Optimizations Based Feature Selection Method for Disease Survival Prediction

In the realm of survival prediction, identifying relevant features plays a pivotal role in enhancing model accuracy and interpretability. This research proposes a novel feature selection method that leverages the synergies between the Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA) to optimize the selection process. The WOA, inspired by the social behavior of humpback whales, is employed to explore the solution space efficiently, while the GA, inspired by the process of natural selection, is used for refining and evolving potential feature subsets. The proposed hybrid algorithm, termed WOA-GA, introduces a dynamic framework that adaptively adjusts the exploration-exploitation trade-off during the search process. The WOA's exploration capabilities are harnessed in the early stages to efficiently traverse the solution space, while the GA's exploitation capabilities are employed later to fine-tune and evolve promising feature subsets. The synergistic combination of these two optimization techniques aims to mitigate the limitations of each individual algorithm and capitalize on their complementary strengths

Applications of Deep Learning and Machine Learning in Healthcare Domain – A Literature Review

In recent years, Artificial Intelligence (AI) has advanced rapidly in terms of software algorithms, hardware implementation, and implementations in a wide range of fields. The latest advances in AI applications in biomedicine, such as disease diagnostics, living assistance, biomedical information processing, and biomedical science, are summarised in this study. Brain-Computer Interfaces (BCIs), Arterial Spin Labeling (ASL) imaging, ASL-MRI, biomarkers, Natural Language Processing (NLP), and various algorithms all help to reduce errors and monitor disease progression. Computer-assisted diagnosis, decision support systems, expert systems, and software implementation can help doctors reduce intra- and inter-observer variability. In this paper, numerous researchers conduct a systematic literature review on the application and implementation of Machine Learning, Deep Learning, and Artificial Intelligence in the healthcare industry

Deregulation of apoptotic proteins by induction of Dendropthae falcata (L.f.) Ettingsh plant extract in breast cancer cells: A proteome-wide analysis

Objective(s): The present study evaluated the protein-based analysis to unravel the role and mechanism behind the Dendropthae falcata plant extract treatment in breast cancer cells. Materials and Methods: The protein sample was extracted from the cancer cells after treatment with the plant extract and subjected to two-dimensional electrophoresis for protein separation. Further, the proteins that were differentially regulated among the samples which were treated and non-treated were selected and processed further for protein identification using a tandem mass spectrometry approach.Results: Using these strategies, we identified 16 potential candidates which were showing remarkable changes in treated samples. All the candidates were analyzed further for gene ontology analysis, and it was observed that all proteins were involved in multiple pathways pertaining to the carcinogenesis process. Specifically, apoptotic pathway proteins including BAD, BIK, BID, CASP8, MCL1, BCL2, and BAK1 were highly impacted by treatment with D. falcata plant extract. All these protein hits were further taken for validation experiments using RT PCR analysis. Conclusion: Initiation of these apoptotic proteins by D. falcata plant extract treatment in breast cancer cells shows a positive direction toward nature-based alternative medicine

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum