Machine Learning and Graph Analytics in Computational Biomedicine
Publication date: Available online 7 September 2017 Source:Artificial Intelligence in Medicine Author(s): Quan Zou, Lei Chen, Tao Huang, Zhenguo Zhang, Yungang Xu (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - September 8, 2017 Category: Bioinformatics Source Type: research

Gene2DisCo: Gene to disease using disease commonalities
We present a novel network-based method, Gene2DisCo, based on generalized linear models (GLMs) to effectively prioritize genes by exploiting data regarding disease-genes, gene interaction networks and disease similarities. The scarcity of disease-genes is addressed by applying an efficient negative selection procedure, together with imbalance-aware GLMs. Gene2DisCo is a flexible framework, in the sense it is not dependent upon specific types of data, and/or upon specific disease ontologies. Results On a benchmark dataset composed of nine human networks and 708 medical subject headings (MeSH) diseases, Gene2DisCo largely ou...
Source: Artificial Intelligence in Medicine - September 6, 2017 Category: Bioinformatics Source Type: research

Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies
Conclusion Owlready has been successfully used in a medical research project. It has been published as Open-Source software and then used by many other researchers. Future developments will focus on the support of vagueness and additional non-monotonic reasoning feature, and automatic dialog box generation. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - August 18, 2017 Category: Bioinformatics Source Type: research

Detecting masses in dense breast using independent component analysis
This study can help specialist to detect lesions in dense breast. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 27, 2017 Category: Bioinformatics Source Type: research

A New Preprocessing Parameter Estimation based on Geodesic Active Contour Model for Automatic Vestibular Neuritis Diagnosis
Publication date: Available online 23 July 2017 Source:Artificial Intelligence in Medicine Author(s): Amine Ben Slama, Aymen Mouelhi, Hanene Sahli, Sondes Manoubi, Chiraz Mbarek, Hedi Trabelsi, Farhat Fnaiech, Mounir Sayadi The diagnostic of the vestibular neuritis (VN) presents many difficulties to traditional assessment methods This paper deals with a fully automatic VN diagnostic system based on nystagmus parameter estimation using a pupil detection algorithm. A geodesic active contour model is implemented to find an accurate segmentation region of the pupil. Hence, the novelty of the proposed algorithm is to speed up ...
Source: Artificial Intelligence in Medicine - July 25, 2017 Category: Bioinformatics Source Type: research

A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery
Conclusions The results represent an improvement over existing deformation models for real time applications, providing smaller errors and high patient-specificity. The proposed approach addresses the current needs of image-guided surgical systems and has the potential to be employed to model the deformation of any type of soft tissue. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 25, 2017 Category: Bioinformatics Source Type: research

Gaussian Process Classification of Superparamagnetic Relaxometry Data: Phantom Study
Conclusions The GP framework provides acceptable classification accuracies when dealing with in silico and phantom SPMR datasets and can outperform an image reconstruction method for binary classification of SPMR data. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 25, 2017 Category: Bioinformatics Source Type: research

Artificial Intelligence in Medicine AIME 2015
Publication date: Available online 18 July 2017 Source:Artificial Intelligence in Medicine Author(s): John H. Holmes, Lucia Sacchi, Riccardo Bellazzi, Niels Peek (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 19, 2017 Category: Bioinformatics Source Type: research

Employing decomposable partially observable Markov decision processes to control gene regulatory networks
Conclusions The reported test results using both synthetic and real GRNs are promising in demonstrating the applicability, effectiveness and efficiency of the proposed approach. This is due to the fact that partial observability fits well to the problem of noisy acquisition of gene expression data as there are technological limitations to measure precisely exact expression levels of genes. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - July 19, 2017 Category: Bioinformatics Source Type: research

Extract critical factors affecting the length of hospital stay of pneumonia patient by data mining (case study: an Iranian hospital)
Publication date: Available online 13 July 2017 Source:Artificial Intelligence in Medicine Author(s): Naghmeh Khajehali, Somayeh Alizadeh Motivation Pneumonia is a prevalent infection of lower respiratory tract caused by infected lungs. Length of stay (LOS) in hospital is one of the simplest and most important indicators in hospital activity that is used for different purposes. The aim of this study is to explore the important factors affecting the LOS of patients with pneumonia in hospitals. Methods The clinical data set for the study were collected from 387 patients in a specialized hospital in Iran between 2009 and 201...
Source: Artificial Intelligence in Medicine - July 15, 2017 Category: Bioinformatics Source Type: research

