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General General

Extracting Insights From Temporal Data by Integrating Dynamic Modeling and Machine Learning.

In Frontiers in physiology

Biological processes are dynamic. As a result, temporal analyses are necessary to fully understand the complex interactions that occurs within these systems. One example of a multifaceted biological process is restitution: the initial step in complex wound repair. Restitution is a dynamic process that depends on an elegant orchestration between damaged cells and their intact neighbors. Such orchestration enables the quick repair of the damaged area, which is essential to preserve epithelial integrity and prevent further injury. High quality dynamic data of the cellular and molecular events that make up the gastric restitution process has been documented. However, comprehensive dynamic models that connect all relevant molecular interactions to cellular behaviors are challenging to construct and experimentally validate. In order to efficiently provide feedback to ongoing experimental work, we have integrated dynamical modeling and machine learning to efficiently extract data-driven insights without incorporating detailed mechanisms. Dynamical models convert time course data into a set of static features, which are then subjected to machine learning analysis. The integrated analysis provides data-driven insights into how repair might be regulated in individual gastric organoids. We have provided a "proof of concept" of how such an analysis pipeline can be used to analyze any temporal dataset and provide timely data-driven insights.

Ballweg Richard, Engevik Kristen A, Montrose Marshall H, Aihara Eitaro, Zhang Tongli


actin, computational model, gastric epithelium, organoids, restitution

General General

Bacterial Alterations in Post-Cholecystectomy Patients Are Associated With Colorectal Cancer.

In Frontiers in oncology

Background: Although increasing evidences showed a correlation between cholecystectomy and the prevalence rate of colorectal cancer (CRC), and shed light on gut microbiota in colorectal pathogenesis, only a few studies focused on microbial alterations after cholecystectomy, and its sequent role in carcinogenesis and progression of CRC has not been reported. Thus, we aimed to investigate the bacterial alterations and tried to clarify their clinical significance. Methods: 104 subjects were enrolled and divided into post-cholecystectomy patients (PC, n = 52) and healthy controls (HC, n = 52). To investigate the bacterial role in carcinogenesis, PC patients were further separated into preCA_CRC (patients with precancerous lesions and/or CRC, n = 9) and non-CA (patients without precancerous lesions and CRC, n = 43) based on the histopathology. Qualified stool samples were collected for 16S rRNA gene sequencing to analyze the bacterial profile. Results: Our data showed noteworthy compositional and abundant alterations of bacterial microbiota in PC patients, characterized as Bacteroides ovatus, Prevotella copri, and Fusobacterium varium remarkably increased; Faecalibacterium prausnitzii, Roseburia faecis, and Bifidobacterium adolescentis significantly decreased. Additionally, the duration after cholecystectomy was the critical factor that affected bacterial composition. Machine learning-based analysis showed a pivotal role of Megamonas funiformis in discriminating PC from HC subjects and involving in the progression of CRC. Conclusions: The bacterial dysbiosis may associate with CRC in PC patients, and the duration after cholecystectomy was highlighted as an important factor. Altered bacterial microbiota was likely to play a pivotal role in related-disease in the long-term follow-up of PC patients.

Ren Xinhua, Xu Jun, Zhang Yuanyuan, Chen Guodong, Zhang Yiwen, Huang Qing, Liu Yulan


16S rRNA gene sequencing, bacterial alterations, cholecystectomy, colorectal cancer, the duration after cholecystectomy

Surgery Surgery

Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model.

In Frontiers in bioengineering and biotechnology

Cancer is still a severe health problem globally. The therapy of cancer traditionally involves the use of radiotherapy or anticancer drugs to kill cancer cells, but these methods are quite expensive and have side effects, which will cause great harm to patients. With the find of anticancer peptides (ACPs), significant progress has been achieved in the therapy of tumors. Therefore, it is invaluable to accurately identify anticancer peptides. Although biochemical experiments can solve this work, this method is expensive and time-consuming. To promote the application of anticancer peptides in cancer therapy, machine learning can be used to recognize anticancer peptides by extracting the feature vectors of anticancer peptides. Nevertheless, poor performance usually be found in training the machine learning model to utilizing high-dimensional features in practice. In order to solve the above job, this paper put forward a 19-dimensional feature model based on anticancer peptide sequences, which has lower dimensionality and better performance than some existing methods. In addition, this paper also separated a model with a low number of dimensions and acceptable performance. The few features identified in this study may represent the important features of anticancer peptides.

