Skip to main content

Uncertainty in Multiclass Classification

Dr. Rishabh Pathak
1. What is Uncertainty in Classification? Uncertainty refers to the model’s confidence or doubt in its predictions. Quantifying uncertainty is important to understand how reliable each prediction is. In multiclass classification , uncertainty estimates provide probabilities over multiple classes, reflecting how sure the model is about each possible class. 2. Methods to Estimate Uncertainty in Multiclass Classification Most multiclass classifiers provide methods such as: predict_proba: Returns a probability distribution across all classes. decision_function: Returns scores or margins for each class (sometimes called raw or uncalibrated confidence scores). The probability distribution from predict_proba captures the uncertainty by assigning a probability to each class. 3. Shape and Interpretation of predict_proba in Multiclass Output shape: (n_samples, n_classes) Each row corresponds to the probabilities of ...
Post a Comment

Functions Of APC/C-CDH1 In Postmitotic Neurons

Image
Dr. Rishabh Pathak

The Anaphase Promoting Complex/Cyclosome-Cdh1 (APC/C-Cdh1) is a multiprotein complex known for its role in cell cycle regulation, specifically in targeting cell cycle proteins for degradation during mitosis. However, recent studies have revealed novel functions of APC/C-Cdh1 in postmitotic neurons. Here are some key points regarding the functions of APC/C-Cdh1 in postmitotic neurons:


1.      Neuronal Survival:

o Cyclin B1 Degradation: In postmitotic neurons, APC/C-Cdh1 promotes the continuous degradation of cyclin B1, a key cell cycle regulator. By targeting cyclin B1 for proteasomal degradation, APC/C-Cdh1 prevents its nuclear accumulation and inhibits cell cycle re-entry, thereby promoting neuronal survival.

o Apoptosis Regulation: Dysregulation of APC/C-Cdh1-mediated cyclin B1 degradation in postmitotic neurons can lead to aberrant cell cycle activation and apoptosis. Maintaining proper APC/C-Cdh1 activity is crucial for preventing neuronal cell death and ensuring long-term neuronal survival.

2.     Axonal Growth and Synaptogenesis:

o    Regulation of Developmental Processes: APC/C-Cdh1 has been implicated in regulating axonal growth and synaptogenesis in postmitotic neurons. By controlling the degradation of specific proteins involved in neuronal development, APC/C-Cdh1 influences the structural and functional maturation of neurons.

o Synaptic Connectivity: Proper functioning of APC/C-Cdh1 is essential for establishing and maintaining synaptic connectivity in the brain. Disruption of APC/C-Cdh1 activity can impact synaptic plasticity and neuronal network formation, potentially leading to cognitive deficits.

3.     Glucidic Metabolism:

o Metabolic Regulation: APC/C-Cdh1 has been linked to the regulation of glucidic (carbohydrate) metabolism in postmitotic neurons. By modulating the stability of metabolic enzymes or regulators, APC/C-Cdh1 may influence energy production and utilization in neurons, thereby impacting neuronal function and viability.

o    Metabolic Homeostasis: Maintaining metabolic homeostasis is crucial for neuronal health and function. APC/C-Cdh1-mediated control of glucidic metabolism pathways in postmitotic neurons highlights the diverse roles of this complex beyond cell cycle regulation.

4.    In Vivo Studies:

o Mouse Models: Studies using specific neuronal knockout mouse models for Cdh1 have demonstrated the importance of APC/C-Cdh1 in neuronal survival in vivo. Depletion of Cdh1 in the brain leads to selective neuronal loss, emphasizing the essential role of APC/C-Cdh1 in maintaining neuronal integrity and function.

o Layer-Specific Effects: Cdh1 depletion in the cerebral cortex results in a time-dependent shortening of specific cortical layers, indicating a progressive loss of neurons. These in vivo findings underscore the significance of APC/C-Cdh1 in preserving neuronal populations and cortical architecture.

In conclusion, APC/C-Cdh1 plays critical roles in postmitotic neurons beyond its canonical function in cell cycle regulation. By influencing neuronal survival, axonal growth, synaptogenesis, and metabolic processes, APC/C-Cdh1 contributes to the maintenance of neuronal integrity and function. Understanding the diverse functions of APC/C-Cdh1 in postmitotic neurons provides insights into the molecular mechanisms underlying neuronal development, connectivity, and metabolic homeostasis, with implications for neurodegenerative disorders and cognitive function.