Personal sleep pattern visualization using sequence-based kernel self-organizing map on sound data
Publication date: Available online 11 July 2017 Source:Artificial Intelligence in Medicine Author(s): Hongle Wu, Takafumi Kato, Tomomi Yamada, Masayuki Numao, Ken-ichi Fukui We propose a method to discover sleep patterns via clustering of sound events recorded during sleep. The proposed method extends the conventional self-organizing map algorithm by kernelization and sequence-based technologies to obtain a fine-grained map that visualizes the distribution and changes of sleep-related events. We introduced features widely applied in sound processing and popular kernel functions to the proposed method to evaluate and compa...
Source: Artificial Intelligence in Medicine - July 12, 2017 Category: Bioinformatics Source Type: research

Medical Image Classification via Multiscale Representation Learning
Publication date: Available online 29 June 2017 Source:Artificial Intelligence in Medicine Author(s): Qiling Tang, Yangyang Liu, Haihua Liu Multiscale structure is an essential attribute of natural images. Similarly, there exist scaling phenomena in medical images, and therefore a wide range of observation scales would be useful for medical imaging measurements. The present work proposes a multiscale representation learning method via sparse autoencoder networks to capture the intrinsic scales in medical images for the classification task. We obtain the multiscale feature detectors by the sparse autoencoders with differen...
Source: Artificial Intelligence in Medicine - June 30, 2017 Category: Bioinformatics Source Type: research

Random survival forest with space extensions for censored data
Publication date: Available online 20 June 2017 Source:Artificial Intelligence in Medicine Author(s): Hong Wang, Lifeng Zhou Prediction capability of a classifier usually improves when it is built from an extended variable space by adding new variables from randomly combination of two or more original variables. However, its usefulness in survival analysis of censored time-to-event data is yet to be verified. In this research, we investigate the plausibility of space extension technique, originally proposed for classification purpose, to survival analysis. By combing random subspace, bagging and extended space techniques,...
Source: Artificial Intelligence in Medicine - June 21, 2017 Category: Bioinformatics Source Type: research

Machine learning based identification of protein –protein interactions using derived features of physiochemical properties and evolutionary profiles
Publication date: May 2017 Source:Artificial Intelligence in Medicine, Volume 78 Author(s): Muhammad Tahir, Maqsood Hayat Proteins are the central constitute of a cell or biological system. Proteins execute their functions by interacting with other molecules such as RNA, DNA and other proteins. The major functionality of protein–protein interactions (PPIs) is the execution of biochemical activities in living species. Therefore, an accurate identification of PPIs becomes a challenging and demanding task for investigators from last few decades. Various traditional and computational methods have been applied but they h...
Source: Artificial Intelligence in Medicine - June 20, 2017 Category: Bioinformatics Source Type: research

iACP-GAEnsC: Evolutionary genetic Algorithm based Ensemble Classification of Anticancer Peptides by utilizing Hybridd Feature space
Publication date: Available online 17 June 2017 Source:Artificial Intelligence in Medicine Author(s): Shahid Akbar, Maqsood Hayat, Muhammad Iqbal, Mian Ahmad Jan Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of antica...
Source: Artificial Intelligence in Medicine - June 20, 2017 Category: Bioinformatics Source Type: research

Machine Learning based Identification of Protein-Protein Interactions using derived features of physiochemical properties and Evolutionary profiles
Publication date: Available online 13 June 2017 Source:Artificial Intelligence in Medicine Author(s): Muhammad Tahir, Maqsood Hayat Proteins are the central constitute of a cell or biological system. Proteins execute their functions by interacting with other molecules such as RNA, DNA and other proteins. The major functionality of protein-protein interactions (PPIs) is the execution of biochemical activities in living species. Therefore, an accurate identification of PPIs becomes a challenging and demanding task for investigators from last few decades. Various traditional and computational methods have been applied but th...
Source: Artificial Intelligence in Medicine - June 14, 2017 Category: Bioinformatics Source Type: research

Automatic Detection of Surgical Haemorrhage using Computer Vision
Conclusions The proposed algorithm turns out to be a good starting point for an automatic detection of blood and bleeding in the surgical environment which can be applied to enhance the surgeon vision, for example showing the last frame previous to a massive haemorrhage where the incision could be seen using augmented reality capabilities. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - June 11, 2017 Category: Bioinformatics Source Type: research

Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence
In this study, ensemble learning and five data mining approaches, including support vector machine (SVM), C5.0, extreme learning machine (ELM), multivariate adaptive regression splines (MARS), and random forest (RF), were integrated to rank the importance of risk factors and diagnose the recurrence of ovarian cancer. The medical records and pathologic status were extracted from the Chung Shan Medical University Hospital Tumor Registry. Experimental results illustrated that the integrated C5.0 model is a superior approach in predicting the recurrence of ovarian cancer. Moreover, the classification accuracies of C5.0, ELM, M...
Source: Artificial Intelligence in Medicine - June 11, 2017 Category: Bioinformatics Source Type: research

A hybrid framework for reverse engineering of robust Gene Regulatory Networks
Publication date: Available online 9 June 2017 Source:Artificial Intelligence in Medicine Author(s): Mina Jafari, Behnam Ghavami, Vahid Sattari The inference of Gene Regulatory Networks (GRNs) using gene expression data in order to detect the basic cellular processes is a key issue in biological systems. Inferring GRN correctly requires inferring predictor set accurately. In this paper, a fast and accurate predictor set inference framework which linearly combines some inference methods is proposed. The purpose of the combination of various methods is to increase the accuracy of inferred GRN. The proposed framework offers ...
Source: Artificial Intelligence in Medicine - June 10, 2017 Category: Bioinformatics Source Type: research

Fully automated breast boundary and pectoral muscle segmentation in mammograms
Publication date: Available online 9 June 2017 Source:Artificial Intelligence in Medicine Author(s): Andrik Rampun, Philip J. Morrow, Bryan W. Scotney, John Winder Breast and pectoral muscle segmentation is an essential pre-processing step for the subsequent processes in computer aided diagnosis (CAD) systems. Estimating the breast and pectoral boundaries is a difficult task especially in mammograms due to artifacts, homogeneity between the pectoral and breast regions, and low contrast along the skin-air boundary. In this paper, a breast boundary and pectoral muscle segmentation method in mammograms is proposed. For breas...
Source: Artificial Intelligence in Medicine - June 10, 2017 Category: Bioinformatics Source Type: research

Premature Ventricular Contraction Detection Combining Deep Neural Networks and Rules Inference
Publication date: Available online 9 June 2017 Source:Artificial Intelligence in Medicine Author(s): Fei-yan Zhou, Lin-peng Jin, Jun Dong Premature ventricular contraction (PVC), which is a common form of cardiac arrhythmia caused by ectopic heartbeat, can lead to life-threatening cardiac conditions. Computer-aided PVC detection is of considerable importance in medical centers or outpatient ECG rooms. In this paper, we proposed a new approach that combined deep neural networks and rules inference for PVC detection. The detection performance and generalization were studied using publicly available databases: the MIT-BIH ar...
Source: Artificial Intelligence in Medicine - June 10, 2017 Category: Bioinformatics Source Type: research

Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles
Conclusions We studied eighteen features for drug combinations and built a computational model using random forest algorithm. The model was evaluated using an independent test dataset. Our model provides an efficient strategy to identify potentially synergistic drug combinations for cancer and may help reduce the search space for high-throughput synergistic drug combinations screening. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - June 4, 2017 Category: Bioinformatics Source Type: research

Medical image classification based on multi-scale non-negative sparse coding
Publication date: Available online 27 May 2017 Source:Artificial Intelligence in Medicine Author(s): Ruijie Zhang, Jian Shen, Fushan Wei, Xiong Li, Arun Kumar Sangaiah With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding ba...
Source: Artificial Intelligence in Medicine - May 28, 2017 Category: Bioinformatics Source Type: research

Subcellular localization prediction of apoptosis proteins based on evolutionary information and support vector machine
Conclusions The proposed feature representation is powerful and the prediction accuracy will be improved greatly, which denotes our method provides the state-of-the-art performance for predicting subcellular location of apoptosis proteins. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - May 25, 2017 Category: Bioinformatics Source Type: research

From SNOMED CT to Uberon: Transferability of evaluation methodology between similarly structured ontologies
Conclusions Overlapping concepts from Uberon’s disjoint abstraction network are quite likely (up to 28.9%) to exhibit issues. The results suggest that the methodology can transfer well between same family ontologies. Although Uberon exhibited relatively few overlapping concepts, the methodology can be combined with other semantic indicators to expand the process to other concepts within the ontology that will generate high yields of discovered issues. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - May 20, 2017 Category: Bioinformatics Source Type: research

