The binding of microRNAs (miRNAs) to their target sites is a complex process, mediated by the Argonaute (Ago) family of proteins. The prediction of miRNA:target site binding is an important first step for any miRNA target prediction algorithm. To date, the potential for miRNA:target site binding is evaluated using either co-folding free energy measures or heuristic approaches, based on the identification of binding 'seeds', i.e., continuous stretches of binding corresponding to specific parts of the miRNA. The limitations of both these families of methods have produced generations of miRNA target prediction algorithms that are primarily focused on 'canonical' seed targets, even though unbiased experimental methods have shown that only approximately half of in vivo miRNA targets are 'canonical'. Herein, we present miRBind, a deep learning method and web server that can be used to accurately predict the potential of miRNA:target site binding. We trained our method using seed-agnostic experimental data and show that our method outperforms both seed-based approaches and co-fold free energy approaches. The full code for the development of miRBind and a freely accessible web server are freely available.

• MeSH
• Algorithms MeSH
• Argonaute Proteins genetics   metabolism   MeSH
• Deep Learning * MeSH
• MicroRNAs * genetics   metabolism   MeSH
• Computational Biology methods   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks. We provide a proof of concept for the use of deep neural network-based frameworks to aid precision oncology strategies.

• MeSH
• Survival Analysis MeSH
• Drug Resistance, Neoplasm * MeSH
• Deep Learning * MeSH
• Precision Medicine methods   MeSH
• Humans MeSH
• Cell Line, Tumor MeSH
• Neoplasms drug therapy   genetics   metabolism   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Deep Learning * MeSH
• Neural Networks, Computer MeSH
• Image Processing, Computer-Assisted * MeSH
• Publication type
• Journal Article MeSH
• Comment MeSH

The estimation of the speed of human motion from wearable IMU sensors is required in applications such as pedestrian dead reckoning. In this paper, we test deep learning methods for the prediction of the motion speed from raw readings of a low-cost IMU sensor. Each subject was observed using three sensors at the shoe, shin, and thigh. We show that existing general-purpose architectures outperform classical feature-based approaches and propose a novel architecture tailored for this task. The proposed architecture is based on a semi-supervised variational auto-encoder structure with innovated decoder in the form of a dense layer with a sinusoidal activation function. The proposed architecture achieved the lowest average error on the test data. Analysis of sensor placement reveals that the best location for the sensor is the shoe. Significant accuracy gain was observed when all three sensors were available. All data acquired in this experiment and the code of the estimation methods are available for download.

• MeSH
• Leg MeSH
• Pedestrians * MeSH
• Deep Learning * MeSH
• Humans MeSH
• Wearable Electronic Devices * MeSH
• Motion MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Strojové učení zaznamenalo v posledních letech pozoruhodný pokrok od téměř nepoužitelného rozpoznávání řeči a obrazu k nadlidské přesnosti. Od programů, které nedokázaly porazit jen trochu zkušenějšího hráče go, jsme dospěli k přemožiteli mistra světa. Za pokrokem ve vývoji učících se programů stojí tzv. hluboké učení - deep learning.; Strojové učení zaznamenalo v posledních letech pozoruhodný pokrok od téměř nepoužitelného rozpoznávání řeči a obrazu k nadlidské přesnosti. Od programů, které nedokázaly porazit jen trochu zkušenějšího hráče go, jsme dospěli k přemožiteli mistra světa. Za pokrokem ve vývoji učících se programů stojí tzv. hluboké učení – deep learning.

In forensic medicine, fatal hypothermia diagnosis is not always easy because findings are not specific, especially if traumatized. Post-mortem computed tomography (PMCT) is a useful adjunct to the cause-of-death diagnosis and some qualitative image character analysis, such as diffuse hyperaeration with decreased vascularity or pulmonary emphysema, have also been utilized for fatal hypothermia. However, it is challenging for inexperienced forensic pathologists to recognize the subtle differences of fatal hypothermia in PMCT images. In this study, we developed a deep learning-based diagnosis system for fatal hypothermia and explored the possibility of being an alternative diagnostic for forensic pathologists. An in-house dataset of forensic autopsy proven samples was used for the development and performance evaluation of the deep learning system. We used the area under the receiver operating characteristic curve (AUC) of the system for evaluation, and a human-expert comparable AUC value of 0.905, sensitivity of 0.948, and specificity of 0.741 were achieved. The experimental results clearly demonstrated the usefulness and feasibility of the deep learning system for fatal hypothermia diagnosis.

• MeSH
• Deep Learning * MeSH
• Hypothermia * diagnostic imaging   MeSH
• Humans MeSH
• Autopsy methods   MeSH
• Tomography, X-Ray Computed methods   MeSH
• Cause of Death MeSH
• Forensic Pathology methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Tým pracovníků Vysokého učení technického v Brně a Masarykovy univerzity vyvíjí webovou aplikaci, jejímž cílem je poskytovat terapeutům zpětnou vazbu na základě automatického zpracování pravidelně získávaných dotazníkových dat a audionahrávek z terapeutických sezení (z projektové zprávy).

An expert team from Brno University of Technology and Masaryk University is developing a web application to provide therapists with feedback based on automatic processing of regularly collected questionnaire data and audio recordings from therapy sessions (from project report).

Breast cancer survival prediction can have an extreme effect on selection of best treatment protocols. Many approaches such as statistical or machine learning models have been employed to predict the survival prospects of patients, but newer algorithms such as deep learning can be tested with the aim of improving the models and prediction accuracy. In this study, we used machine learning and deep learning approaches to predict breast cancer survival in 4,902 patient records from the University of Malaya Medical Centre Breast Cancer Registry. The results indicated that the multilayer perceptron (MLP), random forest (RF) and decision tree (DT) classifiers could predict survivorship, respectively, with 88.2 %, 83.3 % and 82.5 % accuracy in the tested samples. Support vector machine (SVM) came out to be lower with 80.5 %. In this study, tumour size turned out to be the most important feature for breast cancer survivability prediction. Both deep learning and machine learning methods produce desirable prediction accuracy, but other factors such as parameter configurations and data transformations affect the accuracy of the predictive model.

• MeSH
• Survival Analysis MeSH
• Deep Learning * MeSH
• Demography MeSH
• Adult MeSH
• Calibration MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Breast Neoplasms mortality   MeSH
• Neural Networks, Computer MeSH
• Decision Trees MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Support Vector Machine MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that simpler and quicker methods of examining tissue samples are used by non-pathologists, especially in the early stages of research and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction (~ 50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts' histopathological assessment standards (> 85% of cases) and enables high-throughput image analysis workflows in digital pathology labs.

• MeSH
• Deep Learning * MeSH
• Brain * diagnostic imaging   pathology   MeSH
• Mice, Transgenic MeSH
• Mice MeSH
• Image Processing, Computer-Assisted * methods   MeSH
• Reproducibility of Results MeSH
• Software standards   MeSH
• Animals MeSH
• Check Tag
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH