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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
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DTSTART:20210101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220805T130000
DTEND;TZID=Asia/Seoul:20220805T140000
DTSTAMP:20260425T025223
CREATED:20220804T190000Z
LAST-MODIFIED:20220729T014246Z
UID:6341-1659704400-1659708000@www.ibs.re.kr
SUMMARY:Neural Ordinary Differential Equations
DESCRIPTION:We will discuss about “Neural Ordinary Differential Equations”\, Chen\, Ricky TQ\, et al.\, Advances in neural information processing systems 31 (2018). \nAbstract: We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers\, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a blackbox differential equation solver. These continuous-depth models have constant memory cost\, adapt their evaluation strategy to each input\, and can explicitly trade numerical precision for speed. We demonstrate these properties in continuous-depth residual networks and continuous-time latent variable models. We also construct continuous normalizing flows\, a generative model that can train by maximum likelihood\, without partitioning or ordering the data dimensions. For training\, we show how to scalably backpropagate through any ODE solver\, without access to its internal operations. This allows end-to-end training of ODEs within larger models.
URL:https://www.ibs.re.kr/bimag/event/2022-08-05-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220812T130000
DTEND;TZID=Asia/Seoul:20220812T140000
DTSTAMP:20260425T025223
CREATED:20220811T190000Z
LAST-MODIFIED:20220728T092951Z
UID:6338-1660309200-1660312800@www.ibs.re.kr
SUMMARY:Molecular convolutional neural networks with DNA regulatory circuits
DESCRIPTION:We will discuss about “Molecular convolutional neural networks with DNA regulatory circuits”\, Pei\, Hao\, et al.\, Nature Machine Intelligence (2022): 1-11. \nAbstract: Complex biomolecular circuits enabled cells with intelligent behaviour to survive before neural brains evolved. Since DNA computing was first demonstrated in the mid-1990s\, synthetic DNA circuits in liquid phase have been developed as computational hardware to perform neural network-like computations that harness the collective properties of complex biochemical systems. However\, scaling up such DNA-based neural networks to support more powerful computation remains challenging. Here we present a systematic molecular implementation of a convolutional neural network algorithm with synthetic DNA regulatory circuits based on a simple switching gate architecture. Our DNA-based weight-sharing convolutional neural network can simultaneously implement parallel multiply–accumulate operations for 144-bit inputs and recognize patterns in up to eight categories autonomously. Further\, this system can be connected with other DNA circuits to construct hierarchical networks to recognize patterns in up to 32 categories with a two-step approach: coarse classification on language (Arabic numerals\, Chinese oracles\, English alphabets and Greek alphabets) followed by classification into specific handwritten symbols. We also reduced the computation time from hours to minutes by using a simple cyclic freeze–thaw approach. Our DNA-based regulatory circuits are a step towards the realization of a molecular computer with high computing power and the ability to classify complex and noisy information.
URL:https://www.ibs.re.kr/bimag/event/2022-08-12-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220816T100000
DTEND;TZID=Asia/Seoul:20220816T110000
DTSTAMP:20260425T025223
CREATED:20220815T160000Z
LAST-MODIFIED:20220815T124820Z
UID:6376-1660644000-1660647600@www.ibs.re.kr
SUMMARY:Circadian Interventions in Shift Workers
DESCRIPTION:This talk will be given online (If you want to join\, please send me an email to jaekkim@ibs.re.kr) \nAbstract \nCoupling Math with User-Centric Design Shift workers experience profound circadian disruption due to the nature of their work\, which often has them on-the-clock at times when their internal clock is sending a strong\, sleep-promoting signal. Mathematical models can be used to generate recommendations for shift workers that move their internal clock state to better align with their work schedules\, promote overall sleep\, promote alertness at key times\, or achieve other desired outcomes. Yet for these schedules to have a positive effect in the real world\, they need to be acceptable to the shift workers themselves. In this talk\, I will survey the types of schedules a shift worker may be recommended by an algorithm\, and how they can collide with the preferences of the real people being asked to follow them\, and how math can be used to arrive at new schedules that take these human factors into account.
URL:https://www.ibs.re.kr/bimag/event/2022-08-16-seminar/
LOCATION:Daejeon
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220826T130000
DTEND;TZID=Asia/Seoul:20220826T140000
DTSTAMP:20260425T025223
CREATED:20220825T190000Z
LAST-MODIFIED:20220825T155707Z
UID:6348-1661518800-1661522400@www.ibs.re.kr
SUMMARY:Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
DESCRIPTION:We will discuss about “Inferring Regulatory Networks from Expression Data Using Tree-Based Methods\,” Huynh-Thu et al.\, PLoS ONE (2010). \nAbstract: One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data\, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article\, we present GENIE3\, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems\, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes)\, using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data\, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn’t make any assumption about the nature of gene regulation\, can deal with combinatorial and non-linear interactions\, produces directed GRNs\, and is fast and scalable. In conclusion\, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm\, based on feature selection with tree-based ensemble methods\, is simple and generic\, making it adaptable to other types of genomic data and interactions.
URL:https://www.ibs.re.kr/bimag/event/2022-08-26-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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