Computational Approaches for Understanding and Redesigning Enzyme Catalysis by Karvelis, Elijah

“…The approach combined statistical mechanical path sampling algorithms and machine learning methods to identify the structural characteristics of enzyme-substrate complexes primed for successful conversion of substrate to product, which were then energetically stabilized by mutating KARI. …”
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Transforming kernel-based learners to incorporate domain knowledge from climate science by Bouabid, S

“…<p>In the face of persistent modelling and observational challenges in climate science, which hinder our understanding of the climate system, statistical machine learning has emerged as a potential ally in recent years. Modern machine learning methods promise to leverage the vast volumes of data from climate model simulations, satellite imagery, or in-situ measurements to advance our understanding of the climate system and, thereby, our ability to anticipate the adverse consequences of climate change. …”

A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics by Barthel Sorensen, B., Charalampopoulos, A., Zhang, S., Harrop, B. E., Leung, L. R., Sapsis, T. P.

“…Here, the scope is to formulate a learning method that allows for correction of dynamics and quantification of extreme events with longer return period than the training data. …”
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Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity by Zhang, Zhongheng, Chen, Lin, Liu, Xiaoli, Yang, Jie, Huang, Jiajie, Yang, Qiling, Hu, Qichao, Jin, Ketao, Celi, Leo A., Hong, Yucai

“…The top-down transfer learning method (model trained on cohorts with greater severity was transferred to cohorts with lower severity score) had a higher NMI value than the bottom-up approach (median [Q1, Q3]: 0.64 [0.49, 0.78] vs. 0.23 [0.2, 0.31], p < 0.001). …”
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Hermes: Boosting the Performance of Machine-Learning-Based Intrusion Detection System through Geometric Feature Learning by Zhang, Chaoyu, Shi, Shanghao, Wang, Ning, Xu, Xiangxiang, Li, Shaoyu, Zheng, Lizhong, Marchany, Randy, Gardner, Mark, Hou, Y. Thomas, Lou, Wenjing

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Visual event recognition in videos by learning from web data by Duan, Lixin, Xu, Dong, Tsang, Ivor Wai-Hung, Luo, Jiebo

“…Second, we propose a new transfer learning method, referred to as Adaptive Multiple Kernel Learning (A-MKL), in order to 1) fuse the information from multiple pyramid levels and features (i.e., space-time features and static SIFT features) and 2) cope with the considerable variation in feature distributions between videos from two domains (i.e., web video domain and consumer video domain). …”
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Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm by John, Ayuba, Isnin, Ismail Fauzi, Madni, Syed Hamid Hussain, Muchtar, Farkhana

“…This paper proposes a variable ensemble machine learning method to solve the problem and achieve a low variance model with high accuracy and low false alarm. …”
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Self-supervised Self2Self denoising strategy for OCT speckle reduction with a single noisy image by Ge, Chenkun, Yu, Xiaojun, Yuan, Miao, Fan, Zeming, Chen, Jinna, Shum, Perry Ping, Liu, Linbo

“…Results compared with those of the existing methods demonstrate that S2Snet not only outperforms those existing self-supervised deep learning methods but also achieves better performances than those non-deep learning ones in different cases. …”
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Measuring the predictability of life outcomes with a scientific mass collaboration by Salganik, Matthew J., Lundberg, Ian, Kindel, Alexander T., Ahearn, Caitlin E., Al-Ghoneim, Khaled, Almaatouq, Abdullah, Altschul, Drew M., Brand, Jennie E., Carnegie, Nicole Bohme, Compton, Ryan James, Datta, Debanjan, Davidson, Thomas, Filippova, Anna, Gilroy, Connor, Goode, Brian J., Jahani, Eaman, Kashyap, Ridhi, Kirchner, Antje, McKay, Stephen, Morgan, Allison C., Pentland, Alex, Polimis, Kivan, Raes, Louis, Rigobon, Daniel E., Roberts, Claudia V., Stanescu, Diana M., Suhara, Yoshihiko, Usmani, Adaner, Wang, Erik H., Adem, Muna, Alhajri, Abdulla, AlShebli, Bedoor, Amin, Redwane, Amos, Ryan B., Argyle, Lisa P., Baer-Bositis, Livia, Buchi, Moritz, Chung, Bo-Ryehn, Eggert, William, Faletto, Gregory, Fan, Zhilin, Freese, Jeremy, Gadgil, Tejomay, Gagne ́, Josh, Gao, Yue, Halpern-Manners, Andrew, Hashim, Sonia P., Hausen, Sonia, He, Guanhua, Higuera, Kimberly, Hogan, Bernie, Horwitz, Ilana M., Hummel, Lisa M., Jain, Naman, Jin, Kun, Jurgens, David, Kaminski, Patrick, Karapetyan, Areg, Kim, E. H., Leizman, Ben, Liu, Naijia, Moser, Malte, Mack, Andrew E., Mahajan, Mayank, Mandell, Noah, Marahrens, Helge, Mercado-Garcia, Diana, Mocz, Viola, Mueller-Gastell, Katariina, Musse, Ahmed, Niu, Qiankun, Nowak, William, Omidvar, Hamidreza, Or, Andrew, Ouyang, Karen, Pinto, Katy M., Porter, Ethan, Porter, Kristin E., Qian, Crystal, Rauf, Tamkinat, Sargsyan, Anahit, Schaffner, Thomas, Schnabel, Landon, Schonfeld, Bryan, Sender, Ben, Tang, Jonathan D., Tsurkov, Emma, van Loon, Austin, Varol, Onur, Wang, Xiafei, Wang, Zhi, Wang, Julia, Wang, Flora, Weissman, Samantha, Whitaker, Kirstie, Wolters, Maria K., Woon, Wei Lee, Wu, James, Wu, Catherine, Yang, Kengran, Yin, Jingwen, Zhao, Bingyu, Zhu, Chenyun, Brooks-Gunn, Jeanne, Engelhardt, Barbara E., Hardt, Moritz, Knox, Dean, Levy, Karen, Narayanan, Arvind, Stewart, Brandon M., Watts, Duncan J., McLanahan, Sara

“…Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. …”
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Building occupant sensing : occupancy prediction and behavior recognition by Zhu, Qingchang

“…To achieve these goals in smart buildings, it is necessary to study the problem of occupant sensing by leveraging machine learning methods to understand occupants based on sensor signals. …”
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Distinctive antibody responses to Mycobacterium tuberculosis in pulmonary and brain infection by Spatola, M, Nziza, N, Irvine, EB, Cizmeci, D, Jung, W, Van, LH, Nhat, LTH, Ha, VTN, Phu, NH, Nghia, HDT, Thwaites, GE, Lauffenburger, DA, Fortune, S, Thuong, NTT, Alter, G

“…Antibody studies included analysis of immunoglobulin isotypes (IgG, IgM, IgA) and subclass levels (IgG1–4) and the capacity of <i>M. tuberculosis</i>-specific antibodies to bind to Fc receptors or C1q and to activate innate immune effector functions (complement and natural killer cell activation; monocyte or neutrophil phagocytosis). Machine learning methods were applied to characterize serum and CSF responses in TBM, identify prognostic factors associated with disease severity, and define the key antibody features that distinguish TBM from pulmonary TB. …”

Microbial communities: network reconstruction and control by Fu, A

“…It proposes adaptive learning methods and experimental design rules to transform PAG-inferred structures into fully identified causal models, thus enhancing our understanding of microbial dynamics and providing a systematic approach for future research in causal inference within complex biological systems. …”

Speeding up deep neural network training with decoupled and analytic learning by Zhuang, Huiping

“…A fully decoupled learning method using delayed gradients (FDG) is first proposed which addresses all the three lockings. …”
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Feature extraction from EEG signals and regularization for brain-computer interface by Mishuhina, Vasilisa

“…The goal of this research is to improve feature extraction and regularization of EEG signals using machine learning methods and hence achieve better results during the classification of the signals for motor imagery BCI (MI-BCI). …”
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Natural robustness of machine learning in the open world by Wei, Hongxin

“…Secondly, classic machine learning methods are built on the i.i.d. assumption that training and testing data are independent and identically distributed. …”
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High cycle fatigue characterisation and modelling of 316L stainless steel processed by laser powder bed fusion by Zhang, Meng

“…Lastly, considering the numerous influencing factors arising from the process and the associated failure behaviours, a neuro-fuzzy-based machine learning method was applied to provide an effective unifying approach for high cycle fatigue life prediction. …”
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Digital problem-based learning in health professions : systematic review and meta-analysis by the digital health education collaboration by Car, Lorainne Tudor, Kyaw, Bhone Myint, Dunleavy, Gerard, Smart, Neil A., Semwal, Monika, Rotgans, Jerome Ingmar, Low-Beer, Naomi, Campbell, James

“…We included studies that compared the effectiveness of DPBL with traditional learning methods or other forms of digital education in improving health professionals’ knowledge, skills, attitudes, and satisfaction. …”
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Sensor-based human activity recognition via zero-shot learning by Wang, Wei

“…For problems under this problem setting, as there are no labeled training instances belonging to the unseen classes, the zero-shot learning methods are used. We focus on three problems under this setting. …”
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Topics in Bayesian machine learning for finance by Spears, T

“…Further, we estimate an approximation to epistemic uncertainty via a pseudo-Bayesian deep learning method. This work demonstrates the utility of the model output for deciding the relative allocation of risk capital across trades. …”

Brain Informatics : 14th International Conference, BI 2021, Virtual Event, September 17-19, 2021, Proceedings / by International Conference on Brain Informatics (14th : 2021 : Online), organiser 643024, Mahmud, Mufti, editor 643025, Kaiser, M. Shamim, editor 643026, Vassanelli, Stefano, editor 643027, Dai, Qionghai, 1964-, editor 643028, Zhong, Ning, 1956-, editor 643029