. However, microarray data is difficult to analyze and is not reproducible. Several machine-learning algorithms have therefore been developed in a repeatable manner. Random forests algorithm, which uses an ensemble approach based on classification and regression trees, appears to be superior for predicting multidrug sensitivity. This is because ensemble methods are more effective when there are much more predictors than samples. Here, we review recent advances in the development of classification algorithms using microarray technology for prediction of anticancer sensitivity, discuss the availability of ensemble methods for prediction models, and present data regarding the identification of potential responders to FOLFOX therapy using random forests algorithm.

关键词： anticancer therapy ensemble method expression array machine-learning algorithm personalized therapy predictor genes

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Bioinformatical approaches to characterize intrinsically disordered/unstructured proteins

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BRIEFINGS IN BIOINFORMATICS 2010年第2期11卷 225-243页

作者： Dosztanyi, Zsuzsanna Meszaros, Balint Simon, Istvan Hungarian Acad Sci Biol Res Ctr Inst Enzymol Prot Res Grp H-1518 Budapest Hungary

Intrinsically disordered/unstructured proteins exist without a stable three-dimensional (3D) structure as highly flexible conformational ensembles. The available genome sequences revealed that these proteins are surprisingly common and their frequency reaches high proportions in eukaryotes. Due to their vital role in various biological processes including signaling and regulation and their involvement in various diseases, disordered proteins and protein segments are the focus of many biochemical, molecular biological, pathological and pharmaceutical studies. These proteins are difficult to study experimentally because of the lack of unique structure in the isolated form. Their amino acid sequence, however, is available, and can be used for their identification and characterization by bioinformatic tools, analogously to globular proteins. In this review, we first present a small survey of current methods to identify disordered proteins or protein segments, focusing on those that are publicly available as web servers. In more detail we also discuss approaches that predict disordered regions and specific regions involved in protein binding by modeling the physical background of protein disorder. In our review we argue that the heterogeneity of disordered segments needs to be taken into account for a better understanding of protein disorder.

关键词： protein disorder coupled folding and binding machine-learning algorithm prediction method binary classification

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