scholarly journals Multiple-Molecule Drug Design Based on Systems Biology Approaches and Deep Neural Network to Mitigate Human Skin Aging

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3178
Author(s):  
Shan-Ju Yeh ◽  
Jin-Fu Lin ◽  
Bor-Sen Chen
Keyword(s):  
Neural Network ◽  
Systems Biology ◽  
Signaling Pathways ◽  
Human Skin ◽  
Deep Neural Network ◽  
Middle Age ◽  
Skin Aging ◽  
Identification System ◽  
Potential Candidate ◽  
Systems Medicine

Human skin aging is affected by various biological signaling pathways, microenvironment factors and epigenetic regulations. With the increasing demand for cosmetics and pharmaceuticals to prevent or reverse skin aging year by year, designing multiple-molecule drugs for mitigating skin aging is indispensable. In this study, we developed strategies for systems medicine design based on systems biology methods and deep neural networks. We constructed the candidate genomewide genetic and epigenetic network (GWGEN) via big database mining. After doing systems modeling and applying system identification, system order detection and principle network projection methods with real time-profile microarray data, we could obtain core signaling pathways and identify essential biomarkers based on the skin aging molecular progression mechanisms. Afterwards, we trained a deep neural network of drug–target interaction in advance and applied it to predict the potential candidate drugs based on our identified biomarkers. To narrow down the candidate drugs, we designed two filters considering drug regulation ability and drug sensitivity. With the proposed systems medicine design procedure, we not only shed the light on the skin aging molecular progression mechanisms but also suggested two multiple-molecule drugs for mitigating human skin aging from young adulthood to middle age and middle age to old age, respectively.

scholarly journals Systems Medicine Design for Triple-Negative Breast Cancer and Non-Triple-Negative Breast Cancer Based on Systems Identification and Carcinogenic Mechanisms

2021 ◽  
Vol 22 (6) ◽  
pp. 3083
Author(s):  
Shan-Ju Yeh ◽  
Bo-Jie Hsu ◽  
Bor-Sen Chen
Keyword(s):  
Breast Cancer ◽  
Systems Biology ◽  
Signaling Pathways ◽  
Triple Negative Breast Cancer ◽  
Molecular Mechanisms ◽  
Triple Negative ◽  
Potential Candidate ◽  
Systems Medicine ◽  
Mesenchymal Transition ◽  
Systematic Procedure

Triple-negative breast cancer (TNBC) is a heterogeneous subtype of breast cancers with poor prognosis. The etiology of triple-negative breast cancer (TNBC) is involved in various biological signal cascades and multifactorial aberrations of genetic, epigenetic and microenvironment. New therapeutic for TNBC is urgently needed because surgery and chemotherapy are the only available modalities nowadays. A better understanding of the molecular mechanisms would be a great challenge because they are triggered by cascade signaling pathways, genetic and epigenetic regulations, and drug–target interactions. This would allow the design of multi-molecule drugs for the TNBC and non-TNBC. In this study, in terms of systems biology approaches, we proposed a systematic procedure for systems medicine design toward TNBC and non-TNBC. For systems biology approaches, we constructed a candidate genome-wide genetic and epigenetic network (GWGEN) by big databases mining and identified real GWGENs of TNBC and non-TNBC assisting with corresponding microarray data by system identification and model order selection methods. After that, we applied the principal network projection (PNP) approach to obtain the core signaling pathways denoted by KEGG pathway of TNBC and non-TNBC. Comparing core signaling pathways of TNBC and non-TNBC, essential carcinogenic biomarkers resulting in multiple cellular dysfunctions including cell proliferation, autophagy, immune response, apoptosis, metastasis, angiogenesis, epithelial-mesenchymal transition (EMT), and cell differentiation could be found. In order to propose potential candidate drugs for the selected biomarkers, we designed filters considering toxicity and regulation ability. With the proposed systematic procedure, we not only shed a light on the differences between carcinogenetic molecular mechanisms of TNBC and non-TNBC but also efficiently proposed candidate multi-molecule drugs including resveratrol, sirolimus, and prednisolone for TNBC and resveratrol, sirolimus, carbamazepine, and verapamil for non-TNBC.

scholarly journals Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Li-Hsin Cheng ◽  
Te-Cheng Hsu ◽  
Che Lin
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Feature Selection ◽  
Systems Biology ◽  
Ensemble Learning ◽  
Microarray Data ◽  
Deep Neural Network ◽  
Prediction Models ◽  
Biological Knowledge ◽  
Prognosis Prediction

AbstractBreast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.

scholarly journals Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jiarui Feng ◽  
Heming Zhang ◽  
Fuhai Li
Keyword(s):  
Neural Network ◽  
Survival Analysis ◽  
Deep Learning ◽  
Survival Time ◽  
Signaling Pathways ◽  
Cancer Patients ◽  
Deep Neural Network ◽  
Survival Prediction ◽  
Omics Data ◽  
Learning Models

Abstract Background Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions. Results In this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients’ survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients’ survival time. Conclusion The proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients’ survival by integrating multi-omics data and clinical factors.

scholarly journals Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

2021 ◽  
Author(s):  
Li-Hsin Cheng ◽  
Te-Cheng Hsu ◽  
Che Lin
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Feature Selection ◽  
Systems Biology ◽  
Ensemble Learning ◽  
Microarray Data ◽  
Deep Neural Network ◽  
Prediction Models ◽  
Biological Knowledge ◽  
Prognosis Prediction

Abstract Breast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic, predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.