Li Qingwen, Zhou Wenyang, Wang Donghua, Wang Sui, Li Qingyuan


anticancer peptide, feature extraction, feature model, feature selection, machine learning

General General

NanoReviser: An Error-Correction Tool for Nanopore Sequencing Based on a Deep Learning Algorithm.

In Frontiers in genetics ; h5-index 62.0

Nanopore sequencing is regarded as one of the most promising third-generation sequencing (TGS) technologies. Since 2014, Oxford Nanopore Technologies (ONT) has developed a series of devices based on nanopore sequencing to produce very long reads, with an expected impact on genomics. However, the nanopore sequencing reads are susceptible to a fairly high error rate owing to the difficulty in identifying the DNA bases from the complex electrical signals. Although several basecalling tools have been developed for nanopore sequencing over the past years, it is still challenging to correct the sequences after applying the basecalling procedure. In this study, we developed an open-source DNA basecalling reviser, NanoReviser, based on a deep learning algorithm to correct the basecalling errors introduced by current basecallers provided by default. In our module, we re-segmented the raw electrical signals based on the basecalled sequences provided by the default basecallers. By employing convolution neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) networks, we took advantage of the information from the raw electrical signals and the basecalled sequences from the basecallers. Our results showed NanoReviser, as a post-basecalling reviser, significantly improving the basecalling quality. After being trained on standard ONT sequencing reads from public E. coli and human NA12878 datasets, NanoReviser reduced the sequencing error rate by over 5% for both the E. coli dataset and the human dataset. The performance of NanoReviser was found to be better than those of all current basecalling tools. Furthermore, we analyzed the modified bases of the E. coli dataset and added the methylation information to train our module. With the methylation annotation, NanoReviser reduced the error rate by 7% for the E. coli dataset and specifically reduced the error rate by over 10% for the regions of the sequence rich in methylated bases. To the best of our knowledge, NanoReviser is the first post-processing tool after basecalling to accurately correct the nanopore sequences without the time-consuming procedure of building the consensus sequence. The NanoReviser package is freely available at

Wang Luotong, Qu Li, Yang Longshu, Wang Yiying, Zhu Huaiqiu


DNA methylation, convolution neural network, deep learning, long short-term memory networks, nanopore sequencing, sequencing revising

General General

Role of Kinematics Assessment and Multimodal Sensorimotor Training for Motion Deficits in Breast Cancer Chemotherapy-Induced Polyneuropathy: A Perspective on Virtual Reality Avatars.

In Frontiers in oncology

Chemotherapy-induced polyneuropathy (CIPN), one of the most severe and incapacitating side effects of chemotherapeutic drugs, is a serious concern in breast cancer therapy leading to dose diminution, delay, or cessation. The reversibility of CIPN is of increasing importance since active chemotherapies prolong survival. Clinical assessment tools show that patients experiencing sensorimotor CIPN symptoms not only do they have to cope with loss in autonomy and life quality, but CIPN has become a key restricting factor in treatment. CIPN incidence poses a clinical challenge and has lacked established and efficient therapeutic options up to now. Complementary, non-opioid therapies are sought for both prevention and management of CIPN. In this perspective, we explore the potential that digital interventions have for sensorimotor CIPN rehabilitation in breast cancer patients. Our primary goal is to emphasize the benefits and impact that Virtual Reality (VR) avatars and Machine Learning have in combination in a digital intervention aiming at (1) assessing the complete kinematics of deficits through learning underlying patient sensorimotor parameters, and (2) parameterize a multimodal VR simulation to drive personalized deficit compensation. We support our perspective by evaluating sensorimotor effects of chemotherapy, the metrics to assess sensorimotor deficits, and relevant clinical studies. We subsequently analyse the neurological substrate of VR sensorimotor rehabilitation, with multisensory integration acting as a key element. Finally, we propose a closed-loop patient-centered design recommendation for CIPN sensorimotor rehabilitation. Our aim is to provoke the scientific community toward the development and use of such digital interventions for more efficient and targeted rehabilitation.

Axenie Cristian, Kurz Daria


body sensors, breast cancer, chemotherapy-induced peripheral neuropathy, machine learning, sensorimotor rehabilitation, virtual reality

General General

An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images.

In BMC biomedical engineering

Background : Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding.

Results : To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods.

Conclusions : We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. Our method also can facilitate downstream nuclei morphological analyses as it provides high-quality features extracted from histopathology images.

Jung Hwejin, Lodhi Bilal, Kang Jaewoo


Convolutional neural network, Deep learning, Histopathology, Image analysis, Nuclei segmentation