An important use of proteomics data from Mass Spectrometry (MS) is the classification of tumor types with respect to peptides in specific cancer types. It is highly critical to find an optimal set of markers among specific cancer peptides whose expression can be clinically utilized to build assays for the diagnosis or to track the progression of specific cancer types. A number of feature selection algorithms have been proposed to obtain the classification of MS data. In this article, we proposed an improved feature selection algorithm based on feature weighting. Relief algorithm can calculate the weight of different features according to the correlation between their characteristics and categories. F-score is a simple filter-based feature selection method by evaluating how two sets of real numbers discriminate from each other. The main goal of this paper is to introduce a new feature weighting selection algorithm combining score from f-value and weight from relief, which is more accurate when classifying high-resolution MALDI-TOF (matrix-assisted laser desorption and ionization time-of-flight) MS data. We have developed a four-step strategy for data processing based on: (1) Align the study sets by binning of raw MS data, (2) local maximum search(LMS) peak detection, (3) a new combination feature weighting selection algorithm and (4) support vector machines achieve a satisfactory performance of identifying cancer and the healthy. The best parameter set for LMS were achieved with control variable method, which achieve an average accuracy of 97.4167% (sd = 0.0146) and the best accuracy of 98.6111% in 1000 independent 10 -fold cross validations.  折りたたむ

Excessive variability in behavioral performance and neuronal activation is a common finding in studies of schizophrenia. Recent evidence suggests that this may be due to an imbalance in the ratio of excitation to inhibition in brain function, or E/I imbalance. We used computational modeling of visual system activity to determine whether different potential causes of E/I imbalance would generate effects resembling those reported in schizophrenia. Three major findings emerged. First, reductions in retinal and lateral geniculate nucleus signaling initially led to increases in firing rate variability within the context of reduced V1 activation; however, with prolonged adaptation to weakened sensory signaling, compensatory hyper-activation in V1 neurons occurred, but variability was no longer increased. Second, direct increases in V1 excitation, or decreases in inhibition, led to the highest levels of initial activation but not variability; however, with prolonged inhibitory adaptation to increased excitation, overall activity was no longer elevated, but an increase in firing rate variability was observed. Third, the greatest fluctuation in firing rate variability, in response to the same stimulus across increasing contrast levels, was observed with reductions in sensory signaling, but only immediately after model perturbations; with prolonged adaption, the largest fluctuations were associated with increased excitation or reduced inhibition within V1. Implication of these findings are that schizophrenia-related increases in neuronal response variability may arise from at least two sources: 1) weakened sensory signaling and its associated low signal-to-noise ratio; and 2) compensatory but incomplete inhibitory responses to continuous increases in cortical excitation. 折りたたむ

Both the human body and the natural world are governed by multiple complex systems. These systems have feedback loops which is a process in which the outputs of a system are circled back and used as inputs. Where there are multiple systems, there is always the potential for a catastrophic system failure. If a system fails in the human body, this can lead to a number of life-threatening and debilitating diseases such as cancer. Diseases such as cancer is in effect, the result of a catastrophic system failure. There are cancer cases in which the root cause of the disease is unknown. System failure in the human reproductive system can lead to congenital birth defects. In cases of a system failure leading to congenital birth defects, some of the causal factors are known but in 65% of these cases, the reasons for this reproductive system failure are unexplained. There are neurological diseases such as Parkinson’s, multiple sclerosis and Alzheimer’s where again, the root causes are unknown. Then there are a number of infectious diseases where the root cause is unknown. The initial causative factors for most of these human diseases are well known. What has yet to be fully understood is the primary root cause that triggers and underpins these system failures in the first place. Nature also has devastating system failures such as in earthquakes and hurricanes. Humans and nature are a close partnership and nature can influence human health. Nature’s systems are deeply interconnected and often exhibit complex behaviours due to positive and negative feedback loops present in both nature and human body systems. Using systems methodology and systems thinking and philosophical insights, the objective is to try to ascertain the answer as to why there are these unknown root causes of diseases; questions that presently, science alone cannot explain. It will be argued that as man and nature are as one, the answers as to why human body systems fail leading to disease may lie not in science but in nature. 折りたたむ

The complexity of living systems, ranging from cellular networks to organisms to ecosystems, poses a significant challenge to the progress of contemporary biology and biomedicine. Living organisms are complex adaptive systems that are characterized by nonlinear interactions, causal-dynamic feedback loops, and context-dependent behaviors that give rise to emergent properties that are not completely explainable or predictable from the properties of the individual components alone. Investigating and understanding systemic complexity is therefore essential for advancing biology and biomedicine. Recently, artificial intelligence (AI) has been developed for analyzing large-scale, high-dimensional datasets generated by experimental and clinical research. AI enables the identification of correlations, causal patterns, and predictive relationships, which are difficult to discern using traditional analytical approaches. However, without an overarching framework, it risks being applied in fragmented or purely data-driven ways. Systems thinking (ST) provides a framework by emphasizing holism, interconnections, and dynamic behaviors across multiple organizational scales. By integrating ST and AI, researchers can creatively and effectively investigate living systems, ensuring that computational insights are meaningful and contextually grounded. An integrated ST–AI approach is proposed as a guiding framework for twenty-first century biology and biomedicine. 折りたたむ