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Molecularly targeted therapies significantly contribute to the efforts of personalized approaches for cancer diagnosis and chemotherapeutic treatment. One of a critical step to identify target molecules is to determine the most representative features for different patient's sub-groups. Breast cancer, one of the most heterogeneous cancer has five main subtypes, so accurately identify gene signatures...
The success of genome-wide association studies and the development of next generation sequencing technology have identified many disease-related common and rare variants. There have been several prediction models suggested using penalized regression or statistical learning methods. However, only a few prediction models are available which use both common and rare variants. The aim of our study is...
Genome-wide association study (GWAS), as one primary approach for genetic studies, has been successfully applied to a variety of complex diseases, leading to the discovery of substantial disease-associated loci. These discovered associations provide unprecedented opportunities for deepening our understanding of complex diseases, such as disease-associated risk variants, genes, and pathways. However,...
In the last years social networks have emerged as a critical mean for information spreading. In spite of all the positive consequences this phenomenon brings, unverified and instrumentally relevant information statements in circulation, named as rumours, are becoming a potential threat to the society. Recently, there have been several studies on topic-independent rumour detection on Twitter. In this...
With the development of deep sequencing technology, isomiRs (isoform of miRNA) are consistently observed in a variety of cell types, tissues, and different cell development stages. miRNA isoforms as the products of miRNA genes, are variants which are different from mature miRNAs in length and position. Recently, many studies emphasized on isomiR and found its subtypes are differentially expression...
The rapid development of high-throughput sequencing technology provides unique opportunities for studies of transcription factor binding, while also bringing new computational challenges. Recently, a series of discriminative motif discovery (DMD) methods have been proposed and offer promising solutions for addressing these challenges. However, because of the huge computational cost, most of them have...
Genome classification has become an increasingly important genomic research method for cancer identification and treatment. One challenge associated with genome classification is feature selection; which genes can be used for phenotyping. This challenge is made more complicated considering affected gene mutate at different rates and schedules. In addition, the number of genes and consequently the...
The gene structure is consist of intron, exons, promoter, start codon, stop codon, etc. for the eukaryotic organism. The boundary between intron and exon is splice site. There is the need for accurate algorithms to be used in the splice sites identification and more attention was paid during past few years. This proposed system, Splice Hybrid have three layered architecture — in this layer2nd orderMM...
In this paper, we propose a classification model for learning state based on individual biometric data. In particular, we use the pupil size as a biometric data and the data has been collected from 72 participants. We also deploy the support vector machine (SVM) in conjunction with k-fold validation as an analysis tool. In order to improve the performance of the SVM, the we remove outliers from the...
Identifying effective cancer biomarkers is crucial in precision medicine. Based on the high-throughput available omics data such as microarray, this paper aims to identify potential biomarker genes for hepatocellular carcinoma by bioinformatics and machine learning. We describe the gene coexpressions with network model and detect out the genes that are closely related to liver cancer infected by hepatitis...
Class imbalance in machine learning is when there are significantly fewer training instances of one class in comparison to another one. In bioinformatics, there is such a problem in the computational prediction of novel microRNA (miRNAs) within a full genome. The well-known precursors miRNA (pre-miRNA) are usually only a few in comparison to the hundreds of thousands of potential candidates, which...
Recognizing secondary structures in proteins can be a highly computationally expensive task that may not always yield good results. Using Restricted Boltzmann Machines (RBM) we were able to train a simple neural network to recognize an alpha-helix with a good degree of accuracy. Modifying the RBM implementation to be much simpler and more efficient than the standard implementation we are able to see...
In silico diagnosis through microRNA expression profiling experiments is a promising direction in the clinical practices of bioinformatics science. The task is computationally defined as a classification problem where a query experiment is required to be assigned into one of the predefined diseases using a model learned from previously labeled samples. While several powerful machine learning models...
Nowadays a huge volume of biomedical data (images, genes, etc) are daily generated. The interpretation of such data involves a considerable expertise. The misinterpretation and/or misdetection of a suspicious clinical finding leads to increasing the negligence claims, and redundant procedures (e.g. biopsies). The analysis of biomedical data is a complex task which are performed by specialists on whose...
In recent years, the problem of classification for high dimensional and class-imbalanced data is found in many fields like bioinformatics and so on. High dimensional problem result in bad classification results because of some combinations of features have adverse effect on classification. Class-imbalanced problem means the number of samples of one class is more than another class, which would make...
Research on activity based computing has, recently made progress and is growing attention in many real life applications like healthcare monitoring using wearable sensors and development of rehabilitation systems. However there is a lack of overview about the various sensing technologies used for activity recognition. In this paper, we present a comprehensive survey about the various sensing technologies...
This paper presents a novel approach based on the analysis of genetic variants from publicly available genetic profiles and the manually curated database, the National Human Genome Research Institute Catalog. Using data science techniques, genetic variants are identified in the collected participant profiles and then indexed as risk variants in the National Human Genome Research Institute Catalog...
Nowadays, along with the development of information technologies, storage and analysis of biomedical datasets are easy in health sector. In this area, Machine Learning methods provide a great contribution for evaluation and interpretation of data. In this paper, in addition to Support Vector Machines, Decision Tree, K-Nearest Neighbors, Naive Bayes and Dictionary Learning methods, Random Feature Subspaces...
Cancer is still one of the challenging diseases to develop new therapies due to the late diagnosis and its complex progression nature. There is an urgent need for new therapy regimes for cancer patients having late stage diagnosis or recurrence. New computational approaches can help to identify more effective drug combinations as new treatment options for cancer. For this purpose, we developed a classification...
Gene Regulatory Network (GRN) represents the regulatory interactions between genes. Experimental methods are capable of determining the nature of gene regulation in a given system, but are time-consuming and expensive. High-throughput technologies produce large number of gene expression data. These data along with additional information from heterogeneous data sources enable computational biologists...
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