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Dermatology Dermatology

Prediction of Chemosensitivity in Multiple Primary Cancer Patients Using Machine Learning.

In Anticancer research

BACKGROUND/AIM : Many cancer patients face multiple primary cancers. It is challenging to find an anticancer therapy that covers both cancer types in such patients. In personalized medicine, drug response is predicted using genomic information, which makes it possible to choose the most effective therapy for these cancer patients. The aim of this study was to identify chemosensitive gene sets and compare the predictive accuracy of response of cancer cell lines to drug treatment, based on both the genomic features of cell lines and cancer types.

MATERIALS AND METHODS : In this study, we identified a gene set that is sensitive to a specific therapeutic drug, and compared the performance of several predictive models using the identified genes and cancer types through machine learning (ML). To this end, publicly available gene expression datasets and drug sensitivity datasets of gastric and pancreatic cancers were used. Five ML algorithms, including linear discriminant analysis, classification and regression tree, k-nearest neighbors, support vector machine and random forest, were implemented.

RESULTS : The predictive accuracy of the cancer type models were 0.729 to 0.763 on the training dataset and 0.731 to 0.765 on the testing dataset. The predictive accuracy of the genomic prediction models was 0.818 to 1.0 on the training dataset and 0.759 to 0.896 on the testing dataset.

CONCLUSION : Performance of the specific gene models was much better than those of the cancer type models using the ML methods. Therofore, the most effective therapeutic drug can be chosen based on the expression of specific genes in patients with multiple primary cancers, regardless of cancer types.

Zhang Xianglan, Jang M I, Zheng Zhenlong, Gao Aihua, Lin Zhenhua, Kim Ki-Yeol


Multiple primary cancers, cancer type, chemosensitivity prediction, gene expression, machine learning

General General

Machine Learning Pipeline for Segmentation and Defect Identification from High-Resolution Transmission Electron Microscopy Data.

In Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach for fast, accurate analysis of electron microscopy data. Here, we demonstrate a flexible two-step pipeline for the analysis of high-resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for the detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from the amorphous background with a Dice coefficient of 0.8 and significantly outperforms traditional image segmentation methods. Using these segmented regions, we are then able to classify whether nanoparticles contain a visible stacking fault with 86% accuracy. We provide this adaptable pipeline as an open-source tool for the community. The combined output of the segmentation network and classifier offer a way to determine statistical distributions of features of interest, such as size, shape, and defect presence, enabling the detection of correlations between these features.

Groschner Catherine K, Choi Christina, Scott Mary C


HRTEM, automated analysis, deep learning, segmentation, structure classification

General General

Persistent dependent behaviour is accompanied by dynamic switching between the ventral and dorsal striatal connections in internet gaming disorder.

In Addiction biology ; h5-index 43.0

Cross-sectional studies have suggested that functional heterogeneity within the striatum in individuals with addictive behaviours may involve the transition from ventral to dorsal partitions; however, due to limitations of the cross-sectional design, whether the contribution of this transition to addiction was confused by individual differences remains unclear, especially for internet gaming disorder (IGD). Longitudinal functional magnetic resonance imaging (fMRI) data from 22 IGD subjects and 18 healthy controls were collected at baseline and more than 6 months later. We examined the connectivity features of subregions within the striatum between these two scans. Based on the results, we further performed dynamic causal modelling to explore the directional effect between regions and used these key features for data classification in machine learning to test the replicability of the results. Compared with controls, IGD subjects exhibited decreased functional connectivity between the left dorsal striatum (putamen) and the left insula, whereas connectivity between the right ventral striatum (nucleus accumbens [Nacc]) and the left insula was relatively stable over time. An inhibitory effective connectivity from the left putamen to the right Nacc was found in IGD subjects during the follow-up scan. Using the above features, the classification accuracy of the training model developed with the follow-up was better than that of the model based on the initial scan. Persistent IGD status was accompanied by a switch in the locus of control within the striatum, which provided new insights into association between IGD and drug addiction.

Wang Min, Zheng Hui, Zhou Weiran, Jiang Qing, Dong Guang-Heng


dynamic causal modelling, functional connectivity, internet gaming disorder, longitudinal study, machine learning, striatum

General General

EEG-based auditory attention decoding using speech-level-based segmented computational models.

In Journal of neural engineering ; h5-index 52.0

OBJECTIVE : Auditory attention in complex scenarios can be decoded by electroencephalography (EEG)-based cortical speech-envelope tracking. The relative root-mean-square (RMS) intensity is a valuable cue for the decomposition of speech into distinct characteristic segments. To improve auditory attention decoding (AAD) performance, this work proposed a novel segmented AAD approach to decode target speech envelopes from different RMS-level-based speech segments.

APPROACH : Speech was decomposed into higher- and lower-RMS-level speech segments with a threshold of -10 dB relative RMS level. A support vector machine classifier was designed to identify higher- and lower-RMS-level speech segments. Segmented computational models were developed with the classification results of higher- and lower-RMS-level speech segments. Speech envelopes were reconstructed based on segmented decoding models for either higher- or lower-RMS-level speech segments.

