The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Lana X Garmire - One of the best experts on this subject based on the ideXlab platform.
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deep learning based multi omics integration robustly predicts survival in liver cancer
bioRxiv, 2017Co-Authors: Kumardeep Chaudhary, Olivier Poirion, Liangqun Lu, Lana X GarmireAbstract:Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill in this gap, we present a deep learning (DL) based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We train the DL based, survival-sensitive model on 360 HCC patient data using RNA-seq, miRNA-seq and methylation data from TCGA. This model provides two optimal subgroups of patients with significant survival differences (P=7.13e-6) and good model fitness (C-index=0.68). More aggressive subtype is associated with frequent TP53 inactivation mutations, higher expression of stemness markers (KRT19, EPCAM) and tumor marker BIRC5, and activated Wnt and Akt signaling pathways. We validated this multi-omics model on five external datasets of various omics types: LIRI-JP cohort (n=230, c-index=0.75), NCI cohort (n=221, c-index=0.67), Chinese cohort (n=166, c-index=0.69), E-TABM-36 cohort (n=40, c-index=0.77), and Hawaiian cohort (n=27, c-index=0.82). This is the first study to employ deep learning to identify multi-omics features linked to the differential survival of HCC patients. Given its robustness over multiple cohorts, we expect this model to be clinically useful for HCC prognosis prediction.
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deep learning based multi omics integration robustly predicts survival in liver cancer
Clinical Cancer Research, 2017Co-Authors: Kumardeep Chaudhary, Olivier Poirion, Liangqun Lu, Lana X GarmireAbstract:Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill this gap, we present a deep learning (DL)–based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We built the DL-based, survival-sensitive model on 360 HCC patients9 data using RNA sequencing (RNA-Seq), miRNA sequencing (miRNA-Seq), and methylation data from The Cancer Genome Atlas (TCGA), which predicts prognosis as good as an alternative model where genomics and clinical data are both considered. This DL-based model provides two optimal subgroups of patients with significant survival differences (P = 7.13e−6) and good model fitness [concordance index (C-index) = 0.68]. More aggressive subtype is associated with frequent TP53 inactivation mutations, higher expression of stemness markers (KRT19 and EPCAM) and tumor marker BIRC5, and activated Wnt and Akt signaling pathways. We validated this multi-omics model on five external datasets of various omics types: LIRI-JP cohort (n = 230, C-index = 0.75), NCI cohort (n = 221, C-index = 0.67), Chinese cohort (n = 166, C-index = 0.69), E-TABM-36 cohort (n = 40, C-index = 0.77), and Hawaiian cohort (n = 27, C-index = 0.82). This is the first study to employ DL to identify multi-omics features linked to the differential survival of patients with HCC. Given its robustness over multiple cohorts, we expect this workflow to be useful at predicting HCC prognosis prediction. Clin Cancer Res; 24(6); 1248–59. ©2017 AACR.
Qi Tian - One of the best experts on this subject based on the ideXlab platform.
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effective image retrieval via multilinear multi index fusion
IEEE Transactions on Multimedia, 2019Co-Authors: Zhizhong Zhang, Yuan Xie, Wensheng Zhang, Qi TianAbstract:Multi-Index fusion has demonstrated impressive performances in the retrieval task by integrating different visual representations in a unified framework. However, previous works mainly consider propagating similarities via a neighbor structure, ignoring the high-order information among different visual representations. In this paper, we propose a new Multi-Index fusion scheme for image retrieval. By formulating this procedure as a multilinear-based optimization problem, the complementary information hidden in different indexes can be explored more thoroughly. Specifically, we first build our multiple indexes from various visual representations. Then, a so-called index-specific functional matrix, which aims to propagate similarities, is introduced to update the original index. The functional matrices are then optimized in a unified tensor space to achieve a refinement, such that the relevant images can be pushed closer. The optimization problem can be efficiently solved by the augmented Lagrangian method with a theoretical convergence guarantee. Unlike the traditional Multi-Index fusion scheme, our approach embeds the Multi-Index subspace structure into the new indexes with sparse constraint and, thus, it has little additional memory consumption in the online query stage. Experimental evaluation on three benchmark datasets reveals that the proposed approach achieves state-of-the-art performance, that is, N-score 3.94 on UKBench, mAP 94.1% on Holiday, and 62.39% on Market-1501.
