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Toplam kayıt 35, listelenen: 1-10
A New Approach for Predicting the Value of Gene Expression: Two-way Collaborative Filtering
(2019)
Background: Predicting the value of gene expression in a given condition is a challenging topic in computational systems biology. Only a limited number of studies in this area have provided solutions to predict the expression ...
DeepMBS: Prediction of Protein Metal Binding-Site Using Deep Learning Networks
(2017)
The tertiary structure of a protein indicates what vital function that protein fulfills in the cell. Prediction of the metal binding comformation of a protein from its sequence is a crucial step in predicting its tertiary ...
Microrna Expression Prediction: Regression from Regulatory Elements
(2016)
MicroRNAs are known as important actors in post-transcriptional regulation and relevant biological processes. Their expression levels do not only provide information about their own activities but also implicitly explain ...
Sequence Analysis to Predict Microrna Chemotherapy Resistance
(2016)
Recent findings suggest that microRNAs play important role in resistance to certain chemotherapies. The knowledge of what microRNAs are potentially resistant to given chemotherapies is therefore a crucial knowledge on drug ...
Eliminating Rib Shadows in Chest Radiographic Images Providing Diagnostic Assistance
(2016)
A major difficulty with chest radiographic analysis is the invisibility of abnormalities caused by the superimposition of normal anatomical structures, such as ribs, over the main tissue to be examined. Suppressing the ...
Exploiting Active MicroRNA Interactions for Diagnosis from Expression Profiling Experiments
(2017)
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 ...
Context-Sensitive Model Learning for Lung Nodule Detection
(2016)
Nodule detection in chest radiographs is a main component of current Computer Aided Diagnosis (CAD) systems. The problem is usually approached as a supervised classification task of candidate nodule segments. To this end, ...
Texture of Activities: Exploiting Local Binary Patterns for Accelerometer Data Analysis
(2016)
Recognition of activities through wearable sensors such as accelerometers is a recent challenge in pervasive and ubiquitous computing. The problem is often considered as a classification task where a set of descriptive ...
Parkinson's Disease Monitoring from Gait Analysis via Foot-Worn Sensors
(2018)
Background: In Parkinson's disease (PD), neuronal loss in the substantia nigra ultimate in dopaminergic denervation of the stiratum is followed by disarraying of the movements' preciseness, automatism, and agility. Hence, ...