scholarly journals Zerumbone Exhibits Antiphotoaging and Dermatoprotective Properties in Ultraviolet A-Irradiated Human Skin Fibroblast Cells via the Activation of Nrf2/ARE Defensive Pathway

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
You-Cheng Hseu ◽  
Chih-Ting Chang ◽  
Yugandhar Vudhya Gowrisankar ◽  
Xuan-Zao Chen ◽  
Hui-Chang Lin ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Human Skin ◽  
Skin Fibroblast ◽  
Skin Aging ◽  
Nuclear Accumulation ◽  
Ros Generation ◽  
Human Skin Fibroblast ◽  
Ultraviolet A ◽  
Uva Irradiation ◽  
Collagen Iii

Ultraviolet A (UVA) irradiation (320-400 nm range) triggers deleterious consequences in skin cell microenvironment leading to skin damage, photoaging (premature skin aging), and cancer. The accumulation of intracellular reactive oxygen species (ROS) plays a key role in this effect. With rapid progress in cosmetic health and quality of life, use of safe and highly effective phytochemicals has become a need of the hour. Zerumbone (ZER), a natural sesquiterpene, from Zingiber zerumbet rhizomes is well-known for its beneficial effects. We investigated the antiphotoaging and dermatoprotective efficacies of ZER (2-8 μM) against UVA irradiation (3 J/cm2) and elucidated the underlying molecular mechanisms in human skin fibroblast (HSF) cells. ZER treatment prior to low dose of UVA exposure increased cell viability. UVA-induced ROS generation was remarkably suppressed by ZER with parallel inhibition of MMP-1 activation and collagen III degradation. This was due to the inhibition of AP-1 (c-Fos and c-Jun) translocation. Furthermore, ZER alleviated UVA-induced SA-β-galactosidase activity. Dose- or time-dependent increase of antioxidant genes, HO-1 and γ-GCLC by ZER, was associated with increased expression and nuclear accumulation of Nrf2 as well as decreased cytosolic Keap-1 expressions. Altered luciferase activity of ARE could explain the significance of Nrf2/ARE pathway underlying the dermatoprotective properties of ZER. Pharmacological inhibition of various signaling pathways suppressed nuclear Nrf2 activation in HSF cells confirming that Nrf2 translocation was mediated by ERK, JNK, PI3K/AKT, PKC, AMPK, casein kinase II, and ROS signaling pathways. Moreover, increased basal ROS levels and Nrf2 translocation seem crucial in ZER-mediated Nrf2/ARE signaling pathway. This was also evidenced from Nrf2 knocked-out studies in which ZER was not able to suppress the UVA-induced ROS generation in the absence of Nrf2. This study concluded that in the treatment of UVA-induced premature skin aging, ZER may consider as a desirable food supplement for skin protection and/or preparation of skin care products.

scholarly journals Development of Indian Spoken Language Identification System for Two Languages using MFCC Feature with Deep Neural Network

2020 ◽  
Vol 9 (7S) ◽  
pp. 43-45
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Language Translation ◽  
Spoken Language ◽  
Language Identification ◽  
Classification Model ◽  
Identification System ◽  
Speech Sample ◽  
Indian Languages ◽  
Language Recognition

Language is the ability to communicate with any person. Approximate number of spoken languages are 6500 in the world. Different regions in a world have different languages spoken. Spoken language recognition is the process to identify the language spoken in a speech sample. Most of the spoken language identification is done on languages other than Indian. There are many applications to recognize a speech like spoken language translation in which the fundamental step is to recognize the language of the speaker. This system is specifically made to identify two Indian languages. The speech data of various news channels is used that is available online. The Mel Frequency Cepstral Coefficients (MFCC) feature is used to collect features from the speech sample because it provides a particular identity to the different classes of audio. The identification is done by using MFCC feature in the Deep Neural Network. The objective of this work is to improve the accuracy of the classification model. It is done by making changes in several layers of the Deep Neural Network.

scholarly journals Dialect Identification in Telugu Language Speech Utterance Using Modified Features with Deep Neural Network

2021 ◽  
Vol 38 (6) ◽  
pp. 1793-1799
Author(s):  
Shivaprasad Satla ◽  
Sadanandam Manchala
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Speech Processing ◽  
Deep Neural Network ◽  
Speaker Identification ◽  
Research Work ◽  
Gaussian Mixture ◽  
Vital Role ◽  
Identification System ◽  
Mel Frequency Cepstral Coefficients

Dialect Identification is the process of identifies the dialects of particular standard language. The Telugu Language is one of the historical and important languages. Like any other language Telugu also contains mainly three dialects Telangana, Costa Andhra and Rayalaseema. The research work in dialect identification is very less compare to Language identification because of dearth of database. In any dialects identification system, the database and feature engineering play vital roles because of most the words are similar in pronunciation and also most of the researchers apply statistical approaches like Hidden Markov Model (HMM), Gaussian Mixture Model (GMM), etc. to work on speech processing applications. But in today's world, neural networks play a vital role in all application domains and produce good results. One of the types of the neural networks is Deep Neural Networks (DNN) and it is used to achieve the state of the art performance in several fields such as speech recognition, speaker identification. In this, the Deep Neural Network (DNN) based model Multilayer Perceptron is used to identify the regional dialects of the Telugu Language using enhanced Mel Frequency Cepstral Coefficients (MFCC) features. To do this, created a database of the Telugu dialects with the duration of 5h and 45m collected from different speakers in different environments. The results produced by DNN model compared with HMM and GMM model and it is observed that the DNN model provides good performance.

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