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multi index fusion via similarity matrix pooling for image retrieval
International Conference on Communications, 2017Co-Authors: Xin Chen, Shaoyan Sun, Qi TianAbstract:Different kinds of features hold some distinct merits, making them complementary to each other. Inspired by this idea an index level multiple feature fusion scheme via similarity matrix pooling is proposed in this paper. We first compute the similarity matrix of each index, and then a novel scheme is used to pool on these similarity matrices for updating the original indices. Compared with the existing fusion schemes, the proposed scheme performs feature fusion at index level to save memory and reduce computational complexity. On the other hand, the proposed scheme treats different kinds of features adaptively based on its importance, thus improves retrieval accuracy. The performance of the proposed approach is evaluated using two public datasets, which significantly outperforms the baseline methods in retrieval accuracy with low memory consumption and computational complexity.
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packing and padding coupled multi index for accurate image retrieval
arXiv: Computer Vision and Pattern Recognition, 2014Co-Authors: Liang Zheng, Shengjin Wang, Ziqiong Liu, Qi TianAbstract:In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low discriminative power, so false positive matches occur prevalently. Apart from the information loss during quantization, another cause is that the SIFT feature only describes the local gradient distribution. To address this problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform feature fusion at indexing level. Basically, complementary features are coupled into a multi-dimensional inverted index. Each dimension of c-MI corresponds to one kind of feature, and the retrieval process votes for images similar in both SIFT and other feature spaces. Specifically, we exploit the fusion of local color feature into c-MI. While the precision of visual match is greatly enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation of SIFT and color features significantly reduces the impact of false positive matches. Extensive experiments on several benchmark datasets demonstrate that c-MI improves the retrieval accuracy significantly, while consuming only half of the query time compared to the baseline. Importantly, we show that c-MI is well complementary to many prior techniques. Assembling these methods, we have obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench datasets, respectively, which compare favorably with the state-of-the-arts.
Kenji Omasa - One of the best experts on this subject based on the ideXlab platform.
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a multi angular invariant spectral index for the estimation of leaf water content across a wide range of plant species in different growth stages
Remote Sensing of Environment, 2021Co-Authors: Zhongqiu Sun, Kenji OmasaAbstract:Abstract Plant leaf water content plays a key role in several biogeochemical processes, such as photosynthesis, evapotranspiration, and net primary production. Yet, the accurate estimation of leaf water content using multi-angular reflectance measurements across different plant species is still challenging. This study aims to propose a generic spectral index for accurately estimating equivalent water thickness (EWT) when multi-angular spectral reflection is considered. The index was selected to have the format of a difference ratio using three reflectance factors. The reflectance factor at 410 nm was used to reduce the specular reflection from the leaf surface in the 400–2500 nm range, and the ratio of the wavelengths in the 1268–1285 nm range (at non-water absorption wavelengths) to the wavelengths in the 1339–1346 nm range (at water absorption wavelengths) strengthened the relationship with EWT for all of the sampled plant species. The modified difference ratio (MDR) index, (R1271-R410)/(R1342-R410), was linearly proportional to EWT, with R2 > 0.90, when the leaves reflection data were collected from various viewing angles. However, the relationships between some existing indices (simple difference, simple ratio, normalized difference, double difference index and difference ratio indices) and EWT at the leaf level were weak and unstable for all of the 18 plant species (including 14 broadleaf, 3 shrub, and 1 liana species) at different angles under laboratory and field conditions. Moreover, validation results from six independent datasets (n = 1800) and one modeled dataset (n = 2375) further confirmed that the algorithm derived from the proposed index (based on multi-angular reflectance factors of leaves) was not only effective for EWT estimation across a diverse set of plant species with widely variable leaf structure and water content, but also insensitive to different measurement conditions (leaf clip, integrating spheres or multi-angle measurements). The algorithm developed from this new index is generic, does not require reparameterization for each species, and can be accurately used for nondestructive EWT estimations using a simple handheld laboratory or field instrument, and thus, is convenient for agricultural and ecological studies.
Liangqun Lu - One of the best experts on this subject based on the ideXlab platform.
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deep learning based multi omics integration robustly predicts survival in liver cancer
bioRxiv, 2017Co-Authors: Kumardeep Chaudhary, Olivier Poirion, Liangqun Lu, Lana X GarmireAbstract:Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill in this gap, we present a deep learning (DL) based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We train the DL based, survival-sensitive model on 360 HCC patient data using RNA-seq, miRNA-seq and methylation data from TCGA. This model provides two optimal subgroups of patients with significant survival differences (P=7.13e-6) and good model fitness (C-index=0.68). More aggressive subtype is associated with frequent TP53 inactivation mutations, higher expression of stemness markers (KRT19, EPCAM) and tumor marker BIRC5, and activated Wnt and Akt signaling pathways. We validated this multi-omics model on five external datasets of various omics types: LIRI-JP cohort (n=230, c-index=0.75), NCI cohort (n=221, c-index=0.67), Chinese cohort (n=166, c-index=0.69), E-TABM-36 cohort (n=40, c-index=0.77), and Hawaiian cohort (n=27, c-index=0.82). This is the first study to employ deep learning to identify multi-omics features linked to the differential survival of HCC patients. Given its robustness over multiple cohorts, we expect this model to be clinically useful for HCC prognosis prediction.
