Artificial intelligence deciphers codes for color and odor perceptions based on large-scale chemoinformatic data

Zhang, Xiayin; Zhang, Kai; Lin, Duoru; Chen, Chuan; He, Lin; Guo, Xusen; Chen, Kexin; Wang, Ruixin; Liu, Zhenzhen; Wu, Xiaohang; Long, Erping; Huang, Kai; He, Zhiqiang; Liu, Xiyang

doi:10.1093/gigascience/giaa011

Cited by 15 publications

(14 citation statements)

References 37 publications

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“…The faster R-CNN was implemented with Caffe [ 24 ] (Berkeley Vision and Learning Center deep learning framework) and trained in parallel on 4 Nvidia Titan X graphics processing units. Accuracy, sensitivity, and specificity [ 25 , 26 ] were analyzed to comprehensively evaluate the performance of this system.…”

Section: Methodsmentioning

confidence: 99%

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Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study

Pan¹,

Zhang²,

He³

et al. 2021

JMIR Med Inform

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Background Disk herniation and disk bulge are two common disorders of lumbar intervertebral disks (IVDs) that often result in numbness, pain in the lower limbs, and lower back pain. Magnetic resonance (MR) imaging is one of the most efficient techniques for detecting lumbar diseases and is widely used for making clinical diagnoses at hospitals. However, there is a lack of efficient tools for effectively interpreting massive amounts of MR images to meet the requirements of many radiologists. Objective The aim of this study was to present an automatic system for diagnosing disk bulge and herniation that saves time and can effectively and significantly reduce the workload of radiologists. Methods The diagnosis of lumbar vertebral disorders is highly dependent on medical images. Therefore, we chose the two most common diseases—disk bulge and herniation—as research subjects. This study is mainly about identifying the position of IVDs (lumbar vertebra [L] 1 to L2, L2-L3, L3-L4, L4-L5, and L5 to sacral vertebra [S] 1) by analyzing the geometrical relationship between sagittal and axial images and classifying axial lumbar disk MR images via deep convolutional neural networks. Results This system involved 4 steps. In the first step, it automatically located vertebral bodies (including the L1, L2, L3, L4, L5, and S1) in sagittal images by using the faster region-based convolutional neural network, and our fourfold cross-validation showed 100% accuracy. In the second step, it spontaneously identified the corresponding disk in each axial lumbar disk MR image with 100% accuracy. In the third step, the accuracy for automatically locating the intervertebral disk region of interest in axial MR images was 100%. In the fourth step, the 3-class classification (normal disk, disk bulge, and disk herniation) accuracies for the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 IVDs were 92.7%, 84.4%, 92.1%, 90.4%, and 84.2%, respectively. Conclusions The automatic diagnosis system was successfully built, and it could classify images of normal disks, disk bulge, and disk herniation. This system provided a web-based test for interpreting lumbar disk MR images that could significantly improve diagnostic efficiency and standardized diagnosis reports. This system can also be used to detect other lumbar abnormalities and cervical spondylosis.

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Section: Methodsmentioning

confidence: 99%

“…Accuracy, sensitivity, and specificity [25,26] were analyzed to comprehensively evaluate the performance of this system.…”

Section: Automatically Locating Vertebral Bodies In Midsagittal Imagesmentioning

confidence: 99%

Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study

Pan¹,

Zhang²,

He³

et al. 2021

JMIR Med Inform

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“…The decision tree is a basic machine learning method that applies tree data structure to recursively split the whole dataset into multiple subsets. Finally, the samples in each leaf node either belong to one class or own more features could be used to be split, namely, the class of each sample can be inferred according to the paths from the root node to leaf nodes in the tree (39)(40)(41)(42). In our research, the RF model was used to construct models of whether a patient will survive for more than 4 years and whether the tumor will metastasis within 4 years after plaque brachytherapy.…”

Section: Prediction Modelmentioning

confidence: 99%

Prognosis Prediction of Uveal Melanoma After Plaque Brachytherapy Based on Ultrasound With Machine Learning

et al. 2022

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IntroductionUveal melanoma (UM) is the most common intraocular malignancy in adults. Plaque brachytherapy remains the dominant eyeball-conserving therapy for UM. Tumor regression in UM after plaque brachytherapy has been reported as a valuable prognostic factor. The present study aimed to develop an accurate machine-learning model to predict the 4-year risk of metastasis and death in UM based on ocular ultrasound data.Material and MethodsA total of 454 patients with UM were enrolled in this retrospective, single-center study. All patients were followed up for at least 4 years after plaque brachytherapy and underwent ophthalmologic evaluations before the therapy. B-scan ultrasonography was used to measure the basal diameters and thickness of tumors preoperatively and postoperatively. Random Forest (RF) algorithm was used to construct two prediction models: whether a patient will survive for more than 4 years and whether the tumor will develop metastasis within 4 years after treatment.ResultsOur predictive model achieved an area under the receiver operating characteristic curve (AUC) of 0.708 for predicting death using only a one-time follow-up record. Including the data from two additional follow-ups increased the AUC of the model to 0.883. We attained AUCs of 0.730 and 0.846 with data from one and three-time follow-up, respectively, for predicting metastasis. The model found that the amount of postoperative follow-up data significantly improved death and metastasis prediction accuracy. Furthermore, we divided tumor treatment response into four patterns. The D(decrease)/S(stable) patterns are associated with a significantly better prognosis than the I(increase)/O(other) patterns.ConclusionsThe present study developed an RF model to predict the risk of metastasis and death from UM within 4 years based on ultrasound follow-up records following plaque brachytherapy. We intend to further validate our model in prospective datasets, enabling us to implement timely and efficient treatments.

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“…The advent of Machine Learning (ML) and Deep Learning (DL) approaches have created a path towards solving previously challenging unsolved problems in biology and chemistry [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. Various reviews summarize the application of ML/DL in drug design and discovery [ 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction

Verma

Trozzi

et al. 2021

IJMS

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Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-Machine Learning (ML) models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind, including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research, while being readily accessible for de novo drug designers as a standalone package.

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Artificial intelligence deciphers codes for color and odor perceptions based on large-scale chemoinformatic data

Cited by 15 publications

References 37 publications

Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study

Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study

Prognosis Prediction of Uveal Melanoma After Plaque Brachytherapy Based on Ultrasound With Machine Learning

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction

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