Results 71 to 80 of about 3,746,359 (297)

Structural insights into lacto‐N‐biose I recognition by a family 32 carbohydrate‐binding module from Bifidobacterium bifidum

FEBS Letters, EarlyView.
Bifidobacterium bifidum establishes symbiosis with infants by metabolizing lacto‐N‐biose I (LNB) from human milk oligosaccharides (HMOs). The extracellular multidomain enzyme LnbB drives this process, releasing LNB via its catalytic glycoside hydrolase family 20 (GH20) lacto‐N‐biosidase domain.
Xinzhe Zhang, Naoki Sunagawa, Toma Kashima, Kiyohiko Igarashi, Akimasa Miyanaga, Shinya Fushinobu   +5 more
wiley   +1 more source

Deep learning for supervised classification [PDF]

, 2016
One of the most recent area in the Machine Learning research is Deep Learning. Deep Learning algorithms have been applied successfully to computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics ...
DI CIACCIO, AGOSTINO, GIORGI, Giovanni Maria   +1 more
core  

Protein pyrophosphorylation by inositol pyrophosphates — detection, function, and regulation

FEBS Letters, EarlyView.
Protein pyrophosphorylation is an unusual signaling mechanism that was discovered two decades ago. It can be driven by inositol pyrophosphate messengers and influences various cellular processes. Herein, we summarize the research progress and challenges of this field, covering pathways found to be regulated by this posttranslational modification as ...
Sarah Lampe, Tanmay Kumar Mohanty, Rashna Bhandari, Dorothea Fiedler   +3 more
wiley   +1 more source

MalSSL—Self-Supervised Learning for Accurate and Label-Efficient Malware Classification

IEEE Access
Malware classification with supervised learning requires a large dataset, which needs an expensive and time-consuming labeling process. In this paper, we explore the efficacy of self-supervised learning techniques for malware classification.
Setia Juli Irzal Ismail, Hendrawan, Budi Rahardjo, Tutun Juhana, Yasuo Musashi   +4 more
doaj   +1 more source

Classification Algorithm and Application Based on Semi-Supervised Deep Auto-Encoder Network [PDF]

Jisuanji gongcheng
In industrial classification prediction, labeled data are scarce, and labeling is expensive, leading to inaccurate model predictions. Simultaneously, features in most unlabeled data are not effectively used, resulting in insufficient generalization of ...
ZHANG Xinbo, ZHANG Xueying, HUANG Lixia, CHEN Guijun
doaj   +1 more source

Tree Species Classification Based on Self-Supervised Learning with Multisource Remote Sensing Images

Applied Sciences, 2023
In order to solve the problem of manual labeling in semi-supervised tree species classification, this paper proposes a pixel-level self-supervised learning model named M-SSL (multisource self-supervised learning), which takes the advantage of the ...
Xueliang Wang, Nan Yang, Enjun Liu, Wencheng Gu, Jinglin Zhang, Shuo Zhao, Guijiang Sun, Jian Wang   +7 more
doaj   +1 more source

Implicitly Constrained Semi-Supervised Least Squares Classification

, 2015
We introduce a novel semi-supervised version of the least squares classifier. This implicitly constrained least squares (ICLS) classifier minimizes the squared loss on the labeled data among the set of parameters implied by all possible labelings of the ...
B Widrow, GJ McLachlan, K Nigam, KP Bennett, L Bottou, M Loog, M Loog, M Opper, O Chapelle, R Rifkin, R Tibshirani, RH Byrd, S Raudys, T Hastie, T Poggio, X Zhu, YF Li   +16 more
core   +1 more source

Substrate specificity of Burkholderia pseudomallei multidrug transporters is influenced by the hydrophilic patch in the substrate‐binding pocket

FEBS Letters, EarlyView.
Multidrug transporters BpeB and BpeF from the Gram‐negative pathogen Burkholderia pseudomallei have a hydrophilic patch in their substrate‐binding pocket. Drug susceptibility tests and growth curve analyses using an Escherichia coli recombinant expression system revealed that the hydrophilic patches of BpeB and BpeF are involved in the substrate ...
Ui Okada, Satoshi Murakami
wiley   +1 more source

SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification

Frontiers in Artificial Intelligence
IntroductionIn clinical, the echocardiogram is the most widely used for diagnosing heart diseases. Different heart diseases are diagnosed based on different views of the echocardiogram images, so efficient echocardiogram view classification can help ...
Shizhou Ma, Yifeng Zhang, Delong Li, Yixin Sun, Zhaowen Qiu, Lei Wei, Suyu Dong   +6 more
doaj   +1 more source

Effectiveness of Semi-Supervised Learning and Multi-Source Data in Detailed Urban Landuse Mapping with a Few Labeled Samples

Remote Sensing, 2022
Detailed urban landuse information plays a fundamental role in smart city management. A sufficient sample size has been identified as a very crucial pre-request in machine learning algorithms for urban landuse classification.
Bo Sun, Yang Zhang, Qiming Zhou, Xinchang Zhang   +3 more
doaj   +1 more source