Heart murmur detection based on Wavelet Transformation and a synergy between Artificial Neural Network and modified Neighbor Annealing methods
In this study, Artificial Neural Network (ANN) is trained with Modified Neighbor Annealing (MNA) to classify heart cycles into normal and murmur classes. Heart cycles are separated from heart sounds using wavelet transformer. The network inputs are features extracted from individual heart cycles, and two classification outputs. Classification accuracy of the proposed model is compared with five multilayer perceptron trained with Levenberg-Marquardt, Extreme-learning-machine, back-propagation, simulated-annealing, and neighbor-annealing algorithms. It is also compared with a Self-Organizing Map (SOM) ANN. The proposed model...
Source: Artificial Intelligence in Medicine - May 14, 2017 Category: Bioinformatics Source Type: research

Intelligent computational model for classification of sub-Golgi protein using oversampling and fisher feature selection methods
Publication date: Available online 10 May 2017 Source:Artificial Intelligence in Medicine Author(s): Jamal Ahmad, Faisal Javed, Maqsood Hayat Golgi is one of the core proteins of a cell, constitutes in both plants and animals, which is involved in protein synthesis. Golgi is responsible for receiving and processing the macromolecules and trafficking of newly processed protein to its intended destination. Dysfunction in Golgi protein is expected to cause many neurodegenerative and inherited diseases that may be cured well if they are detected effectively and timely. Golgi protein is categorized into two parts cis-Golgi and...
Source: Artificial Intelligence in Medicine - May 10, 2017 Category: Bioinformatics Source Type: research

Modeling New Immunoregulatory Therapeutics as Antimicrobial Alternatives for treating Clostridium difficile infection
Publication date: Available online 9 May 2017 Source:Artificial Intelligence in Medicine Author(s): Andrew Leber, Raquel Hontecillas, Vida Abedi, Nuria Tubau-Juni, Victoria Zoccoli-Rodriguez, Caroline Stewart, Josep Bassaganya-Riera The current treatment paradigm in Clostridium difficile infection is the administration of antibiotics contributing to the high rates of recurrent infections. Recent alternative strategies, such as fecal microbiome transplantation and anti-toxin antibodies, have shown similar efficacy in the treatment of C. difficile associated disease (CDAD). However, barriers exist for either treatment or ot...
Source: Artificial Intelligence in Medicine - May 9, 2017 Category: Bioinformatics Source Type: research

Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods
Conclusions The use of AL methods, (a) reduces intra-labeler variability in the performance of the induced models during the training phase, and thus reduces the risk of halting the process at a local minimum that is significantly different in performance from the rest of the learned models; and (b) reduces Inter-labeler performance variance, and thus reduces the dependence on the use of a particular labeler. In addition, the use of a consensus label, agreed upon by a rather uneven group of labelers, might be at least as good as using the gold standard labeler, who might not be available, and certainly better than randomly...
Source: Artificial Intelligence in Medicine - April 27, 2017 Category: Bioinformatics Source Type: research

Knowledge Graph for TCM Health Preservation: Design, Construction, and Applications
In this study, we construct a large-scale knowledge graph, which integrates terms, documents, databases and other knowledge resources. This knowledge graph can facilitate various knowledge services such as knowledge visualization, knowledge retrieval, and knowledge recommendation, and helps the sharing, interpretation, and utilization of TCM health care knowledge. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - April 21, 2017 Category: Bioinformatics Source Type: research

Analyzing interactions on combining multiple clinical guidelines
Publication date: Available online 11 April 2017 Source:Artificial Intelligence in Medicine Author(s): Veruska Zamborlini, Marcos da Silveira, Cedric Pruski, Annette ten Teije, Edwin Geleijn, Marike van der Leeden, Martijn Stuiver, Frank van Harmelen Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approac...
Source: Artificial Intelligence in Medicine - April 12, 2017 Category: Bioinformatics Source Type: research

Identification of transcription factors that may reprogram lung adenocarcinoma
Publication date: Available online 1 April 2017 Source:Artificial Intelligence in Medicine Author(s): Chenglin Liu, Yu-Hang Zhang, Tao Huang, Yudong Cai Background Lung adenocarcinoma is one of most threatening disease to human health. Although many efforts have been devoted to its genetic study, few researches have been focused on the transcription factors which regulate tumor initiation and progression by affecting multiple downstream gene transcription. It is proved that proper transcription factors may mediate the direct reprogramming of cancer cells, and reverse the tumorigenesis on the epigenetic and transcription l...
Source: Artificial Intelligence in Medicine - April 2, 2017 Category: Bioinformatics Source Type: research

