We aim to establish a prediction model for pregnancy outcomes through a combinatorial analysis of circulating biomarkers and maternal characteristics to effectively identify pregnant women with higher risks of preeclampsia in the first and third trim...
Background: During the previous decade a new class of benzamide-based inhibitors of 2-trans enoyl-acyl carrier protein reductase (InhA) of Mycobacterium tuberculosis (Mt) with unusual binding mode have emerged. Here we report in silico design and eva...
Oil temperature plays a crucial role in hydraulic closed-circuit systems (HCS), and conventional thermal equilibrium models and coupled simulation models face challenges in terms of accuracy, efficiency, and cost when calculating oil temperature. Thi...
To effectively prevent the occurrence of poisoning and asphyxiation accidents in underground mines, this paper establishes an evaluation index system for the factors influencing accidents, constructs a combined assignment model to solve the problem o...
Background: Identifying potential targets for bioactive compounds is crucial for elucidating the mechanisms of action and drug development. Methods: This study presents ComTarget, a computational tool that integrates 3D molecular shape similarity ana...
Runoff prediction plays a critical role in water resource management and flood mitigation. Traditional runoff prediction methods often rely on single-layer optimization frameworks that process the data without decomposition and employ relatively simp...
This study aims to design improved inhibitors targeting the thymidylate kinase (TMK) of Mycobacterium tuberculosis (Mtb), the causative agent of infectious disease tuberculosis that is associated with high morbidity and mortality in developing countr...
The clinical integration of artificial intelligence has outpaced the development of robust evaluative frameworks, raising critical safety concerns. This perspective establishes a clear taxonomy distinguishing probabilistic language models from determ...
Non-destructive, fast, and accurate prediction of soil organic matter content in farmland is of great significance for soil fertility assessment and rational fertilization. In the process of soil organic matter prediction, it is important to give ful...
Chemokine receptors play crucial roles in fundamental biological processes. Their malfunction may result in many diseases, including cancer, autoimmune diseases, and HIV. The oligomerization of chemokine receptors holds significant functional implica...
Large deep foundation pits are usually in a complex environment, so their surface deformation tends to show a stable rising trend with a small range of fluctuation, which brings certain difficulty to the prediction work. Therefore, in this study we p...
Addressing the challenges associated with wind turbine blade root loads—including nonlinearity, strong coupling effects, high computational complexity, and the limitations of conventional mathematical-physical modeling approaches. This paper pr...
The efficiency of conventional screening programs to identify early-stage malignancies can be limited by the low number of cancers recommended for screening as well as the high cumulative false-positive rate, and associated iatrogenic burden, resulti...
Metabolite identification for untargeted metabolomics is often hampered by the lack of experimentally collected reference spectra from tandem mass spectrometry (MS/MS). To circumvent this problem, Competitive Fragmentation Modeling-ID (CFM-ID) was de...
To more accurately predict short-term traffic flow, this study posits a sophisticated integrated prediction model, CNN-BiGRU-AAM, based on the additive attention mechanism of a convolutional bidirectional gated recurrent unit neural network. This mod...
Accurate short-term traffic flow prediction is a prerequisite for achieving an intelligent transportation system to proactively alleviate traffic congestion. Considering the complex and variable traffic environment, so that the traffic flow contains...
The accurate prediction of PM10 concentrations at smart construction sites is crucial for improving urban air quality, protecting public health, and advancing sustainable development in the construction industry. PM10 concentrations at construction s...
In this study, we used the hydrophobic-polar (HP) two-dimensional square and three-dimensional cubic lattice models for the problem of protein structure prediction (PSP). This kind of lattice reduces computational time and calculations, the conformat...
Link prediction is a very important field in network science with various emerging algorithms, the goal of which is to estimate the presence or absence of an edge in the network. Depending on the type of network, different link prediction algorithms...
China aims to reduce carbon dioxide emissions and achieve peak carbon and carbon neutrality goals. Natural gas, as a high-quality fossil fuel energy, is an important transition resource for China in the process of carbon reduction, so it is necessary...
Due to synergistic effects, combinatorial drugs are widely used for treating complex diseases. However, combining drugs and making them synergetic remains a challenge. Genetic disease genes are considered a promising source of drug targets with impor...
We present a self-organizing map (SOM) approach to predicting macromolecular targets for combinatorial compound libraries. The aim was to study the usefulness of the SOM in combination with a topological pharmacophore representation (CATS) for select...
Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results in insufficient sensitivity and specificity necessary to rea...
The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFR...
Accurate temperature prediction is essential for understanding climate variability and hydrological extremes. In this context, Long Short-Term Memory (LSTM) networks have become a widely adopted tool for temperature forecasting; however, their perfor...
