Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Prediction of kinase inhibitors binding modes with machine learning and reduced descriptor sets.

In Scientific reports ; h5-index 158.0

Protein kinases are receiving wide research interest, from drug perspective, due to their important roles in human body. Available kinase-inhibitor data, including crystallized structures, revealed many details about the mechanism of inhibition and binding modes. The understanding and analysis of these binding modes are expected to support the discovery of kinase-targeting drugs. The huge amounts of data made it possible to utilize computational techniques, including machine learning, to help in the discovery of kinase-targeting drugs. Machine learning gave reasonable predictions when applied to differentiate between the binding modes of kinase inhibitors, promoting a wider application in that domain. In this study, we applied machine learning supported by feature selection techniques to classify kinase inhibitors according to their binding modes. We represented inhibitors as a large number of molecular descriptors, as features, and systematically reduced these features in a multi-step manner while trying to attain high classification accuracy. Our predictive models could satisfy both goals by achieving high accuracy while utilizing at most 5% of the modeling features. The models could differentiate between binding mode types with MCC values between 0.67 and 0.92, and balanced accuracy values between 0.78 and 0.97 for independent test sets.

Abdelbaky Ibrahim, Tayara Hilal, Chong Kil To

2021-Jan-12

General General

Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers.

In Entropy (Basel, Switzerland)

BACKGROUND : the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers.

METHODS : in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers.

RESULTS : in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83).

CONCLUSION : the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.

Ćwiklinski Bartosz, Giełczyk Agata, Choraś Michał

2021-Jan-10

big data, football support, machine learning, sports analytics

Ophthalmology Ophthalmology

Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera.

In Journal of diabetes science and technology ; h5-index 38.0

BACKGROUND : Portable retinal cameras and deep learning (DL) algorithms are novel tools adopted by diabetic retinopathy (DR) screening programs. Our objective is to evaluate the diagnostic accuracy of a DL algorithm and the performance of portable handheld retinal cameras in the detection of DR in a large and heterogenous type 2 diabetes population in a real-world, high burden setting.

METHOD : Participants underwent fundus photographs of both eyes with a portable retinal camera (Phelcom Eyer). Classification of DR was performed by human reading and a DL algorithm (PhelcomNet), consisting of a convolutional neural network trained on a dataset of fundus images captured exclusively with the portable device; both methods were compared. We calculated the area under the curve (AUC), sensitivity, and specificity for more than mild DR.

RESULTS : A total of 824 individuals with type 2 diabetes were enrolled at Itabuna Diabetes Campaign, a subset of 679 (82.4%) of whom could be fully assessed. The algorithm sensitivity/specificity was 97.8 % (95% CI 96.7-98.9)/61.4 % (95% CI 57.7-65.1); AUC was 0·89. All false negative cases were classified as moderate non-proliferative diabetic retinopathy (NPDR) by human grading.

CONCLUSIONS : The DL algorithm reached a good diagnostic accuracy for more than mild DR in a real-world, high burden setting. The performance of the handheld portable retinal camera was adequate, with over 80% of individuals presenting with images of sufficient quality. Portable devices and artificial intelligence tools may increase coverage of DR screening programs.

Malerbi Fernando Korn, Andrade Rafael Ernane, Morales Paulo Henrique, Stuchi José Augusto, Lencione Diego, de Paulo Jean Vitor, Carvalho Mayana Pereira, Nunes Fabrícia Silva, Rocha Roseanne Montargil, Ferraz Daniel A, Belfort Rubens

2021-Jan-12

Covid-19, artificial intelligence, diabetic retinopathy, mobile healthcare, point-of-care, screening, telemedicine

Ophthalmology Ophthalmology

A novel retinal ganglion cell quantification tool based on deep learning.

In Scientific reports ; h5-index 158.0

Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major research efforts are presently directed towards the understanding of disease pathogenesis and the development of new therapies, with the help of rodent models as an important preclinical research tool. The ultimate goal is reaching neuroprotection of the RGCs, which requires a tool to reliably quantify RGC survival. Hence, we demonstrate a novel deep learning pipeline that enables fully automated RGC quantification in the entire murine retina. This software, called RGCode (Retinal Ganglion Cell quantification based On DEep learning), provides a user-friendly interface that requires the input of RBPMS-immunostained flatmounts and returns the total RGC count, retinal area and density, together with output images showing the computed counts and isodensity maps. The counting model was trained on RBPMS-stained healthy and glaucomatous retinas, obtained from mice subjected to microbead-induced ocular hypertension and optic nerve crush injury paradigms. RGCode demonstrates excellent performance in RGC quantification as compared to manual counts. Furthermore, we convincingly show that RGCode has potential for wider application, by retraining the model with a minimal set of training data to count FluoroGold-traced RGCs.

Masin Luca, Claes Marie, Bergmans Steven, Cools Lien, Andries Lien, Davis Benjamin M, Moons Lieve, De Groef Lies

2021-Jan-12

General General

Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network.

In Scientific reports ; h5-index 158.0

Closed-cycle aquaculture using hatchery produced seed stocks is vital to the sustainability of endangered species such as Pacific bluefin tuna (Thunnus orientalis) because this aquaculture system does not depend on aquaculture seeds collected from the wild. High egg quality promotes efficient aquaculture production by improving hatch rates and subsequent growth and survival of hatched larvae. In this study, we investigate the possibility of a simple, low-cost, and accurate egg quality prediction system based only on photographic images using deep neural networks. We photographed individual eggs immediately after spawning and assessed their qualities, i.e., whether they hatched normally and how many days larvae survived without feeding. The proposed system predicted normally hatching eggs with higher accuracy than human experts. It was also successful in predicting which eggs would produce longer-surviving larvae. We also analyzed the image aspects that contributed to the prediction to discover important egg features. Our results suggest the applicability of deep learning techniques to efficient egg quality prediction, and analysis of early developmental stages of development.

Ienaga Naoto, Higuchi Kentaro, Takashi Toshinori, Gen Koichiro, Tsuda Koji, Terayama Kei

2021-Jan-12

General General

Domain randomization-enhanced deep learning models for bird detection.

In Scientific reports ; h5-index 158.0

Automatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

Mao Xin, Chow Jun Kang, Tan Pin Siang, Liu Kuan-Fu, Wu Jimmy, Su Zhaoyu, Cheong Ye Hur, Ooi Ghee Leng, Pang Chun Chiu, Wang Yu-Hsing

2021-Jan-12