Feasibility of spirography features for objective assessment of motor function in Parkinson's disease
Conclusions The relatively high classication accuracy and AUC demonstrates the usefulness of this approach for objective monitoring of PD patients. The positive evaluation of computer's explanations suggests the potential use of this methodology in a decision support setting. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - April 1, 2017 Category: Bioinformatics Source Type: research

Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs
Publication date: Available online 27 March 2017 Source:Artificial Intelligence in Medicine Author(s): Changjian Sun, Shuxu Guo, Huimao Zhang, Jing Li, Meimei Chen, Shuzhi Ma, Lanyi Jin, Xiaoming Liu, Xueyan Li, Xiaohua Qian This paper presents a novel, fully automatic approach based on a fully convolutional network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a multi-channel fully convolutional network (MC-FCN) to segment liver tumors from multiphase contrast-enhanced CT images. Because each phase of contrast-enhanced data provides distinct information on pathological features, we trained o...
Source: Artificial Intelligence in Medicine - March 28, 2017 Category: Bioinformatics Source Type: research

Protein fold recognition based on sparse representation based classification
In this study, we apply the SRC to solve the protein fold recognition problem. Experimental results on a widely used benchmark dataset show that the proposed method is able to improve the performance of some basic classifiers and three state-of-the-art methods to feature selection, including autocross-covariance (ACC) fold, D-D, and Bi-gram. Finally, we propose a novel computational predictor called MF-SRC for fold recognition by combining these three features into the framework of SRC to achieve further performance improvement. Compared with other computational methods in this field on DD dataset, EDD dataset and TG datas...
Source: Artificial Intelligence in Medicine - March 28, 2017 Category: Bioinformatics Source Type: research

Automatic matching of surgeries to predict surgeons ’ next actions
Conclusions This work shows that, even from the low-level description of surgeries and without other sources of information, it is often possible to predict the next surgical task when the conditions are consistent with the previously recorded surgeries. We also showed that our method is able to assess when there is actually a large divergence between the predictions and decide that it is not reasonable to make a prediction. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - March 24, 2017 Category: Bioinformatics Source Type: research

Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome
Publication date: Available online 22 March 2017 Source:Artificial Intelligence in Medicine Author(s): Liang Yu, Jin Zhao, Lin Gao Finding new uses for existing drugs has become a new strategy for decades to treat more patients. Few traditional approaches consider the tissue specificities of diseases. Moreover, disease genes, drug targets and protein interaction (PPI) networks remain largely incomplete and the relationships between drugs and diseases conform to the triangularly balanced structure. Therefore, based on tissue specificities of diseases, we apply the triangularly balanced theory and the module distance define...
Source: Artificial Intelligence in Medicine - March 23, 2017 Category: Bioinformatics Source Type: research

User recommendation in healthcare social media by assessing user similarity in heterogeneous network
Conclusion The results indicate that content-based methods can effectively capture the similarity of inactive users who usually have focused interests, while structural methods can achieve better performance when rich structural information is available. Local structural approach only considers direct connections between nodes in the network, while global structural approach takes the indirect connections into account. Therefore, the global similarity approach can deal with sparse networks and capture the implicit similarity between two users. Different approaches may capture different aspects of the similarity relationshi...
Source: Artificial Intelligence in Medicine - March 19, 2017 Category: Bioinformatics Source Type: research

Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification
Conclusion Thanks to the performance and interpretability of the fuzzy classification method employed, predictors of both parotid shrinkage and xerostomia are detected, and their influence on each outcome is revealed. Moreover, models for predicting parotid shrinkage at initial and half radiotherapy stages are found. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - March 19, 2017 Category: Bioinformatics Source Type: research

Updating Markov models to integrate cross-sectional and longitudinal studies
Publication date: Available online 9 March 2017 Source:Artificial Intelligence in Medicine Author(s): Allan Tucker, Yuanxi Li, David Garway-Heath Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. Cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modelling detailed prognostic predictions. Longitudinal studies on the other hand, are used to explore how these processes deve...
Source: Artificial Intelligence in Medicine - March 10, 2017 Category: Bioinformatics Source Type: research

Identify and analysis crotonylation sites in histone by using support vector machines
Conclusion Identification of the Kcr sites in histone is an indispensable step for decoding protein function. Therefore, the method can promote the deep understanding of the physiological roles of crotonylation and provide useful information for developing drugs to treat various diseases associated with crotonylation. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - March 8, 2017 Category: Bioinformatics Source Type: research

Improved prediction of protein –protein interactions using novel negative samples, features, and an ensemble classifier
In this study, two PPNI datasets were artificially generated from a PPI database. In contrast to various traditional PPI feature extraction methods based on sequential information, two types of novel feature extraction methods were proposed. One is based on secondary structure information, and the other is based on the physicochemical properties of proteins. The experimental results of the RandomPairs dataset validate the efficiency and effectiveness of the proposed prediction model. These results reveal the potential of constructing a PPI negative dataset to reduce false negatives. Related datasets, tools, and source code...
Source: Artificial Intelligence in Medicine - March 5, 2017 Category: Bioinformatics Source Type: research

Differential regulation analysis reveals dysfunctional regulatory mechanism involving transcription factors and microRNAs in gastric carcinogenesis
Publication date: Available online 1 March 2017 Source:Artificial Intelligence in Medicine Author(s): Quanxue Li, Junyi Li, Wentao Dai, Yi-Xue Li, Yuan-Yuan Li Gastric cancer (GC) is one of the most incident malignancies in the world. Although lots of featured genes and microRNAs (miRNAs) have been identified to be associated with gastric carcinogenesis, underlying regulatory mechanisms still remain unclear. In order to explore the dysfunctional mechanisms of GC, we developed a novel approach to identify carcinogenesis relevant regulatory relationships, which is characterized by quantifying the difference of regulatory re...
Source: Artificial Intelligence in Medicine - March 1, 2017 Category: Bioinformatics Source Type: research

A novel hierarchical selective ensemble classifier with bioinformatics application
Publication date: Available online 27 February 2017 Source:Artificial Intelligence in Medicine Author(s): Leyi Wei, Shixiang Wan, Jiasheng Guo, Kelvin KL Wong Selective ensemble learning is a technique that selects a subset of diverse and accurate basic models in order to generate stronger generalization ability. In this paper, we proposed a novel learning algorithm that is based on parallel optimization and hierarchical selection (PTHS). Our novel feature selection method is based on maximize the sum of relevance and distance (MSRD) for solving the problem of high dimensionality. Specifically, we have a PTHS algorithm th...
Source: Artificial Intelligence in Medicine - February 28, 2017 Category: Bioinformatics Source Type: research

Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem
Conclusions The combination of the proposed cost-sensitive evolutionary algorithm together with the application of an over-sampling technique improves the predictive capability of our model in a significant way (especially for minority classes), which can help the surgeons make more informed decisions about the most appropriate recipient for an specific donor organ, in order to maximize the probability of survival after the transplantation and therefore the fairness principle. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - February 21, 2017 Category: Bioinformatics Source Type: research

Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports
Conclusion Evaluation analysis demonstrated that CARD is more likely to identify true causal drug variables and associations to adverse event. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - February 13, 2017 Category: Bioinformatics Source Type: research

DisTeam: A decision support tool for surgical team selection
Conclusion DisTeam is a decision support tool for assisting in surgical team selection. It can facilitate the job of scheduling personnel in the hospital which involves an overwhelming number of factors pertaining to patients, individual team members, and team dynamics and can be used to compose patient-personalized surgical teams with minimum (potential) surgical complications. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - February 13, 2017 Category: Bioinformatics Source Type: research

A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis
Publication date: Available online 11 February 2017 Source:Artificial Intelligence in Medicine Author(s): Dongxiao Gu, Changyong Liang, Huimin Zhao Currently, breast cancer diagnosis depends largely on physicians’ experiential knowledge. By retrieving similar cases in a breast cancer decision support system, oncologists can obtain powerful information or knowledge, complementing their own experiential knowledge, in their medical decision making. In this paper, we present the implementation of a case-based reasoning (CBR) system for breast cancer related diagnoses and its application in two studies related to benign/...
Source: Artificial Intelligence in Medicine - February 13, 2017 Category: Bioinformatics Source Type: research

Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways
Conclusions This study provided novel insight of how lncRNAs may affect tumorigenesis and which pathways may play important roles during it. These results could help understanding the biological mechanisms of lncRNAs and treating cancer. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - February 13, 2017 Category: Bioinformatics Source Type: research

Editorial from the new Editor-in-Chief: Artificial Intelligence in Medicine and the forthcoming challenges
Publication date: Available online 6 February 2017 Source:Artificial Intelligence in Medicine Author(s): Carlo Combi (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - February 7, 2017 Category: Bioinformatics Source Type: research