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deep learning based multi omics integration robustly predicts survival in liver cancer
Clinical Cancer Research, 2017Co-Authors: Kumardeep Chaudhary, Olivier Poirion, Liangqun Lu, Lana X GarmireAbstract:Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill this gap, we present a deep learning (DL)–based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We built the DL-based, survival-sensitive model on 360 HCC patients9 data using RNA sequencing (RNA-Seq), miRNA sequencing (miRNA-Seq), and methylation data from The Cancer Genome Atlas (TCGA), which predicts prognosis as good as an alternative model where genomics and clinical data are both considered. This DL-based model provides two optimal subgroups of patients with significant survival differences (P = 7.13e−6) and good model fitness [concordance index (C-index) = 0.68]. More aggressive subtype is associated with frequent TP53 inactivation mutations, higher expression of stemness markers (KRT19 and EPCAM) and tumor marker BIRC5, and activated Wnt and Akt signaling pathways. We validated this multi-omics model on five external datasets of various omics types: LIRI-JP cohort (n = 230, C-index = 0.75), NCI cohort (n = 221, C-index = 0.67), Chinese cohort (n = 166, C-index = 0.69), E-TABM-36 cohort (n = 40, C-index = 0.77), and Hawaiian cohort (n = 27, C-index = 0.82). This is the first study to employ DL to identify multi-omics features linked to the differential survival of patients with HCC. Given its robustness over multiple cohorts, we expect this workflow to be useful at predicting HCC prognosis prediction. Clin Cancer Res; 24(6); 1248–59. ©2017 AACR.
Kumardeep Chaudhary - One of the best experts on this subject based on the ideXlab platform.
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deep learning based multi omics integration robustly predicts survival in liver cancer
bioRxiv, 2017Co-Authors: Kumardeep Chaudhary, Olivier Poirion, Liangqun Lu, Lana X GarmireAbstract:Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill in this gap, we present a deep learning (DL) based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We train the DL based, survival-sensitive model on 360 HCC patient data using RNA-seq, miRNA-seq and methylation data from TCGA. This model provides two optimal subgroups of patients with significant survival differences (P=7.13e-6) and good model fitness (C-index=0.68). More aggressive subtype is associated with frequent TP53 inactivation mutations, higher expression of stemness markers (KRT19, EPCAM) and tumor marker BIRC5, and activated Wnt and Akt signaling pathways. We validated this multi-omics model on five external datasets of various omics types: LIRI-JP cohort (n=230, c-index=0.75), NCI cohort (n=221, c-index=0.67), Chinese cohort (n=166, c-index=0.69), E-TABM-36 cohort (n=40, c-index=0.77), and Hawaiian cohort (n=27, c-index=0.82). This is the first study to employ deep learning to identify multi-omics features linked to the differential survival of HCC patients. Given its robustness over multiple cohorts, we expect this model to be clinically useful for HCC prognosis prediction.
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deep learning based multi omics integration robustly predicts survival in liver cancer
Clinical Cancer Research, 2017Co-Authors: Kumardeep Chaudhary, Olivier Poirion, Liangqun Lu, Lana X GarmireAbstract:Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill this gap, we present a deep learning (DL)–based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We built the DL-based, survival-sensitive model on 360 HCC patients9 data using RNA sequencing (RNA-Seq), miRNA sequencing (miRNA-Seq), and methylation data from The Cancer Genome Atlas (TCGA), which predicts prognosis as good as an alternative model where genomics and clinical data are both considered. This DL-based model provides two optimal subgroups of patients with significant survival differences (P = 7.13e−6) and good model fitness [concordance index (C-index) = 0.68]. More aggressive subtype is associated with frequent TP53 inactivation mutations, higher expression of stemness markers (KRT19 and EPCAM) and tumor marker BIRC5, and activated Wnt and Akt signaling pathways. We validated this multi-omics model on five external datasets of various omics types: LIRI-JP cohort (n = 230, C-index = 0.75), NCI cohort (n = 221, C-index = 0.67), Chinese cohort (n = 166, C-index = 0.69), E-TABM-36 cohort (n = 40, C-index = 0.77), and Hawaiian cohort (n = 27, C-index = 0.82). This is the first study to employ DL to identify multi-omics features linked to the differential survival of patients with HCC. Given its robustness over multiple cohorts, we expect this workflow to be useful at predicting HCC prognosis prediction. Clin Cancer Res; 24(6); 1248–59. ©2017 AACR.