MAIN RESULTS : Higher- and lower-RMS-level speech segments in continuous sentences could be identified robustly with classification accuracies that approximated or exceeded 80% based on corresponding EEG signals at 6 dB, 3 dB, 0 dB, -3 dB and -6 dB signal-to-mask ratios (SMRs). Compared with unified AAD decoding methods, the proposed segmented AAD approach achieved more accurate results in the reconstruction of target speech envelopes and in the detection of attentional directions. Moreover, the proposed segmented decoding method had higher information transfer rates and shorter minimum expected switch times compared with the unified decoder.

SIGNIFICANCE : This study revealed that EEG signals may be used to classify higher- and lower-RMS-level-based speech segments across a wide range of SMR conditions (from 6 dB to -6 dB). A novel finding was that the specific information in different RMS-level-based speech segments facilitated EEG-based decoding of auditory attention. The significantly improved AAD accuracies and information transfer rates of the segmented decoding method suggests that this proposed computational model may be an effective method for the application of neuro-controlled brain-computer interfaces in complex auditory scenes.

Wang Lei, Wu Ed X, Chen Fei


EEG, RMS-level-based speech segments, auditory attention decoding (AAD), machine learning, signal-to-mask ratio (SMR), support vector machine (SVM), temporal response function (TRF)

General General

Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India.

In The Science of the total environment

In the 21st century, groundwater depletion is posing a serious threat to humanity throughout the world, particularly in developing nations. India being the largest consumer of groundwater in the world, dwindling groundwater storage has emerged as a serious concern in recent years. Consequently, the judicious and efficient management of vital groundwater resources is one of the grand challenges in India. Groundwater modeling is a promising tool to develop sustainable management strategies for the efficient utilization of this treasured resource. This study demonstrates a pragmatic framework for predicting seasonal groundwater levels at a large scale using real-world data. Three relatively powerful Machine Learning (ML) techniques viz., ANFIS (Adaptive Neuro-Fuzzy Inference System), Deep Neural Network (DNN) and Support Vector Machine (SVM) were employed for predicting seasonal groundwater levels at the country scale using in situ groundwater-level and pertinent meteorological data of 1996-2016. ANFIS, DNN and SVM models were developed for 18 Agro-Ecological Zones (AEZs) of India and their efficacy was evaluated using suitable statistical and graphical indicators. The findings of this study revealed that the DNN model is the most proficient in predicting seasonal groundwater levels in most AEZs, followed by the ANFIS model. However, the prediction ability of the three models is 'moderate' to 'very poor' in 3 AEZs ['Western Plain and Kutch Peninsula' in Western India, and 'Deccan Plateau (Arid)' and 'Eastern Ghats and Deccan Plateau' in Southern India]. It is recommended that groundwater-monitoring network and data acquisition systems be strengthened in India in order to ensure efficient use of modeling techniques for the sustainable management of groundwater resources.

Mohapatra Janaki B, Jha Piyush, Jha Madan K, Biswal Sabinaya


ANFIS, Agro-Ecological Zone, DNN, Data-driven modeling, Groundwater-level prediction, Machine learning/artificial intelligence techniques, SVM

Public Health Public Health

Prenatal exposure to ambient fine particulate matter and early childhood neurodevelopment: A population-based birth cohort study.

In The Science of the total environment

Although previous studies have reported the adverse effect of air pollution exposure during pregnancy on neurodevelopment in children, epidemiological evidence is limited, and the results are inconsistent. This study aimed to explore the association between prenatal ambient fine particulate matter (PM2.5) exposure and early childhood neurodevelopment in a large birth cohort study of 4009 maternal-child pairs. Prenatal daily PM2.5 exposure concentrations at 1 km spatial revolution were estimated using high-performance machine-learning models. Neurodevelopmental outcomes of children at ages 2, 6, 12, and 24 months were assessed using the Ages and Stages Questionnaire (ASQ). Distributed lag nonlinear models were used to identify critical windows of prenatal PM2.5 exposure. General linear mixed models with binomially distributed errors were used to estimate the effect of prenatal PM2.5 exposure on suspected developmental delay (SDD) in five developmental domains based on the longitudinal design. Prenatal PM2.5 exposure was significantly associated with decreased scores for all neurodevelopmental domains of children at ages 2, 6, and 24 months. Each 10-μg/m3 increase in PM2.5 exposure was significantly associated with increased risk of SDD for all subjects (RR: 1.52 95% CI: 1.19, 2.03), specifically, in problem-solving domain for girls (RR: 2.23, 95% CI: 1.22, 4.35). Prenatal PM2.5 exposure in weeks 18 to 34 was significantly associated with both ASQ scores and SDDs. Our study proposed that prenatal PM2.5 exposure affected early childhood neurodevelopment evaluated with the ASQ scale. PM2.5 exposure might increase the risk of SDD for boys and girls, specifically in the problem-solving domain for girls.

Wang Pengpeng, Zhao Yingya, Li Jialin, Zhou Yuhan, Luo Ranran, Meng Xia, Zhang Yunhui


Birth cohort, Critical windows, Fine particulate matter, Neurodevelopment