Efficient Automated Deep Learning for Time Series Forecasting

Recent years have witnessed tremendously improved efficiency of Automated Machine Learning (AutoML), especially Automated Deep Learning (AutoDL) systems, but recent work focuses on tabular, image, or NLP tasks. So far, little attention has been paid to general AutoDL frameworks for time series forecasting, despite the enormous success in applying different novel architectures to such tasks. In this paper, we propose an efficient approach for the joint optimization of neural architecture and hyperparameters of the entire data processing pipeline for time series forecasting. In contrast to common NAS search spaces, we designed a novel neural architecture search space covering various state-of-the-art architectures, allowing for an efficient macro-search over different DL approaches. To efficiently search in such a large configuration space, we use Bayesian optimization with multi-fidelity optimization. We empirically study several different budget types enabling efficient multi-fidelity optimization on different forecasting datasets. Furthermore, we compared our resulting system, dubbed \system, against several established baselines and show that it significantly outperforms all of them across several datasets

Quantitative analysis of transient and sustained transforming growth factor-β signaling dynamics

Mathematical modeling and experimental analyses reveal that TGF-β ligand depletion has an important role in converting short-term graded signaling responses to long-term switch-like responses

Cell-type-specific role of CHK2 in mediating DNA damage-induced G2 cell cycle arrest

Cancer is a life-threatening disease that affects one in three people. Although most cases are sporadic, cancer risk can be increased by genetic factors. It remains unknown why certain genes predispose for specific forms of cancer only, such as checkpoint protein 2 (CHK2), in which gene mutations convey up to twofold higher risk for breast cancer but do not increase lung cancer risk. We have investigated the role of CHK2 and the related kinase checkpoint protein 1 (CHK1) in cell cycle regulation in primary breast and lung primary epithelial cells. At the molecular level, CHK1 activity was higher in lung cells, whereas CHK2 was more active in breast cells. Inhibition of CHK1 profoundly disrupted the cell cycle profile in both lung and breast cells, whereas breast cells were more sensitive toward inhibition of CHK2. Finally, we provide evidence that breast cells require CHK2 to induce a G2–M cell cycle arrest in response of DNA damage, whereas lung cells can partially compensate for the loss of CHK2. Our results provide an explanation as to why CHK2 germline mutations predispose for breast cancer but not for lung cancer

AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks

The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years. In NLP, especially Large Language Models (LLMs) have experienced a rapid series of breakthroughs very recently. We envision that the two fields can radically push the boundaries of each other through tight integration. To showcase this vision, we explore the potential of a symbiotic relationship between AutoML and LLMs, shedding light on how they can benefit each other. In particular, we investigate both the opportunities to enhance AutoML approaches with LLMs from different perspectives and the challenges of leveraging AutoML to further improve LLMs. To this end, we survey existing work, and we critically assess risks. We strongly believe that the integration of the two fields has the potential to disrupt both fields, NLP and AutoML. By highlighting conceivable synergies, but also risks, we aim to foster further exploration at the intersection of AutoML and LLMs

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges

Most machine learning algorithms are configured by a set of hyperparameters whose values must be carefully chosen and which often considerably impact performance. To avoid a time-consuming and irreproducible manual process of trial-and-error to find well-performing hyperparameter configurations, various automatic hyperparameter optimization (HPO) methods—for example, based on resampling error estimation for supervised machine learning—can be employed. After introducing HPO from a general perspective, this paper reviews important HPO methods, from simple techniques such as grid or random search to more advanced methods like evolution strategies, Bayesian optimization, Hyperband, and racing. This work gives practical recommendations regarding important choices to be made when conducting HPO, including the HPO algorithms themselves, performance evaluation, how to combine HPO with machine learning pipelines, runtime improvements, and parallelization. This article is categorized under: Algorithmic Development > Statistics Technologies > Machine Learning Technologies > Prediction

Tissue-Specific Chk1 Activation Determines Apoptosis by Regulating the Balance of p53 and p21

Summary: The DNA damage response (DDR) protects cells against genomic instability. Surprisingly, little is known about the differences in DDR across tissues, which may affect cancer evolutionary trajectories and chemotherapy response. Using mathematical modeling and quantitative experiments, we found that the DDR is regulated differently in human breast and lung primary cells. Equal levels of cisplatin-DNA lesions caused stronger Chk1 activation in lung cells, leading to resistance. In contrast, breast cells were more resistant and showed more Chk2 activation in response to doxorubicin. Further analyses indicate that Chk1 activity played a regulatory role in p53 phosphorylation, whereas Chk2 activity was essential for p53 activation and p21 expression. We propose a novel “friction model,” in which the balance of p53 and p21 levels contributes to the apoptotic response in different tissues. Our results suggest that modulating the balance of p53 and p21 dynamics could optimize the response to chemotherapy. : Bioinformatics; Mathematical Biosciences; Systems Biology; Cancer Systems Biology Subject Areas: Bioinformatics, Mathematical Biosciences, Systems Biology, Cancer Systems Biolog

Liebig's law of the minimum in the TGF-β/SMAD pathway.

Cells use signaling pathways to sense and respond to their environments. The transforming growth factor-β (TGF-β) pathway produces context-specific responses. Here, we combined modeling and experimental analysis to study the dependence of the output of the TGF-β pathway on the abundance of signaling molecules in the pathway. We showed that the TGF-β pathway processes the variation of TGF-β receptor abundance using Liebig's law of the minimum, meaning that the output-modifying factor is the signaling protein that is most limited, to determine signaling responses across cell types and in single cells. We found that the abundance of either the type I (TGFBR1) or type II (TGFBR2) TGF-β receptor determined the responses of cancer cell lines, such that the receptor with relatively low abundance dictates the response. Furthermore, nuclear SMAD2 signaling correlated with the abundance of TGF-β receptor in single cells depending on the relative expression levels of TGFBR1 and TGFBR2. A similar control principle could govern the heterogeneity of signaling responses in other signaling pathways

Spatiotemporal Control of TGF‑β Signaling with Light

Cells employ signaling pathways to make decisions in response to changes in their immediate environment. Transforming growth factor beta (TGF-β) is an important growth factor that regulates many cellular functions in development and disease. Although the molecular mechanisms of TGF-β signaling have been well studied, our understanding of this pathway is limited by the lack of tools that allow the control of TGF-β signaling with high spatiotemporal resolution. Here, we developed an optogenetic system (optoTGFBRs) that enables the precise control of TGF-β signaling in time and space. Using the optoTGFBRs system, we show that TGF-β signaling can be selectively and sequentially activated in single cells through the modulation of the pattern of light stimulations. By simultaneously monitoring the subcellular localization of TGF-β receptor and Smad2 proteins, we characterized the dynamics of TGF-β signaling in response to different patterns of blue light stimulations. The spatial and temporal precision of light control will make the optoTGFBRs system as a powerful tool for quantitative analyses of TGF-β signaling at the single cell level