In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[Q]: One of the most prevalent symptoms among the elderly population, dementia, can be detected by classifiers trained on linguistic features extracted from narrative transcripts. However, these linguistic features are impacted in a similar but different fashion by the normal aging process. Aging is therefore a confounding factor, whose effects have been hard for machine learning classifiers to isolate.

In this paper, we show that deep neural network (DNN) classifiers can infer ages from linguistic features, which is an entanglement that could lead to unfairness across age groups. We show this problem is caused by undesired activations of v-structures in causality diagrams, and it could be addressed with fair representation learning. We build neural network classifiers that learn low-dimensional representations reflecting the impacts of dementia yet discarding the effects of age. To evaluate these classifiers, we specify a model-agnostic score $\Delta_{eo}^{(N)}$ measuring how classifier results are disentangled from age. Our best models outperform baseline neural network classifiers in disentanglement, while compromising accuracy by as little as 2.56\% and 2.25\% on DementiaBank and the Famous People dataset respectively.
[A]: Show that age confounds cognitive impairment detection + solve with fair representation learning + propose metrics and models.

[Q]: Protein classification is responsible for the biological sequence, we came up with an idea whichdeals with the classification of proteomics using deep learning algorithm. This algorithm focusesmainly to classify sequences of protein-vector which is used for the representation of proteomics.Selection of the type protein representation is challenging based on which output in terms ofaccuracy is depended on, The protein representation used here is n-gram i.e. 3-gram and Kerasembedding used for biological sequences like protein. In this paper we are working on the Proteinclassification to show the strength and representation of biological sequence of the proteins
[A]: Protein Family Classification using Deep Learning

[Q]:
The recent expansion of machine learning applications to molecular biology proved to have a significant contribution to our understanding of biological systems, and genome functioning in particular. Technological advances enabled the collection of large epigenetic datasets, including information about various DNA binding factors (ChIP-Seq) and DNA spatial structure (Hi-C). Several studies have confirmed the correlation between DNA binding factors and Topologically Associating Domains (TADs) in DNA structure. However, the information about physical proximity represented by genomic coordinate was not yet used for the improvement of the prediction models.

In this research, we focus on Machine Learning methods for prediction of folding patterns of DNA in a classical model organism Drosophila melanogaster. The paper considers linear models with four types of regularization, Gradient Boosting and Recurrent Neural Networks for the prediction of chromatin folding patterns from epigenetic marks. The bidirectional LSTM RNN model outperformed all the models and gained the best prediction scores. This demonstrates the utilization of complex models and the importance of memory of sequential DNA states for the chromatin folding. We identify informative epigenetic features that lead to the further conclusion of their biological significance.
[A]:
We apply RNN to solve the biological problem of chromatin folding patterns prediction from epigenetic marks and demonstrate for the first time that utilization of memory of sequential states on DNA molecule is significant for the best performance.