 

Categories:

Comments

Post a Comment

Popular posts from this blog

Relation of Model Complexity to Dataset Size

Dr. Rishabh Pathak
Core Concept The relationship between model complexity and dataset size is fundamental in supervised learning, affecting how well a model can learn and generalize. Model complexity refers to the capacity or flexibility of the model to fit a wide variety of functions. Dataset size refers to the number and diversity of training samples available for learning. Key Points 1. Larger Datasets Allow for More Complex Models When your dataset contains more varied data points , you can afford to use more complex models without overfitting. More data points mean more information and variety, enabling the model to learn detailed patterns without fitting noise. Quote from the book: "Relation of Model Complexity to Dataset Size. It’s important to note that model complexity is intimately tied to the variation of inputs contained in your training dataset: the larger variety of data points your dataset contains, the more complex a model you can use without overfitting....
Post a Comment
Read more

Linear Models

Dr. Rishabh Pathak
1. What are Linear Models? Linear models are a class of models that make predictions using a linear function of the input features. The prediction is computed as a weighted sum of the input features plus a bias term. They have been extensively studied over more than a century and remain widely used due to their simplicity, interpretability, and effectiveness in many scenarios. 2. Mathematical Formulation For regression , the general form of a linear model's prediction is: y^ ​ = w0 ​ x0 ​ + w1 ​ x1 ​ + … + wp ​ xp ​ + b where; y^ ​ is the predicted output, xi ​ is the i-th input feature, wi ​ is the learned weight coefficient for feature xi ​ , b is the intercept (bias term), p is the number of features. In vector form: y^ ​ = wTx + b where w = ( w0 ​ , w1 ​ , ... , wp ​ ) and x = ( x0 ​ , x1 ​ , ... , xp ​ ) . 3. Interpretation and Intuition The prediction is a linear combination of features — each feature contributes prop...
Post a Comment
Read more

Predicting Probabilities

Dr. Rishabh Pathak
1. What is Predicting Probabilities? The predict_proba method estimates the probability that a given input belongs to each class. It returns values in the range [0, 1] , representing the model's confidence as probabilities. The sum of predicted probabilities across all classes for a sample is always 1 (i.e., they form a valid probability distribution). 2. Output Shape of predict_proba For binary classification , the shape of the output is (n_samples, 2) : Column 0: Probability of the sample belonging to the negative class. Column 1: Probability of the sample belonging to the positive class. For multiclass classification , the shape is (n_samples, n_classes) , with each column corresponding to the probability of the sample belonging to that class. 3. Interpretation of predict_proba Output The probability reflects how confidently the model believes a data point belongs to each class. For example, in ...
Post a Comment
Read more

Uncertainty in Multiclass Classification

Dr. Rishabh Pathak
1. What is Uncertainty in Classification? Uncertainty refers to the model’s confidence or doubt in its predictions. Quantifying uncertainty is important to understand how reliable each prediction is. In multiclass classification , uncertainty estimates provide probabilities over multiple classes, reflecting how sure the model is about each possible class. 2. Methods to Estimate Uncertainty in Multiclass Classification Most multiclass classifiers provide methods such as: predict_proba: Returns a probability distribution across all classes. decision_function: Returns scores or margins for each class (sometimes called raw or uncalibrated confidence scores). The probability distribution from predict_proba captures the uncertainty by assigning a probability to each class. 3. Shape and Interpretation of predict_proba in Multiclass Output shape: (n_samples, n_classes) Each row corresponds to the probabilities of ...
Post a Comment
Read more

Uncertainty Estimates from Classifiers

Dr. Rishabh Pathak
1. Overview of Uncertainty Estimates Many classifiers do more than just output a predicted class label; they also provide a measure of confidence or uncertainty in their predictions. These uncertainty estimates help understand how sure the model is about its decision , which is crucial in real-world applications where different types of errors have different consequences (e.g., medical diagnosis). 2. Why Uncertainty Matters Predictions are often thresholded to produce class labels, but this process discards the underlying probability or decision value. Knowing how confident a classifier is can: Improve decision-making by allowing deferral in uncertain cases. Aid in calibrating models. Help in evaluating the risk associated with predictions. Example: In medical testing, a false negative (missing a disease) can be worse than a false positive (extra test). 3. Methods to Obtain Uncertainty from Classifiers 3.1 ...
Post a Comment
Read more