The combinatorial therapy with multiple drugs may lead to unexpected drug-drug interactions (DDIs) and result in adverse reactions to patients. Predicting DDI events can mitigate the potential risks of combinatorial therapy and enhance drug safety. I...
The prediction of stock price trends is of vital importance for maintaining the stability of the financial market, optimizing resource allocation and preventing systemic risks. To ensure the practical application value of the prediction model, it is...
Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of many solid tumors. In breast cancer (BC), immunotherapy is currently approved in combination with chemotherapy, albeit only in triple-negative breast cancer. Unfortunately, most...
The high interest in the prediction of the intestinal absorption for New Chemical Entities (NCEs) is generated by the increasing rate in the synthesis of compounds by combinatorial chemistry and the extensive cost of the traditional evaluation method...
Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods...
We examine the Rosencrantz coin that can “stick” in states for extended periods. Non-ergodic dynamics is highlighted by logarithmically growing block lengths in sequences. Traditional entropy decomposition into predictable and unpredictab...
The prediction of drug combinations is of great clinical significance. In many diseases, such as high blood pressure, diabetes, and stomach ulcers, the simultaneous use of two or more drugs has shown clear efficacy. It has greatly reduced the progres...
In 1982, Stanley predicted a combinatorial upper bound for the depth of any finitely generated multigraded module over a polynomial ring. The predicted invariant is now called the Stanley depth. Duval et al. found a counterexample for Stanley’s...
The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilit...
This paper introduces a novel inverse design framework that combines pixelated microstrip antenna modeling, convolutional neural network (CNN), and binary particle swarm optimization (BPSO) to automate the process of generating antenna structures fro...
The partial information decomposition (PID) aims to quantify the amount of redundant information that a set of sources provides about a target. Here, we show that this goal can be formulated as a type of information bottleneck (IB) problem, termed th...
Cost-effective therapy of neglected and tropical diseases such as malaria requires everlasting drug discovery efforts due to the rapidly emerging drug resistance of the plasmodium parasite. We have carried out computational design of new inhibitors o...
The Rigid Adsorbent Lattice Fluid model has been shown to comply with all the requirements for thermodynamic consistency in the case of an adsorbent that does not undergo structural changes. This is achieved by introducing a correction to the reduced...
Quality–Diversity (QD) optimization has emerged as a distinctive paradigm in evolutionary computation, shifting the focus from identifying a single global optimum to illuminating a high-dimensional repertoire of elite solutions that jointly max...
Intrinsic motivation provides a principled mechanism for driving exploration in reinforcement learning when external rewards are sparse or delayed. A central challenge, however, lies in defining meaningful novelty signals in high-dimensional and comb...
Metabolite structure identification remains a significant challenge in nontargeted metabolomics research. One commonly used strategy relies on searching biochemical databases using exact mass. However, this approach fails when the database does not c...
The quadratic multidimensional knapsack problem (QMdKP) is a combinatorial optimization problem that involves selecting a subset of items to maximize both linear and quadratic profits without exceeding the capacity constraints across multiple dimensi...
As histone deacetylase inhibitors (HDACIs) have limited efficacy against solid tumors, we investigated whether and how oxidative stress is involved in sensitivity to HDACIs to develop a novel therapeutic option of HDACIs treatment. We first tested wh...
Linear system solving is a main workhorse in applied mathematics. Recently, theoretical computer scientists contributed sophisticated algorithms for solving linear systems with symmetric diagonally-dominant (SDD) matrices in provably nearly-linear ti...
Background: Small-cell lung cancer (SCLC) is an aggressive type of lung cancer, and several factors are currently used to predict poor outcomes, including performance status (PS), extensive-stage disease, male sex, advanced age, and elevated lactate...
Motivated by recent interest in predictive inference under distribution shifts, we study the problem of approximating finite weighted exchangeable sequences by a mixture of finite sequences with independent terms. Two different approaches are develop...
The combination of wet-lab experimental data on multi-site combinatorial mutations and machine learning is an innovative method in protein engineering. In this study, we used an innovative sequence-activity relationship (innov’SAR) methodology...
Combinatorial Route Optimization (CRO) problems, such as the Vehicle Routing Problem (VRP) or the Travelling Salesman Problem (TSP), are commonly seen in scheduling, logistics, and transportation. While current machine learning (ML) methods can overc...
19 December 2023
We present a theoretical approach for the in silico generation of new polymer structures for the systematic search for new materials with advanced properties. It is based on Bicerano’s Regression Model (RM), which uses the structure of the smal...
Electronic Medical Records (EMRs) can be used to create alerts for clinicians to identify patients at risk and to provide useful information for clinical decision-making support. In this study, we proposed a novel approach for predicting Amyotrophic...
of 6